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Respiratory monitoring system using Bluetooth Low Energy
Sensors and Actuators A 286 (2019) 152–162
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Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Respiratory monitoring system using Bluetooth Low Energy P. Janik ∗ , M. Pielka, M.A. Janik, Z. Wróbel University of Silesia in Katowice, Faculty of Computer Science and Materials Science, Institute of Computer Science, Department of Biomedical Computer nska 39, 41-200 Sosnowiec, Poland Systems, ul. B˛edzi´
a r t i c l e
i n f o
Article history: Received 10 May 2018 Received in revised form 22 December 2018 Accepted 25 December 2018 Available online 26 December 2018 Keywords: Beacon Breath monitoring IoT Micropower Mobile system Smart sensor
a b s t r a c t The paper presents a system for monitoring respiratory frequency and strength using a BLE transmitter in non-connectable advertising mode and a variable impedance sensor operating in a relaxation oscillator circuit. The presented solution is characterized by low energy consumption. Both the measurement system and teletransmission system operate at 2.2 V power supply. By using a micropower operational amplifier, the analogue part of the system consumes 240–300 W. Unipolar power supply and configuration of the relaxation oscillator enable to create a pseudobinary signal with the frequency f < 10 kHz. The relaxation oscillator is connected to the microcontroller binary input without having to convert the signal. By using a System On Chip (SoC) with CPU ARM Cortex to control the radio and perform measurements of the pseudobinary signal, the design was simplified and its dimensions were reduced. The operation of the analogue part of the system was compared with a commercial sensor. In addition, the ability of the system to reproduce the breath cycle signal on the BLE side was verified. Differences between the duration of individual cycles turned out to be statistically insignificant, both in the case of normal (p = 0.6889) and fast (p = 0.3226) breathing. © 2018 Elsevier B.V. All rights reserved.
1. Introduction Respiratory monitoring is widely used in medicine, emergency medical services and sport, and nowadays also at home. Breath control is particularly important in the case of respiratory diseases, not only during self-breathing but also during mechanical ventilation. Respiratory monitoring can be carried out in multiple ways, by using, inter alia, humidity [1–4], gas flow [5,6], vibration (acceleration) [7,8], temperature [6,9], capacitive [10], piezo-resistive [11], pressure [12], or pyroelectric sensors [7,13]. There are also systems that use the magneto-elastic effect [14] in breath monitoring, or systems based on acoustic signal analysis [15]. Monitoring vital signs, including respiration, is usually a longterm process. Therefore, it is convenient to perform remote measurements by using contactless systems that monitor, for example, breath, pulse or body movement from a relatively small distance [16,17], or biomonitoring systems that use teletransmission technologies [18,19]. This approach provides the monitored person with relative comfort, e.g. during sleep. One of the problems with designing remote monitoring systems is energy consumption, in particular that of the radio system.
∗ Corresponding author. E-mail address: [email protected] (P. Janik). https://doi.org/10.1016/j.sna.2018.12.040 0924-4247/© 2018 Elsevier B.V. All rights reserved.
Currently, Bluetooth Low Energy (BLE) is commonly used for communication between monitoring systems and mobile devices, e.g. smartphones [19–22]. It is also used in energy-saving measurement systems [23,24]. However, standard solutions offered on the market use the connection mode (e.g. GATT) between BLE interfaces, which requires establishing a connection between communicating devices. The connection transmission in BLE is used to transmit more data, e.g. from the device memory, after which the connection may break. Despite the low power consumption of radio interfaces in the connection mode (about a few/a dozen mA), the use of BLE for continuous monitoring is limited, especially in battery-powered devices. By default, Bluetooth operates in the star topology. However, the mesh network topology is now also propagated [25]. BLE nonconnectable advertising can be an alternative to these topologies. It can be used for continuous monitoring of various processes by means of a pulsed radio interface. The BLE transmitter configured in non-connectable advertising mode (broadcaster in GAP profile) is commonly known as a beacon. Its basic function is to distribute its unique identifier, owing to which such systems are used in radio identification systems [26] or indoor navigation [27–29]. It is possible to realize many-to-many transmissions by using advertising mode. The paper presents a system which, by using energy-efficient base components and ICT (Information and Communication Tech-
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nologies), allows for a significant reduction in power consumption during operation. The presented design was developed in the form of an integrated radio sensor, using a programmable BLE module. The solution, due to its small size, is wearable and the applied radio interface enables to extend its functionality to the Internet of Things (IoT). The small smart sensor can be mounted, for example, on inhalation masks with an average oxygen concentration. Moreover, the device does not restrict the movements of the monitored person owing to the radio data transmission. The detector element can be placed at the outlet of one of the mask ventilation openings The system is characterized by the following features: i) simple system design, ii) low energy consumption by using an energyefficient radio interface, iii) the possibility of cooperation with mobile devices, iv) small dimensions, v) low production costs, vi) short time of reaction of the system to respiratory functions and vii) ability to work in many configurations of the teletransmission network. To maintain the compatibility of the presented solution with mobile devices, the method of advertising described in the Bluetooth Core specification has been used. The presented smart sensor can also create dedicated sensor networks, which allows for more efficient management of transmission channels. 2. Materials and methods 2.1. Measurement equipment The first element of the measuring chain is a parametric microcondensation breath sensor (MCBS) with a hydrophilic layer a commercially available octenidine-based antiseptic. The properties of this sensor type and the method of its preparation are described in [2], whereas its minimized version is described in more detail in [3]. The presented system uses a minimized version of MCBS, which is presented in Fig. 1a. Respiratory functions are processed into an electrical signal in the multivibrator system, shown conceptually in Fig. 1b. In turn, Fig. 1c shows the diagram of the Analogue Module (AM), i.e. a relaxation oscillator with MCBS. The sensor is incorporated into the negative feedback loop of the operational amplifier (Fig. 1c) with one interdigitated electrode connected to the inverting input and the other to the output, and together with the capacitor attached between the inverting input and the ground form the RS C branch. The oscillation frequency f of the output signal U0 depends on the time constant determined by the variable (depending on the breath phase) resistance of the sensor RS and the capacity of the capacitor C, and when R1 =R2 =R3 , it is [30]: f = 1/(R S Cln4) The multivibrator was realized using the OPA244 amplifier, which is characterized by low power consumption, and is classified as a micropower system, and low value of the minimum supply voltage. The discussed system uses R1 , R2 , R3 resistors with the value of 1 M each and the capacitor C = 4.7 nF. Moreover, the system uses the change of impedance parameters of MCBS. During breathing, the multivibrator generates a pseudobinary signal with a frequency dependent on the breath phase. Powering the multivibrator from an asymmetric voltage source causes the output signal to oscillate between the ground potential and the potential of the power source. Therefore, it can be treated as pseudobinary. This construction allows the analogue relaxation oscillator circuit (AM) to work directly with digital circuits. In order to demonstrate the functionality of MCBS in the multivibrator circuit and to compare it with the reference sensor, the measurement set shown in Fig. 1c was used. The pseudobinary output signal of the multivibrator is processed in the frequency-
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voltage converter block f/V, and then fed to one of the channels of the digital oscilloscope (OSC) from the converter output. The output of the reference sensor circuit is connected to another channel of the oscilloscope. This configuration of the system allows for concurrent registration of the output signals of two different sensors monitoring the same breathing functions. The HIH5030 [31] humidity sensor was used as a reference sensor. The reference sensor was configured in accordance with the manufacturer specifications. In order to compare the MCBS operating in the AM system with the HIH sensor, a 30 cm tube with an internal diameter of 14 mm was used, at the outlet of which both sensors were placed in such a way that they did not constitute resistance to the stream of exhaled and inhaled air. Breath of different speed was simulated by a healthy volunteer, who breathed through his mouth through the tube equipped with humidity sensors (Fig. 1d). In the Smart Breath Sensor (SBS) structure (Fig. 1e), AM was connected to the programmable BLE module. The output of the multivibrator is connected to one of the GPIO (General Purpose Input Output) ports of the microcontroller C. The microcontroller fulfils a measuring function and at the same time controls the operation of the radio frequency (RF) circuit. The discussed system uses a commercial, miniature BLE module [32] that includes a microcontroller with ARM architecture (Cortex M0) integrated with RF and a PCB antenna. The applied BLE module works in Bluetooth 4.2. The dotted lines (Fig. 1e) indicate symbolically the integrity of the commercial BLE module and the SBS electronics. Both the multivibrator and the BLE module were powered with a voltage of 2.2 V. SBS is an autonomous module that monitors breathing functions and transmits data from the monitoring process in advertising mode. Verification of SBS functionality requires confronting data regarding monitored respiratory functions on the receiver side. To this end, a two-chain measurement system presented in Fig. 1e and Fig. 1f was designed. The first measurement chain is related to the electrical signal path from the multivibrator to the recorder (OSC) (Fig. 1e). In turn, the second measurement chain concerns: signal measurement performed by the microcontroller of the SBS transmitter, data formatting, radio transmission (RF circuit), receiving and decoding data in the receiver module (BLE Module Receiver) and data recovery in a PC. A block diagram of the Receiver Station for receiving advertising data is presented in Fig. 1f. The BLE Module Receiver was powered from the FTDI converter, adjusting logic levels between the PC and the receiver microcontroller, communicated via the serial port. Since the measurements were performed in two parallel measuring chains, it was necessary to introduce a synchronization signal SYNC. The synchronization signal in the form of a single, cyclic impulse was generated by the microcontroller and fed to one of the channels of the OSC. At the same time, the advertising package was sent. This procedure enabled to synchronize signals recorded in two remote measuring chains. The respiratory function curves were registered using the Rigol MSO1104Z oscilloscope. Long-term battery discharge measurements were recorded using the I/O Device - National Instruments NI USB-6361 and LabView software. 2.2. Communication interface configuration The hardware layer of the presented solution uses programmable systems and the BLE interface (also called Bluetooth Smart). The integrated module of the microcontroller and BLE transmitter uses the GAP (Generic Access Profile). The module functions as a peripheral device in advertising mode broadcasting packets [33]. These packets have the format defined in the Bluetooth Core 4.2 specification [34] presented schematically in Fig. 2a. The packet contains several blocks: preamble, access address,
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Fig. 1. a) View of the printed circuit of MCBS, b) schematic diagram of the multivibrator, where RS represents resistance of MCBS, c) block diagram of the system for concurrent measurements of MCBS in the multivibrator system (AM) and the reference sensor, d) visualization of breath measurement with the tube, e) functional structure of the Smart Breath Sensor (SBS), f) diagram of the station for receiving and detecting advertising packets - Receiver Station.
Packet Data Unit (PDU) and Cyclic Redundancy Check (CRC) [35]. An important segment is PDU because it specifies when data or advertising transmissions take place. The PDU type in the discussed system was set at ADV NONCONN IND (Non-connectable undirected advertising event). This type does not allow for sending a scan request. The Advertising Data of PDU [34] section contains thirty-one bytes (D1 -D31 ), which can be modified. The BLE standard makes three channels available for broadcast. SBS uses a mechanism in which each of the BLE packets is transmitted via all three channels. The presented smart sensor maintains this way of transmission, which makes it compatible with devices compatible with Bluetooth Core 4.2, e.g. smartphones. Thus, every packet is transmitted three times. The interval for non-connectable transmission can be configured as a value between 100 ms and 10.24 s. The packet must have only a header and address (Fig. 2a), whereas Advertising Data do not have to be defined or may be equal to zero. When these data are non-zero, the first two bytes D1 and D2 (Fig. 2a) specify the length and type of Advertising Data. Thus, the configured BLE transmitter is called a non-connectable beacon. Fig. 2b presents the characteristics of BLE transmission in advertising mode in the presented system. Packets are broadcast in transmission cycles consisting of several impulses, usually lasting
about 0.5 ms each. During the transmission process, the current is drawn by the transmitter in a discrete manner, which is why the advertising packet transmission was presented as a cycle of impulses measured on the radio module power supply. The configuration of the BLE radio module in the advertising mode allows you to create sensor network structures with many transmitters and many receivers. This structure allows to monitor the breathing activity of many people on many receivers at the same time. However these aspects are not discussed in this article. The simplest structure is a single smart sensor in the advertising mode and a dedicated receiver with a BLE interface or a mobile device, as shown in Fig. 3a. Of course, data broadcast by a single advertiser (SBS) can be received by multiple receivers simultaneously. Fig. 3b presents an exemplary structure where several SBS broadcast data are received by several dedicated and mobile receivers (smartphones, tablets). In the case of connection transmission, there is an increased current consumption during the connection process between the two devices. If the connection is broken, the process is repeated. In addition, when multiple devices are involved in the connection transmission, the connection process may be delayed. In the case of transmission in the advertising mode, the connection is not established. Although the tested modules NRF51822 [32] showed a similar current consumption in the case of connection
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Fig. 2. a) BLE packet structure, b) Power consumption for transmission in nonconnectable advertising mode.
transmission and in the advertising mode implemented on three channels (about 0.5 mA), a significant reduction in power consumption was possible when the advertiser was configured to transmit via one channel. The advertiser then consumed about 0.25 mA. However, it should be noted that transmission via one channel does not ensure compatibility with, for example, smartphones, and requires a dedicated device scanning on the given advertising channel. 3. Results and disscussion 3.1. Analogue module (AM) In the process of generating a pseudobinary signal U0 by the multivibrator (Fig. 4a), a change in the resistance RS of MCBS during respiratory functions plays a predominant role. During the exhalation phase, a thin, conductive film is formed on the sensor surface (MCBS) due to water vapour condensation, which reduces the sen-
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sor resistance. In turn, during the inhalation phase, the opposite occurs. Fig. 4b shows the change in the resistance RS of the sensor during a few breath cycles. Arrows indicate the direction of resistance changes during breathing. The change in the sensor resistance RS was estimated based on the frequency f of the output signal from the multivibrator. For this purpose, the formula presented in Section 2.1 was used, taking into account that the system used a 4.7 nF capacitor. A reduction in the sensor resistance during exhalation results in increased frequency generated by the relaxation oscillator, whereas during inhalation, the frequency decreases. An example of the characteristics of the oscillator output signal and changes in the frequency f of the generated signal U0 during a single breath cycle are shown in Fig. 4a. The analogue module of SBS was compared with the reference sensor (HIH5030, Honeywell) according to the block diagram presented in Fig. 1c. The reference sensor was selected for the processing of the same physical quantity, which is realized by the analogue module of SBS, i.e. humidity. According to the manufacturer’s documentation, HIH5030 is also intended for use in medical devices, including sleep apnoea monitoring devices. In addition, the reference sensor is characterized by a relatively short response time in the group of humidity sensors - typically 5 s according to the product data sheet [31]. Fig. 5a and b present a summary of signals of respiratory function monitoring by means of MCBS in the multivibrator system (AM) and the reference sensor. The reaction of both sensors to a change in breath strength was examined, recording the cycles of normal breathing and very weak breathing (Fig. 5a). For normal breathing, local maxima and minima are visible on both recorded curves, whereas in the case of weak breathing, a significant decrease in amplitude for individual cycles can be observed. In the case of AM, each of the cycles of weak breathing is clearly separated on the recorded curve, but the detection of cycles for the reference sensor is difficult. In weak breathing, water vapour condensation on the surface of the sensors decreases and the reference sensor generates a falling edge. Especially in this area (marked with a rectangle in Fig. 5a, it is difficult to distinguish individual breath cycles of the reference sensor, whereas for AM this problem does not occur. Additionally, the AM system differentiates the amplitude of the breath cycle curve more precisely than the reference sensor, which is particularly evident for the last two cycles in Fig. 5a, the third and fourth cycle in Fig. 5b as well as the last two cycles in Fig. 5c. In turn, Fig. 5b shows the response of the reference sensor and AM to breathing rate changes. In the first phase of testing normal breathing (about 12BPM), both recorded curves are characterized by clearly separated cycles. In the case of accelerated breathing (approx. 30BPM), the reference sensor curve has a strongly reduced
Fig. 3. Examples of system configurations using SBS: a) one to one, b) many to many.
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Fig. 4. a) Pseudobinary signal generated by the multivibrator during a breath cycle, b) change in the resistance RS of MCBS during seven breath cycles.
Fig. 5. Comparison of the effectiveness of respiratory monitoring by MCBS and the reference sensor in terms of a) changes in breathing strength, b) changes in breathing rate, c) sensor response time, and d) scaled breathing curves registered in a long measurement, the upper graph shows a full one-hour measurement and the lower one is magnification of the area marked with a rectangle.
amplitude of the variable component representing the breathing cycles, whereas the value of its constant component is increased. It results from increased condensation of water vapour coming from the exhaled air on the sensor surface during fast breathing. Due to the relatively large inertia of the reference sensor, the layer of water vapour condensed on its surface is not sufficiently reduced during
fast breathing cycles. The AM is also characterized by a decrease in the amplitude of the recorded signal for accelerated breathing, but the problem of distinguishing individual cycles does not occur. The analysis of the curves presented in Fig. 5a and b also shows that the reference sensor has greater inertia. It is more precisely represented in Fig. 5c, where the beginning and end of the first
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breath cycle registered with the micro-condensation sensor are symbolically marked with dotted lines. Signals were recorded for normal breathing (approximately 12BPM). The system presented in the paper (AM) detects more quickly both the exhalation phase and the inhalation phase. The beginning of exhalation was usually recorded more than 300 ms earlier than on the reference sensor (HIH5030). In turn, the peak of the breath cycle, which means the end of the exhalation phase and the beginning of the inhalation phase, was recorded about 1 s earlier compared to the reference sensor. Inertia of the sensor is important especially when monitoring very weak or accelerated breathing - when the amplitude of the recorded curve decreases (Fig. 5a and b). Then, the delayed reaction of the sensor makes it difficult to detect the minima and maxima of individual breath cycles, which can be noticed in the case of the reference sensor. The curves for AM shown in Fig. 5a, b and c were mapped using a scale expressed in volts in order to plot them against the reference sensor curves with a voltage output. The measurements were performed on the system shown in Fig. 1c. The f/V converter was set at 670 Hz/V. In order to demonstrate stable operation of AM, the measurement was carried out over a long period of time, lasting over an hour (Fig. 5d). 3.2. Smart breath sensor In order to simplify the structure of SBS, miniature module based on the NRF51822 chipset was used. According to the manufacturer (Nordic Semiconductor), the chipset contains a 32-bit ARM Cortex M0 microcontroller, which provides its resources to the user. Therefore, the SBS uses only two integrated circuits in respiratory monitoring: OPA244 and NRF51822. The block diagram of the implemented SBS module is presented in Fig. 6a. The DC-DC converter circuit providing 2.2 V power for the operational amplifier and the BLE module are placed on the shared PCB motherboard. The electronics are powered by a 3.7 V lithium-polymer battery. The PCB circuit sized 2.5 × 2.5 cm (Fig. 6b) with the battery (250 mAh) is placed in one housing, whereas the sensor is connected using a two-wire cable. One of the basic assumptions of the system was the use of reduced architecture (a single microcontroller supports the radio and performs measurements), which allows to reduce the size of the PCB circuit. The output signal of the multivibrator U0 (Fig. 4a) provides an amplitude of at least 2 V, so there is no need to convert it for the digital inputs of the applied microcontroller. Connecting the multivibrator output to the C binary input does not require the use of additional filtering systems, which further simplifies the presented structure. According to the specification [32], C binary inputs detect the high state from 0.7 VDD to VDD, whereas the low state from VSS (potential GND in the tested system) to 0.3VDD, which ensures stable operation of the system even in the case of fluctuations of the high and low state values of the pseudobinary signal. OPA244 requires only 2.2 V. With such power supply parameters, the relaxation oscillator circuit during breath monitoring consumes current of about 110 A to approx. 140 A, drawn from the DC-DC converter. Therefore, the power consumption by the multivibrator ranges from approx. 240W to approx. 300W. Connectionless transmission limits current consumption of the SBS module between transmission cycles to the peak value of about 2.5 mA (Fig. 2b). In turn, the peak value of current consumption during the transmission cycle (several pulses with an interval of 0.5 ms) is about 15 mA. Such parameters allow for long-lasting, continuous operation of the system with battery power supply. Energy consumption by the Smart Breath Sensor was also measured, which enabled to estimate the battery lifetime. Fig. 7 shows a discharge curve for a 250 mAh battery used to power SBS. The SBS module transmitted PDUs at intervals of 200 ms, whereas in the multivibrator, a 10k resistor was connected instead of the sensor with the
Fig. 6. a) Block diagram of SBS, b) complete Smart Breath Sensor (SBS) with the 250 mAh Li-Po battery, c) PCB motherboard of the test versions of SBS modules sized 2.5 × 3 cm.
Fig. 7. The discharge curve of the battery supplying the SBS module.
RS resistance, owing to which the multivibrator generated a pseudobinary signal with a frequency of about 8.9 kHz. The completed tests showed that SBS can work continuously for about 6 days. The power was cut off by the Protection Circuit Module (PCM) battery at 2.8 V after almost 144 h. The mean current value consumed by SBS is around 1.8 mA. Mapping of monitored respiratory functions is directly related to the frequency of the output signal from the multivibrator calculated based on the number of impulses per unit of time. The results
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Fig. 8. a) Block diagram of the extended station for testing SBS repeatability, b) view of both sides of the Multitransmitter Station used for testing the PCB circuit.
of these measurements are aggregated as data, sent in 20 bytes of Advertising Data (Fig. 2a). A single byte of PDU data (D5 –D24 in Fig. 2a) was used in the discussed system as a memory cell storing and transporting the measurement result of breath monitoring signal parameters. A proportionality coefficient was introduced to reproduce the actual measurement result of the multivibrator frequency. The proportionality coefficient divides without remainder the frequency of the multivibrator signal calculated in the microcontroller. As a result, the value of the calculated frequency can be saved on one byte, and then the results obtained are subsequently loaded into the PDU data (D5 -D24 ). The data quantity and type are stored on bytes D1 and D2 of Advertising Data, respectively. The device ID is stored on byte D3 , which makes it possible to distinguish SBS modules. The proportionality coefficient of the number of measured impulses is recorded on the fourth byte (D4 ).
3.3. Validation The measuring station shown in Fig. 1e) was extended to include additional BLE modules according to the block diagram presented in Fig. 8a. The same breath cycles were recorded in parallel. This measurement structure enables to verify the correctness of mapping of the same input signal by independent modules BLE1–BLE6. An example of a breathing function curve recorded on the receiver station side is shown in Fig. 9a. The structure of a multi-transmitter sensor network sending data to one receiver was tested. Each transmitting BLE module performs both measurements and signal transmission, whereas the receiver must receive PDUs from individual BLE1-BLE6, read the data contained in them and calculate frequency values, based on which the signal representing the breathing activity sent by each of the BLE modules will be reproduced. The aim of the study was to verify the correctness of both the transmission and reproduction of the respiratory signal on the receiver side as well as the measurements performed by independent digital circuits BLE with independent clocks. The obtained
results will allow to estimate what amount of PDU in the entire sensor network is lost at the set transmission parameters. A single AM was connected to six identical BLE transmitter modules (Fig. 8a and b), so that all BLE modules processed the same respiratory signal. In addition, frequency measurements of the multivibrator output signal were performed synchronously by all microcontrollers of BLE1–BLE6 modules. Thus, each of the BLE modules performed frequency measurements of the same signal at the same time (measured the same point indicated in Fig. 9a), so it was possible to compare the ability of individual modules to correctly reproduce the reference signal. The microcontroller performs period measurements (measurement of time between consecutive falling edges of a square wave) by means of a 32 bit Timer working with a base frequency of 16 MHz, scaled to 1 MHz. The Timer allows to measure time with an accuracy of 1 s. This Timer measurement resolution is sufficient to measure the period of the signal representing respiratory function, i.e. above 100 s (the frequency generated by the multivibrator during respiration is less than 10 kHz). The microcontroller performs measurements of signal periods for a set time (10 ms), and then the mean of period measurements is calculated. The obtained result is converted into frequency and then divided by the proportionality coefficient that is the same value that is saved in a frame on byte D4 . A proportionality coefficient of 40 was adopted in the measurements, which allows to record the frequency of 10200 Hz (255 · 40) on one byte. The result is saved in a one-dimensional array of unsigned bytes. After filling the table with 20 bytes, the data are saved in the transmission frame and transmitted to the receiver. The described measurement algorithm allows to record 100 averaged single-byte measurements within a second. Considering that 20 bytes are saved in the PDU, the packets should be transmitted 5 times per second, i.e. at 200 ms intervals. In addition, the AM repeatability was verified. For this purpose, the frequency of a pseudobarinary signal generated by four independent multivibrators was measured. To ensure reproducible measurement conditions, the same network of passive elements was used in the design of each tested multivibra-
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Fig. 9. a) Example of a signal recorded using one of the BLE systems, b) and c) Average cycle times together with 95% confidence intervals, recorded by means of individual BLE modules, for normal and fast breathing, respectively d) dependence of the frequency generated by 4 multivibrators on the resistance simulating changes in the sensor.
tor. Only the OPA244 amplifier and the Rs resistance (Fig. 1b), which simulated a change in the sensor parameters, were changed. The parameters of the network of multivibrator passive elements were selected in such a way so as to generate signals with frequencies of up to 10 kHz, which corresponds to the AM operation with the real sensor during breathing activities. The described procedure allows for independent verification of each of the tested multivibrators.
3.4. Statistical analysis The compliance of the time intervals of individual cycles with the normal distribution was verified using the Shapiro-Wilk test and assessed graphically based on histograms. To evaluate the reproducibility of the signal after its remote registration, the U Mann-Whitney test was used. In order to compare the mean cycle
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Fig. 10. a) and b) respiratory signal recorded in parallel with SBS and Receiver Station, c) and d) diagrams showing the correlation between the start and end moments of the cycle recorded using SBS and Receiver Station (RS), respectively.
times transmitted independently by 6 BLE modules, a one-way analysis of variance (ANOVA) was performed. A p-value less than 0.05 was considered as statistically significant. In order to assess whether individual BLE modules correctly monitored the beginning and end of breath cycles, a regression and correlation analysis was performed. Because multiple comparisons were made between individual BLEs, results of p < 0.003 were considered significant after the Bonferroni correction. Statistical analysis was performed using Statistica (Dell Inc. (2016). Dell Statistica (data analysis software system), version 13. software.dell.com). One series of breath cycles was performed (Fig. 9a), which consisted of 10 normal breaths (about 12BPM) and 20 fast breaths (about 90BPM). These cycles transmitted by 6 identical BLE modules were recorded simultaneously to compare their operation. Both duration of individual breath cycles and their start and end moments were analysed (Fig. 9a). The variation in the duration of individual cycles was low and amounted to 8.27% for normal breathing and 7.18% for fast breathing. The results of analysis demonstrate that there is no statistically significant difference between the mean cycle times for every BLE module, both in the case of normal (p≈1) and fast (p≈1) breathing. The mean cycle times recorded by individual BLE modules and the corresponding 95% confidence intervals are shown in Fig. 9b and c. To assess whether individual BLE modules correctly monitored the beginning and end of breath cycles, a regression and correlation analysis was performed. In each of the comparisons, Pearson’s linear correlation coefficient r≈1 was obtained (p < 0.0001). Thus,
the correlation between the results from individual BLE modules is practically complete. Under the set transmission conditions, no PDU loss was noticed, which would affect the ability to reproduce the measurement signal. Data analysis showed that there were discrepancies in the values of individual measurement points for different BLEs. However, they were very small. The number of diverging points was 0.5–0.6%, in relation to all measuring points in the registered signal. During the reproduction of individual breathing cycles on the receiver side for individual BLEs, this discrepancy occurred in different parts of the reproduced signal. The article did not analyse the reasons for discrepancies, only the existence of this fact was noted. Fig. 9d presents the results obtained from the comparison of the multivibrator system working with 4 different operational amplifiers. It clearly shows high repeatability in generating a specific frequency value when changing the resistance RS , simulating a change in the resistance of MCBS. In order to compare the values of frequencies generated using individual amplifiers (OPA244), regression and correlation analysis was also used. Since multiple comparisons were made, the Bonferroni correction was applied. Therefore, the results with p < 0.0083 were considered statistically significant. In each of the six comparisons (OPA 1 vs OPA 2, OPA 1 vs. OPA 3 etc.), the correlation was close to complete (r≈1, p < 0.0001). Differences between frequency readings from the systems with particular amplifiers were on average 0.9%. Thus, the repeatability of the analogue part is very high and comparable to the digital part.
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Fig. 10a shows respiratory signals recorded on the transmitter side (SBS) and Fig. 10b on the receiver side (Receiver Station). Signal registration on the transmitter side was realized using the f/V conversion block and OSC (Fig. 1e), whereas on the receiver side, the data from C were recorded on the PC (Fig. 1f). The mean duration of the cycle recorded on both sides was compared, which allowed to evaluate the reproducibility of the signal after its remote registration. Differences proved to be statistically insignificant for both normal (p = 0.6889) and fast (p = 0.3226) breathing. The beginning and end of the cycle were also correlated. In both cases (normal and fast breathing), the correlation is statistically significant (p < 0.0001), with the Pearson’s linear correlation coefficient close to 1 (Fig. 10c and d). 4. Conclusions In computer technology, discrete transmission has been used for many years. Data are fragmented and transmitted in this form. Systems operating in the beacon mode are energy-efficient because they do not carry out transmission in a continuous manner, but at defined time intervals. This method is usually used to transmit the permanent identifier of a transmitting device. However, the transmitted data can be modified, which allows for the transmission of additional information. Then the amount of data that can be transmitted is related to the transmission frequency of advertising packets. Limiting the number of transmission cycles per unit of time saves energy, and as a result the radio interface functions effectively in battery-powered systems. Additionally, when transmitting in advertising mode, there is no need to perform authentication procedures between the transmitter and the receiver. The beacon mode is usually used for sending simple messages or building navigation [36–38]. However, the presented configuration of the radio interface enables to use it for breath monitoring. The key element of the system is a micro-condensation sensor, which is part of the relaxation oscillator circuit (mulitvibrator). The sensor changes its impedance parameters under the influence of breathing, and consequently the frequency of the rectangular signal generated by the multivibrator. By using the micro power multivibrator, which generates a pseudobinary signal sent directly to microcontroller binary inputs, the solution proposed in this paper eliminates the need for an ADC converter, which is commonly used in biomedical signal measurements [36]. The relaxation oscillator is connected to the microcontroller input, which acts as both a measurement and radio interface control system. The results of rectangular signal frequency measurements are stored sequentially on 20 consecutive bytes of the BLE packet in advertising mode. Currently, complex sensor systems cooperating with smartphones, which allow for continuous monitoring of parameters, have acceptable power consumption of several tens of mA [19]. The presented system owing to the use of energy-saving electronic components allows for continuous breath monitoring with minimal energy consumption, and reduced dimensions of the power source enable to minimize the size of the monitoring module. The power supply of the relaxation oscillator and SoC with a voltage of approx. 2 V minimizes energy consumption during respiratory monitoring. Connectionless transmission limits current consumption of the SBS module between transmission cycles to the peak value of about 2.5 mA. In turn, the peak value of current consumption during the transmission cycle (several pulses with an interval of 0.5 ms) is about 15 mA. The aspects of energy saving can also be considered taking into account the communication between the transmitter and the receiver. The process of establishing and terminating the connection constitutes a high energy load for the system [21]. In the case of connectionless transmission, this problem is significantly reduced.
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Michał Pielka has been a PhD student at the Institute of Computer Science since 2017. His research interests are: embedded systems, information-communication technologies, programming.
Malgorzata A. Janik received her PhD in physical sciences from the University of Silesia in 2007. Her research interests are related to biomedical engineering and biostatistics.
Biographies
Pawel Janik received his PhD in technical sciences from the University of Silesia in 2002. His research interests lie mainly in biomedical engineering, electronic measuring equipment and electronics.
Zygmunt Wrobel - Prof. dr hab. engineer, Head of the Department of Biomedical Computer Systems at the Institute of Computer Science, University of Silesia in Katowice. His research interests are: computer analysis and processing of biomedical signals and images, and information technology in medicine and biotechnology. He has authored or co-authored several scientific papers on these subjects. He is also a co-author and editor in chief of several monographs published at the University of Silesia.
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Respiratory monitoring system using Bluetooth Low Energy P. Janik ∗ , M. Pielka, M.A. Janik, Z. Wróbel University of Silesia in Katowice, Faculty of Computer Science and Materials Science, Institute of Computer Science, Department of Biomedical Computer nska 39, 41-200 Sosnowiec, Poland Systems, ul. B˛edzi´
a r t i c l e
i n f o
Article history: Received 10 May 2018 Received in revised form 22 December 2018 Accepted 25 December 2018 Available online 26 December 2018 Keywords: Beacon Breath monitoring IoT Micropower Mobile system Smart sensor
a b s t r a c t The paper presents a system for monitoring respiratory frequency and strength using a BLE transmitter in non-connectable advertising mode and a variable impedance sensor operating in a relaxation oscillator circuit. The presented solution is characterized by low energy consumption. Both the measurement system and teletransmission system operate at 2.2 V power supply. By using a micropower operational amplifier, the analogue part of the system consumes 240–300 W. Unipolar power supply and configuration of the relaxation oscillator enable to create a pseudobinary signal with the frequency f < 10 kHz. The relaxation oscillator is connected to the microcontroller binary input without having to convert the signal. By using a System On Chip (SoC) with CPU ARM Cortex to control the radio and perform measurements of the pseudobinary signal, the design was simplified and its dimensions were reduced. The operation of the analogue part of the system was compared with a commercial sensor. In addition, the ability of the system to reproduce the breath cycle signal on the BLE side was verified. Differences between the duration of individual cycles turned out to be statistically insignificant, both in the case of normal (p = 0.6889) and fast (p = 0.3226) breathing. © 2018 Elsevier B.V. All rights reserved.
1. Introduction Respiratory monitoring is widely used in medicine, emergency medical services and sport, and nowadays also at home. Breath control is particularly important in the case of respiratory diseases, not only during self-breathing but also during mechanical ventilation. Respiratory monitoring can be carried out in multiple ways, by using, inter alia, humidity [1–4], gas flow [5,6], vibration (acceleration) [7,8], temperature [6,9], capacitive [10], piezo-resistive [11], pressure [12], or pyroelectric sensors [7,13]. There are also systems that use the magneto-elastic effect [14] in breath monitoring, or systems based on acoustic signal analysis [15]. Monitoring vital signs, including respiration, is usually a longterm process. Therefore, it is convenient to perform remote measurements by using contactless systems that monitor, for example, breath, pulse or body movement from a relatively small distance [16,17], or biomonitoring systems that use teletransmission technologies [18,19]. This approach provides the monitored person with relative comfort, e.g. during sleep. One of the problems with designing remote monitoring systems is energy consumption, in particular that of the radio system.
∗ Corresponding author. E-mail address: [email protected] (P. Janik). https://doi.org/10.1016/j.sna.2018.12.040 0924-4247/© 2018 Elsevier B.V. All rights reserved.
Currently, Bluetooth Low Energy (BLE) is commonly used for communication between monitoring systems and mobile devices, e.g. smartphones [19–22]. It is also used in energy-saving measurement systems [23,24]. However, standard solutions offered on the market use the connection mode (e.g. GATT) between BLE interfaces, which requires establishing a connection between communicating devices. The connection transmission in BLE is used to transmit more data, e.g. from the device memory, after which the connection may break. Despite the low power consumption of radio interfaces in the connection mode (about a few/a dozen mA), the use of BLE for continuous monitoring is limited, especially in battery-powered devices. By default, Bluetooth operates in the star topology. However, the mesh network topology is now also propagated [25]. BLE nonconnectable advertising can be an alternative to these topologies. It can be used for continuous monitoring of various processes by means of a pulsed radio interface. The BLE transmitter configured in non-connectable advertising mode (broadcaster in GAP profile) is commonly known as a beacon. Its basic function is to distribute its unique identifier, owing to which such systems are used in radio identification systems [26] or indoor navigation [27–29]. It is possible to realize many-to-many transmissions by using advertising mode. The paper presents a system which, by using energy-efficient base components and ICT (Information and Communication Tech-
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nologies), allows for a significant reduction in power consumption during operation. The presented design was developed in the form of an integrated radio sensor, using a programmable BLE module. The solution, due to its small size, is wearable and the applied radio interface enables to extend its functionality to the Internet of Things (IoT). The small smart sensor can be mounted, for example, on inhalation masks with an average oxygen concentration. Moreover, the device does not restrict the movements of the monitored person owing to the radio data transmission. The detector element can be placed at the outlet of one of the mask ventilation openings The system is characterized by the following features: i) simple system design, ii) low energy consumption by using an energyefficient radio interface, iii) the possibility of cooperation with mobile devices, iv) small dimensions, v) low production costs, vi) short time of reaction of the system to respiratory functions and vii) ability to work in many configurations of the teletransmission network. To maintain the compatibility of the presented solution with mobile devices, the method of advertising described in the Bluetooth Core specification has been used. The presented smart sensor can also create dedicated sensor networks, which allows for more efficient management of transmission channels. 2. Materials and methods 2.1. Measurement equipment The first element of the measuring chain is a parametric microcondensation breath sensor (MCBS) with a hydrophilic layer a commercially available octenidine-based antiseptic. The properties of this sensor type and the method of its preparation are described in [2], whereas its minimized version is described in more detail in [3]. The presented system uses a minimized version of MCBS, which is presented in Fig. 1a. Respiratory functions are processed into an electrical signal in the multivibrator system, shown conceptually in Fig. 1b. In turn, Fig. 1c shows the diagram of the Analogue Module (AM), i.e. a relaxation oscillator with MCBS. The sensor is incorporated into the negative feedback loop of the operational amplifier (Fig. 1c) with one interdigitated electrode connected to the inverting input and the other to the output, and together with the capacitor attached between the inverting input and the ground form the RS C branch. The oscillation frequency f of the output signal U0 depends on the time constant determined by the variable (depending on the breath phase) resistance of the sensor RS and the capacity of the capacitor C, and when R1 =R2 =R3 , it is [30]: f = 1/(R S Cln4) The multivibrator was realized using the OPA244 amplifier, which is characterized by low power consumption, and is classified as a micropower system, and low value of the minimum supply voltage. The discussed system uses R1 , R2 , R3 resistors with the value of 1 M each and the capacitor C = 4.7 nF. Moreover, the system uses the change of impedance parameters of MCBS. During breathing, the multivibrator generates a pseudobinary signal with a frequency dependent on the breath phase. Powering the multivibrator from an asymmetric voltage source causes the output signal to oscillate between the ground potential and the potential of the power source. Therefore, it can be treated as pseudobinary. This construction allows the analogue relaxation oscillator circuit (AM) to work directly with digital circuits. In order to demonstrate the functionality of MCBS in the multivibrator circuit and to compare it with the reference sensor, the measurement set shown in Fig. 1c was used. The pseudobinary output signal of the multivibrator is processed in the frequency-
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voltage converter block f/V, and then fed to one of the channels of the digital oscilloscope (OSC) from the converter output. The output of the reference sensor circuit is connected to another channel of the oscilloscope. This configuration of the system allows for concurrent registration of the output signals of two different sensors monitoring the same breathing functions. The HIH5030 [31] humidity sensor was used as a reference sensor. The reference sensor was configured in accordance with the manufacturer specifications. In order to compare the MCBS operating in the AM system with the HIH sensor, a 30 cm tube with an internal diameter of 14 mm was used, at the outlet of which both sensors were placed in such a way that they did not constitute resistance to the stream of exhaled and inhaled air. Breath of different speed was simulated by a healthy volunteer, who breathed through his mouth through the tube equipped with humidity sensors (Fig. 1d). In the Smart Breath Sensor (SBS) structure (Fig. 1e), AM was connected to the programmable BLE module. The output of the multivibrator is connected to one of the GPIO (General Purpose Input Output) ports of the microcontroller C. The microcontroller fulfils a measuring function and at the same time controls the operation of the radio frequency (RF) circuit. The discussed system uses a commercial, miniature BLE module [32] that includes a microcontroller with ARM architecture (Cortex M0) integrated with RF and a PCB antenna. The applied BLE module works in Bluetooth 4.2. The dotted lines (Fig. 1e) indicate symbolically the integrity of the commercial BLE module and the SBS electronics. Both the multivibrator and the BLE module were powered with a voltage of 2.2 V. SBS is an autonomous module that monitors breathing functions and transmits data from the monitoring process in advertising mode. Verification of SBS functionality requires confronting data regarding monitored respiratory functions on the receiver side. To this end, a two-chain measurement system presented in Fig. 1e and Fig. 1f was designed. The first measurement chain is related to the electrical signal path from the multivibrator to the recorder (OSC) (Fig. 1e). In turn, the second measurement chain concerns: signal measurement performed by the microcontroller of the SBS transmitter, data formatting, radio transmission (RF circuit), receiving and decoding data in the receiver module (BLE Module Receiver) and data recovery in a PC. A block diagram of the Receiver Station for receiving advertising data is presented in Fig. 1f. The BLE Module Receiver was powered from the FTDI converter, adjusting logic levels between the PC and the receiver microcontroller, communicated via the serial port. Since the measurements were performed in two parallel measuring chains, it was necessary to introduce a synchronization signal SYNC. The synchronization signal in the form of a single, cyclic impulse was generated by the microcontroller and fed to one of the channels of the OSC. At the same time, the advertising package was sent. This procedure enabled to synchronize signals recorded in two remote measuring chains. The respiratory function curves were registered using the Rigol MSO1104Z oscilloscope. Long-term battery discharge measurements were recorded using the I/O Device - National Instruments NI USB-6361 and LabView software. 2.2. Communication interface configuration The hardware layer of the presented solution uses programmable systems and the BLE interface (also called Bluetooth Smart). The integrated module of the microcontroller and BLE transmitter uses the GAP (Generic Access Profile). The module functions as a peripheral device in advertising mode broadcasting packets [33]. These packets have the format defined in the Bluetooth Core 4.2 specification [34] presented schematically in Fig. 2a. The packet contains several blocks: preamble, access address,
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Fig. 1. a) View of the printed circuit of MCBS, b) schematic diagram of the multivibrator, where RS represents resistance of MCBS, c) block diagram of the system for concurrent measurements of MCBS in the multivibrator system (AM) and the reference sensor, d) visualization of breath measurement with the tube, e) functional structure of the Smart Breath Sensor (SBS), f) diagram of the station for receiving and detecting advertising packets - Receiver Station.
Packet Data Unit (PDU) and Cyclic Redundancy Check (CRC) [35]. An important segment is PDU because it specifies when data or advertising transmissions take place. The PDU type in the discussed system was set at ADV NONCONN IND (Non-connectable undirected advertising event). This type does not allow for sending a scan request. The Advertising Data of PDU [34] section contains thirty-one bytes (D1 -D31 ), which can be modified. The BLE standard makes three channels available for broadcast. SBS uses a mechanism in which each of the BLE packets is transmitted via all three channels. The presented smart sensor maintains this way of transmission, which makes it compatible with devices compatible with Bluetooth Core 4.2, e.g. smartphones. Thus, every packet is transmitted three times. The interval for non-connectable transmission can be configured as a value between 100 ms and 10.24 s. The packet must have only a header and address (Fig. 2a), whereas Advertising Data do not have to be defined or may be equal to zero. When these data are non-zero, the first two bytes D1 and D2 (Fig. 2a) specify the length and type of Advertising Data. Thus, the configured BLE transmitter is called a non-connectable beacon. Fig. 2b presents the characteristics of BLE transmission in advertising mode in the presented system. Packets are broadcast in transmission cycles consisting of several impulses, usually lasting
about 0.5 ms each. During the transmission process, the current is drawn by the transmitter in a discrete manner, which is why the advertising packet transmission was presented as a cycle of impulses measured on the radio module power supply. The configuration of the BLE radio module in the advertising mode allows you to create sensor network structures with many transmitters and many receivers. This structure allows to monitor the breathing activity of many people on many receivers at the same time. However these aspects are not discussed in this article. The simplest structure is a single smart sensor in the advertising mode and a dedicated receiver with a BLE interface or a mobile device, as shown in Fig. 3a. Of course, data broadcast by a single advertiser (SBS) can be received by multiple receivers simultaneously. Fig. 3b presents an exemplary structure where several SBS broadcast data are received by several dedicated and mobile receivers (smartphones, tablets). In the case of connection transmission, there is an increased current consumption during the connection process between the two devices. If the connection is broken, the process is repeated. In addition, when multiple devices are involved in the connection transmission, the connection process may be delayed. In the case of transmission in the advertising mode, the connection is not established. Although the tested modules NRF51822 [32] showed a similar current consumption in the case of connection
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Fig. 2. a) BLE packet structure, b) Power consumption for transmission in nonconnectable advertising mode.
transmission and in the advertising mode implemented on three channels (about 0.5 mA), a significant reduction in power consumption was possible when the advertiser was configured to transmit via one channel. The advertiser then consumed about 0.25 mA. However, it should be noted that transmission via one channel does not ensure compatibility with, for example, smartphones, and requires a dedicated device scanning on the given advertising channel. 3. Results and disscussion 3.1. Analogue module (AM) In the process of generating a pseudobinary signal U0 by the multivibrator (Fig. 4a), a change in the resistance RS of MCBS during respiratory functions plays a predominant role. During the exhalation phase, a thin, conductive film is formed on the sensor surface (MCBS) due to water vapour condensation, which reduces the sen-
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sor resistance. In turn, during the inhalation phase, the opposite occurs. Fig. 4b shows the change in the resistance RS of the sensor during a few breath cycles. Arrows indicate the direction of resistance changes during breathing. The change in the sensor resistance RS was estimated based on the frequency f of the output signal from the multivibrator. For this purpose, the formula presented in Section 2.1 was used, taking into account that the system used a 4.7 nF capacitor. A reduction in the sensor resistance during exhalation results in increased frequency generated by the relaxation oscillator, whereas during inhalation, the frequency decreases. An example of the characteristics of the oscillator output signal and changes in the frequency f of the generated signal U0 during a single breath cycle are shown in Fig. 4a. The analogue module of SBS was compared with the reference sensor (HIH5030, Honeywell) according to the block diagram presented in Fig. 1c. The reference sensor was selected for the processing of the same physical quantity, which is realized by the analogue module of SBS, i.e. humidity. According to the manufacturer’s documentation, HIH5030 is also intended for use in medical devices, including sleep apnoea monitoring devices. In addition, the reference sensor is characterized by a relatively short response time in the group of humidity sensors - typically 5 s according to the product data sheet [31]. Fig. 5a and b present a summary of signals of respiratory function monitoring by means of MCBS in the multivibrator system (AM) and the reference sensor. The reaction of both sensors to a change in breath strength was examined, recording the cycles of normal breathing and very weak breathing (Fig. 5a). For normal breathing, local maxima and minima are visible on both recorded curves, whereas in the case of weak breathing, a significant decrease in amplitude for individual cycles can be observed. In the case of AM, each of the cycles of weak breathing is clearly separated on the recorded curve, but the detection of cycles for the reference sensor is difficult. In weak breathing, water vapour condensation on the surface of the sensors decreases and the reference sensor generates a falling edge. Especially in this area (marked with a rectangle in Fig. 5a, it is difficult to distinguish individual breath cycles of the reference sensor, whereas for AM this problem does not occur. Additionally, the AM system differentiates the amplitude of the breath cycle curve more precisely than the reference sensor, which is particularly evident for the last two cycles in Fig. 5a, the third and fourth cycle in Fig. 5b as well as the last two cycles in Fig. 5c. In turn, Fig. 5b shows the response of the reference sensor and AM to breathing rate changes. In the first phase of testing normal breathing (about 12BPM), both recorded curves are characterized by clearly separated cycles. In the case of accelerated breathing (approx. 30BPM), the reference sensor curve has a strongly reduced
Fig. 3. Examples of system configurations using SBS: a) one to one, b) many to many.
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Fig. 4. a) Pseudobinary signal generated by the multivibrator during a breath cycle, b) change in the resistance RS of MCBS during seven breath cycles.
Fig. 5. Comparison of the effectiveness of respiratory monitoring by MCBS and the reference sensor in terms of a) changes in breathing strength, b) changes in breathing rate, c) sensor response time, and d) scaled breathing curves registered in a long measurement, the upper graph shows a full one-hour measurement and the lower one is magnification of the area marked with a rectangle.
amplitude of the variable component representing the breathing cycles, whereas the value of its constant component is increased. It results from increased condensation of water vapour coming from the exhaled air on the sensor surface during fast breathing. Due to the relatively large inertia of the reference sensor, the layer of water vapour condensed on its surface is not sufficiently reduced during
fast breathing cycles. The AM is also characterized by a decrease in the amplitude of the recorded signal for accelerated breathing, but the problem of distinguishing individual cycles does not occur. The analysis of the curves presented in Fig. 5a and b also shows that the reference sensor has greater inertia. It is more precisely represented in Fig. 5c, where the beginning and end of the first
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breath cycle registered with the micro-condensation sensor are symbolically marked with dotted lines. Signals were recorded for normal breathing (approximately 12BPM). The system presented in the paper (AM) detects more quickly both the exhalation phase and the inhalation phase. The beginning of exhalation was usually recorded more than 300 ms earlier than on the reference sensor (HIH5030). In turn, the peak of the breath cycle, which means the end of the exhalation phase and the beginning of the inhalation phase, was recorded about 1 s earlier compared to the reference sensor. Inertia of the sensor is important especially when monitoring very weak or accelerated breathing - when the amplitude of the recorded curve decreases (Fig. 5a and b). Then, the delayed reaction of the sensor makes it difficult to detect the minima and maxima of individual breath cycles, which can be noticed in the case of the reference sensor. The curves for AM shown in Fig. 5a, b and c were mapped using a scale expressed in volts in order to plot them against the reference sensor curves with a voltage output. The measurements were performed on the system shown in Fig. 1c. The f/V converter was set at 670 Hz/V. In order to demonstrate stable operation of AM, the measurement was carried out over a long period of time, lasting over an hour (Fig. 5d). 3.2. Smart breath sensor In order to simplify the structure of SBS, miniature module based on the NRF51822 chipset was used. According to the manufacturer (Nordic Semiconductor), the chipset contains a 32-bit ARM Cortex M0 microcontroller, which provides its resources to the user. Therefore, the SBS uses only two integrated circuits in respiratory monitoring: OPA244 and NRF51822. The block diagram of the implemented SBS module is presented in Fig. 6a. The DC-DC converter circuit providing 2.2 V power for the operational amplifier and the BLE module are placed on the shared PCB motherboard. The electronics are powered by a 3.7 V lithium-polymer battery. The PCB circuit sized 2.5 × 2.5 cm (Fig. 6b) with the battery (250 mAh) is placed in one housing, whereas the sensor is connected using a two-wire cable. One of the basic assumptions of the system was the use of reduced architecture (a single microcontroller supports the radio and performs measurements), which allows to reduce the size of the PCB circuit. The output signal of the multivibrator U0 (Fig. 4a) provides an amplitude of at least 2 V, so there is no need to convert it for the digital inputs of the applied microcontroller. Connecting the multivibrator output to the C binary input does not require the use of additional filtering systems, which further simplifies the presented structure. According to the specification [32], C binary inputs detect the high state from 0.7 VDD to VDD, whereas the low state from VSS (potential GND in the tested system) to 0.3VDD, which ensures stable operation of the system even in the case of fluctuations of the high and low state values of the pseudobinary signal. OPA244 requires only 2.2 V. With such power supply parameters, the relaxation oscillator circuit during breath monitoring consumes current of about 110 A to approx. 140 A, drawn from the DC-DC converter. Therefore, the power consumption by the multivibrator ranges from approx. 240W to approx. 300W. Connectionless transmission limits current consumption of the SBS module between transmission cycles to the peak value of about 2.5 mA (Fig. 2b). In turn, the peak value of current consumption during the transmission cycle (several pulses with an interval of 0.5 ms) is about 15 mA. Such parameters allow for long-lasting, continuous operation of the system with battery power supply. Energy consumption by the Smart Breath Sensor was also measured, which enabled to estimate the battery lifetime. Fig. 7 shows a discharge curve for a 250 mAh battery used to power SBS. The SBS module transmitted PDUs at intervals of 200 ms, whereas in the multivibrator, a 10k resistor was connected instead of the sensor with the
Fig. 6. a) Block diagram of SBS, b) complete Smart Breath Sensor (SBS) with the 250 mAh Li-Po battery, c) PCB motherboard of the test versions of SBS modules sized 2.5 × 3 cm.
Fig. 7. The discharge curve of the battery supplying the SBS module.
RS resistance, owing to which the multivibrator generated a pseudobinary signal with a frequency of about 8.9 kHz. The completed tests showed that SBS can work continuously for about 6 days. The power was cut off by the Protection Circuit Module (PCM) battery at 2.8 V after almost 144 h. The mean current value consumed by SBS is around 1.8 mA. Mapping of monitored respiratory functions is directly related to the frequency of the output signal from the multivibrator calculated based on the number of impulses per unit of time. The results
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Fig. 8. a) Block diagram of the extended station for testing SBS repeatability, b) view of both sides of the Multitransmitter Station used for testing the PCB circuit.
of these measurements are aggregated as data, sent in 20 bytes of Advertising Data (Fig. 2a). A single byte of PDU data (D5 –D24 in Fig. 2a) was used in the discussed system as a memory cell storing and transporting the measurement result of breath monitoring signal parameters. A proportionality coefficient was introduced to reproduce the actual measurement result of the multivibrator frequency. The proportionality coefficient divides without remainder the frequency of the multivibrator signal calculated in the microcontroller. As a result, the value of the calculated frequency can be saved on one byte, and then the results obtained are subsequently loaded into the PDU data (D5 -D24 ). The data quantity and type are stored on bytes D1 and D2 of Advertising Data, respectively. The device ID is stored on byte D3 , which makes it possible to distinguish SBS modules. The proportionality coefficient of the number of measured impulses is recorded on the fourth byte (D4 ).
3.3. Validation The measuring station shown in Fig. 1e) was extended to include additional BLE modules according to the block diagram presented in Fig. 8a. The same breath cycles were recorded in parallel. This measurement structure enables to verify the correctness of mapping of the same input signal by independent modules BLE1–BLE6. An example of a breathing function curve recorded on the receiver station side is shown in Fig. 9a. The structure of a multi-transmitter sensor network sending data to one receiver was tested. Each transmitting BLE module performs both measurements and signal transmission, whereas the receiver must receive PDUs from individual BLE1-BLE6, read the data contained in them and calculate frequency values, based on which the signal representing the breathing activity sent by each of the BLE modules will be reproduced. The aim of the study was to verify the correctness of both the transmission and reproduction of the respiratory signal on the receiver side as well as the measurements performed by independent digital circuits BLE with independent clocks. The obtained
results will allow to estimate what amount of PDU in the entire sensor network is lost at the set transmission parameters. A single AM was connected to six identical BLE transmitter modules (Fig. 8a and b), so that all BLE modules processed the same respiratory signal. In addition, frequency measurements of the multivibrator output signal were performed synchronously by all microcontrollers of BLE1–BLE6 modules. Thus, each of the BLE modules performed frequency measurements of the same signal at the same time (measured the same point indicated in Fig. 9a), so it was possible to compare the ability of individual modules to correctly reproduce the reference signal. The microcontroller performs period measurements (measurement of time between consecutive falling edges of a square wave) by means of a 32 bit Timer working with a base frequency of 16 MHz, scaled to 1 MHz. The Timer allows to measure time with an accuracy of 1 s. This Timer measurement resolution is sufficient to measure the period of the signal representing respiratory function, i.e. above 100 s (the frequency generated by the multivibrator during respiration is less than 10 kHz). The microcontroller performs measurements of signal periods for a set time (10 ms), and then the mean of period measurements is calculated. The obtained result is converted into frequency and then divided by the proportionality coefficient that is the same value that is saved in a frame on byte D4 . A proportionality coefficient of 40 was adopted in the measurements, which allows to record the frequency of 10200 Hz (255 · 40) on one byte. The result is saved in a one-dimensional array of unsigned bytes. After filling the table with 20 bytes, the data are saved in the transmission frame and transmitted to the receiver. The described measurement algorithm allows to record 100 averaged single-byte measurements within a second. Considering that 20 bytes are saved in the PDU, the packets should be transmitted 5 times per second, i.e. at 200 ms intervals. In addition, the AM repeatability was verified. For this purpose, the frequency of a pseudobarinary signal generated by four independent multivibrators was measured. To ensure reproducible measurement conditions, the same network of passive elements was used in the design of each tested multivibra-
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Fig. 9. a) Example of a signal recorded using one of the BLE systems, b) and c) Average cycle times together with 95% confidence intervals, recorded by means of individual BLE modules, for normal and fast breathing, respectively d) dependence of the frequency generated by 4 multivibrators on the resistance simulating changes in the sensor.
tor. Only the OPA244 amplifier and the Rs resistance (Fig. 1b), which simulated a change in the sensor parameters, were changed. The parameters of the network of multivibrator passive elements were selected in such a way so as to generate signals with frequencies of up to 10 kHz, which corresponds to the AM operation with the real sensor during breathing activities. The described procedure allows for independent verification of each of the tested multivibrators.
3.4. Statistical analysis The compliance of the time intervals of individual cycles with the normal distribution was verified using the Shapiro-Wilk test and assessed graphically based on histograms. To evaluate the reproducibility of the signal after its remote registration, the U Mann-Whitney test was used. In order to compare the mean cycle
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Fig. 10. a) and b) respiratory signal recorded in parallel with SBS and Receiver Station, c) and d) diagrams showing the correlation between the start and end moments of the cycle recorded using SBS and Receiver Station (RS), respectively.
times transmitted independently by 6 BLE modules, a one-way analysis of variance (ANOVA) was performed. A p-value less than 0.05 was considered as statistically significant. In order to assess whether individual BLE modules correctly monitored the beginning and end of breath cycles, a regression and correlation analysis was performed. Because multiple comparisons were made between individual BLEs, results of p < 0.003 were considered significant after the Bonferroni correction. Statistical analysis was performed using Statistica (Dell Inc. (2016). Dell Statistica (data analysis software system), version 13. software.dell.com). One series of breath cycles was performed (Fig. 9a), which consisted of 10 normal breaths (about 12BPM) and 20 fast breaths (about 90BPM). These cycles transmitted by 6 identical BLE modules were recorded simultaneously to compare their operation. Both duration of individual breath cycles and their start and end moments were analysed (Fig. 9a). The variation in the duration of individual cycles was low and amounted to 8.27% for normal breathing and 7.18% for fast breathing. The results of analysis demonstrate that there is no statistically significant difference between the mean cycle times for every BLE module, both in the case of normal (p≈1) and fast (p≈1) breathing. The mean cycle times recorded by individual BLE modules and the corresponding 95% confidence intervals are shown in Fig. 9b and c. To assess whether individual BLE modules correctly monitored the beginning and end of breath cycles, a regression and correlation analysis was performed. In each of the comparisons, Pearson’s linear correlation coefficient r≈1 was obtained (p < 0.0001). Thus,
the correlation between the results from individual BLE modules is practically complete. Under the set transmission conditions, no PDU loss was noticed, which would affect the ability to reproduce the measurement signal. Data analysis showed that there were discrepancies in the values of individual measurement points for different BLEs. However, they were very small. The number of diverging points was 0.5–0.6%, in relation to all measuring points in the registered signal. During the reproduction of individual breathing cycles on the receiver side for individual BLEs, this discrepancy occurred in different parts of the reproduced signal. The article did not analyse the reasons for discrepancies, only the existence of this fact was noted. Fig. 9d presents the results obtained from the comparison of the multivibrator system working with 4 different operational amplifiers. It clearly shows high repeatability in generating a specific frequency value when changing the resistance RS , simulating a change in the resistance of MCBS. In order to compare the values of frequencies generated using individual amplifiers (OPA244), regression and correlation analysis was also used. Since multiple comparisons were made, the Bonferroni correction was applied. Therefore, the results with p < 0.0083 were considered statistically significant. In each of the six comparisons (OPA 1 vs OPA 2, OPA 1 vs. OPA 3 etc.), the correlation was close to complete (r≈1, p < 0.0001). Differences between frequency readings from the systems with particular amplifiers were on average 0.9%. Thus, the repeatability of the analogue part is very high and comparable to the digital part.
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Fig. 10a shows respiratory signals recorded on the transmitter side (SBS) and Fig. 10b on the receiver side (Receiver Station). Signal registration on the transmitter side was realized using the f/V conversion block and OSC (Fig. 1e), whereas on the receiver side, the data from C were recorded on the PC (Fig. 1f). The mean duration of the cycle recorded on both sides was compared, which allowed to evaluate the reproducibility of the signal after its remote registration. Differences proved to be statistically insignificant for both normal (p = 0.6889) and fast (p = 0.3226) breathing. The beginning and end of the cycle were also correlated. In both cases (normal and fast breathing), the correlation is statistically significant (p < 0.0001), with the Pearson’s linear correlation coefficient close to 1 (Fig. 10c and d). 4. Conclusions In computer technology, discrete transmission has been used for many years. Data are fragmented and transmitted in this form. Systems operating in the beacon mode are energy-efficient because they do not carry out transmission in a continuous manner, but at defined time intervals. This method is usually used to transmit the permanent identifier of a transmitting device. However, the transmitted data can be modified, which allows for the transmission of additional information. Then the amount of data that can be transmitted is related to the transmission frequency of advertising packets. Limiting the number of transmission cycles per unit of time saves energy, and as a result the radio interface functions effectively in battery-powered systems. Additionally, when transmitting in advertising mode, there is no need to perform authentication procedures between the transmitter and the receiver. The beacon mode is usually used for sending simple messages or building navigation [36–38]. However, the presented configuration of the radio interface enables to use it for breath monitoring. The key element of the system is a micro-condensation sensor, which is part of the relaxation oscillator circuit (mulitvibrator). The sensor changes its impedance parameters under the influence of breathing, and consequently the frequency of the rectangular signal generated by the multivibrator. By using the micro power multivibrator, which generates a pseudobinary signal sent directly to microcontroller binary inputs, the solution proposed in this paper eliminates the need for an ADC converter, which is commonly used in biomedical signal measurements [36]. The relaxation oscillator is connected to the microcontroller input, which acts as both a measurement and radio interface control system. The results of rectangular signal frequency measurements are stored sequentially on 20 consecutive bytes of the BLE packet in advertising mode. Currently, complex sensor systems cooperating with smartphones, which allow for continuous monitoring of parameters, have acceptable power consumption of several tens of mA [19]. The presented system owing to the use of energy-saving electronic components allows for continuous breath monitoring with minimal energy consumption, and reduced dimensions of the power source enable to minimize the size of the monitoring module. The power supply of the relaxation oscillator and SoC with a voltage of approx. 2 V minimizes energy consumption during respiratory monitoring. Connectionless transmission limits current consumption of the SBS module between transmission cycles to the peak value of about 2.5 mA. In turn, the peak value of current consumption during the transmission cycle (several pulses with an interval of 0.5 ms) is about 15 mA. The aspects of energy saving can also be considered taking into account the communication between the transmitter and the receiver. The process of establishing and terminating the connection constitutes a high energy load for the system [21]. In the case of connectionless transmission, this problem is significantly reduced.
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Michał Pielka has been a PhD student at the Institute of Computer Science since 2017. His research interests are: embedded systems, information-communication technologies, programming.
Malgorzata A. Janik received her PhD in physical sciences from the University of Silesia in 2007. Her research interests are related to biomedical engineering and biostatistics.
Biographies
Pawel Janik received his PhD in technical sciences from the University of Silesia in 2002. His research interests lie mainly in biomedical engineering, electronic measuring equipment and electronics.
Zygmunt Wrobel - Prof. dr hab. engineer, Head of the Department of Biomedical Computer Systems at the Institute of Computer Science, University of Silesia in Katowice. His research interests are: computer analysis and processing of biomedical signals and images, and information technology in medicine and biotechnology. He has authored or co-authored several scientific papers on these subjects. He is also a co-author and editor in chief of several monographs published at the University of Silesia.