Intelligent Toilet System for Non-invasive Estimation of Blood-Sugar Level from Urine

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Original Article

Intelligent Toilet System for Non-invasive Estimation of Blood-Sugar Level from Urine P. Ghosh a,∗ , D. Bhattacharjee b , M. Nasipuri b a b

Department of Computer Science and Engineering, RCC Institute of Information Technology, Kolkata 700015, India Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India

h i g h l i g h t s

g r a p h i c a l

a b s t r a c t

• A complete customized mechanical unit, which controls the chemical process of urine sugar estimation. • An automatic technique to build the fuzzy membership functions from training data set. • Estimate the urine sugar level from training data set.

a r t i c l e

i n f o

Article history: Received 18 April 2019 Received in revised form 26 September 2019 Accepted 18 October 2019 Available online xxxx Keywords: Fuzzy membership function Reaction chamber Process control HSI color model Image filtering Petri-net Urine sugar estimation

a b s t r a c t Background and Objectives: Type-2 diabetes is one of the chronic diseases. This disease can be controlled by adjusting the dose of medicine, which is calculated from regular monitoring of blood sugar level. Blood glucose estimation methods are grouped into two categories direct and indirect. The direct method (invasive in nature) provides more accurate results; but people are not interested to test their blood several times in the day; because blood sample collection process is painful. On the other hand, indirect estimation methods are popular due to its non-invasive nature. The most widely used non-invasive blood glucose estimation method is based on urine sugar level estimation. Urine sugar level estimation is a chemical process requiring manual involvement. Human nature is very different; they dislike the repetitive work of testing urine regularly, although the process is not at all cumbersome. It will be very helpful if a system exists, which monitors urine sugar level automatically from the toilet. Methods: This work describes an automatic technique to estimate blood sugar level from urine. The contribution of this work is as follows:

• A complete customized mechanical unit, which controls the chemical process of urine sugar estimation. • An automatic technique to build the fuzzy membership functions from training data set. This system includes a chemical process control along with a fuzzy logic based color estimation technique, where fuzzy membership functions are derived from training data set. One salient feature of this fuzzy membership functions generator is that it is tuneable, that means it allows calibration after constructing membership functions. From application point of view, it is an intelligent toilet to keep track of blood sugar level from urine. The system is divided into two sub sections named as a control section and a computation section. The control section includes the control of mechanical units and chemical process initiation. The activeness of

*

Corresponding author. E-mail addresses: [email protected] (P. Ghosh), [email protected] (D. Bhattacharjee), [email protected] (M. Nasipuri).

https://doi.org/10.1016/j.irbm.2019.10.005 1959-0318/© 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.

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chemical reagent changes over time, this system has the provision to handle such situation through volume adjustment chamber. The control section includes a lot of valve control, they are interdependent. Petri-net is used to synchronise them. Computation section is used for estimation of urine sugar level from the changed color of Benedict’s Qualitative Solution. Result: From operational point of view, this system is a combination of sequential and parallel sub processes. It can be divided into 9 sub processes. The time required to complete all 9 processes is 660.5 second. This time includes sample collection time, chemical reaction time, result calculation and system cleaning time. The average Sensitivity, Specificity and error rate of the system are as follows 88.0225%, 95.95% and 5.765%. PIPEv4.3.0 is used to analysis the Petri-net. As per the analysis report, the system is safe (reliable). Discussion: This system is efficient to estimate blood sugar level from urine. This system senses the urine sugar level indirectly using the color sensor. The color sensor is not directly in touch with the chemical of the reaction chamber. The normal toilet cleaning (acidic) solution can be used to clean the chambers. So, maintenance process is quite easy. The proposed system can reduce the probability of glaucoma, kidney problem etc. by assisting doctors to control high blood sugar level through regular monitoring of urine sugar level. © 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.

1. Introduction Transportation of oxygen and nutrients to all living cells is one of the vital tasks of blood. The functionality and characteristics of blood are highly influenced by the concentration of its different constituents, like glucose, minerals, hemoglobins, etc. Different organs in the body are responsible for maintaining the proper ratio of these essential constituents in the blood [1]. Pancreas controls the blood glucose level through secretion of insulin. Diabetes is a condition in which the pancreas no longer produces enough insulin (type-1) or when cells stop responding to the insulin that is produced (type-2). As a consequence, glucose in the blood cannot be absorbed into the cells of the body. This situation increases the blood sugar level, and it is called Diabetes [2]. The symptoms of Diabetes include frequent urination, tiredness, excessive thirst, and hunger. This chronic disease is rarely get cured, but it can be controlled by changing the diet according to lifestyle. Oral medications and in some cases, daily injections of insulin are the prescribed treatment procedure. The statistic of blood glucose levels at different times is important to calculate medicine dose for a Diabetic patient [3,4]. WHO report predicts that, total number of diabetic patients in the world will grow from 246 Million (in 2006) to 380 Million by 2025 [5]. Out of this, approximately 10% of cases result from insulin deficiency (type-1), which often starts during childhood and requires giving this hormone usually many times a day. Rest of the cases are due to insulin resistance (type-2), occurring more in people of ages over 40 years [5]. Diabetes experts believe that blood-sugar level above 240 mg/dl is unacceptable and dangerous and the ideal level should be within 80 to 120 mg/dl range. Uncontrolled diabetes can give rise to many complications, which are either acute/short-term or chronic/long-term like Cataracts, Diabetic Retinopathy, Diabetic Nephropathy etc. [4,6]. Estimation methods for blood glucose level are classified into two categories, direct and indirect. In a direct method, blood glucose level is calculated directly from blood samples; it is invasive in nature. On the other hand, indirect estimation methods are popular due to its non-invasive nature. Blood glucose estimation from urine is the popular one. People are not interested to test their blood several times in the day, until they are really feeling bad; because blood sample collection process is painful. 1.1. Related work Reverse iontophoresis [7] is a blood glucose estimation technique from skin tissue. In this technique electro-osmosis is used to

extract neutral molecules like glucose through epidermis surface (skin). This extracted glucose is estimated via traditional glucose meter. This technology is adopted by Animas technology. The proposed glucose counting device is fitted in a wrist watch. The main challenges of this technique are poor accuracy and 2-3 hour is required for electro-osmosis which leads to skin irritation. Polarimetry [8] is another popular approach to estimate blood glucose value from vitreous humor (eye fluid). It is also an optical technique but gives a better result than other spectroscopic methods because it estimates glucose concentration from clear optical media within the eye. The average width of the anterior chamber of human eye is 1 cm. This gives 4.562 milli-degrees of rotation at 633 nm wavelength of light when the approximate glucose level is 5.55 mol/L. This rotation varies with glucose level concentration. This device is quite similar to contact lens with additional circuits. This method suffers from motion artifacts, optical noises, temperature and pH fluctuation; because this technique requires accurate measurement of small angle changes. H.D. Park et al. [9] describes a technique to estimate blood glucose level from urine. In this method they use a special type of biosensor that expresses the density of urine glucose in terms of output current of the sensor. The output current variation (in the range of μA) of the sensor is converted into voltage variation before feeding it into a μ-controller. This biosensor is useful for laboratory experiment, but cleaning and maintenance process is costly. Most of the intelligent toilet related works [10–13] use different types of bio-sensors to estimate urine sugar. This actually increases the recurring cost as well as maintenance cost. 1.2. Motivation All of the above mentioned non-invasive blood glucose estimation techniques are suitable for research purpose but not for daily use because; either they are time-consuming or recurring and maintenance cost is quite high. Blood glucose estimation from urine does not exactly estimate the blood glucose level, but one can get an idea whether blood glucose level is in normal tolerance range or high or very high range. This rough estimation is helpful to calculate statistical report on which the dose of medicine is calculated. Urine sugar level estimation is a chemical process requiring manual involvement. Human nature is very different; they dislike the repetitive work of testing urine regularly although the process is not at all cumbersome. It will be very helpful if a system exists, which monitors urine sugar level automatically from the toilet.

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Fig. 1. The block diagram of the Urine sugar level estimation system.

The contribution of this work is as follows: 1. A complete customized mechanical unit, which controls the chemical process of urine sugar estimation. 2. An automatic technique to build the fuzzy membership functions from training data set. 2. Urine sugar level estimation system Benedict’s Qualitative Solution is a chemical reagent used to detect the presence as well as the concentration of reducing sugars like glucose in a solution. This reagent starts a chemical reaction with reducing sugars and release copper in terms of ion or compound; this causes color changes of the solution [14]. The color of the obtained precipitate gives an idea about the quantity of sugar present in the solution. The default color of Benedict’s Qualitative Solution is blue. A greenish precipitate indicates about 0.5 g% concentration of reducing sugars; similarly yellow precipitate indicates 1 g% concentration; reddish orange indicates 1.5 g% or higher concentration. When blood glucose (sugar) is higher than normal level (determined by renal threshold [15]), then kidney tries to reduce that level by releasing excess glucose through urine. By examining this urine glucose level, blood glucose level can be estimated. It is a well-known procedure. As per experienced pathologist, blue color denotes that urine sugar is Nil that means blood sugar level is less than 180 mg/dl, Green means approximate blood sugar level is 180 - 220 mg/dl (denoted by +), in Yellow indicates blood sugar level 221 - 280 mg/dl (denoted by ++), Reddish Orange color (denoted by + + +) represents approximate blood sugar level 281 mg/dl or higher [16]. The block diagram of the proposed urine sugar level estimation system is shown in Fig. 1. The process of estimating urine sugar from urine sample, obtained from a customized toilet, consists of a sequence of sub-tasks, which are shown in the form of blocks in Fig. 1. The simplified work flow is as follows. When toilet is in use, the urine sample is collected (from a no-mix toilet [17–19]). After that, it is mixed with reagent in reaction chamber. Once all the ingredients are present in the reaction chamber then the system initiates the chemical reaction with external stimulant (heat). After completion of the chemical reaction, the system senses the basic color components of the solution (end product after reaction) through a transducer. For better computation, system converts color information from RGB color format to HIS format. Finally, fuzzy logic is applied to identify color and estimate the corresponding blood glucose level. The blocks are again partitioned into two groups for better understanding of the functionality. The groups are named as control section and computation section. The control section handles the synchronization of the functions of me-

Fig. 2. Drawing of mechanical arrangement of the reaction chamber.

chanical units and chemical process initiation. Computation section does the necessary mathematical computation for estimation of urine sugar level from the changed color of the solution. The subsequent sections describe the details of the system and the complex coordination of the physical units representing each sub-task. Petri-net is used to explain the complex coordination of the physical units. 2.1. Control section In the proposed system, urine samples are collected from nomix[17] toilet outlet [19]; the sketch of that kind of toilet is shown within Fig. 1. After collection of urine, it is mixed with reagents with proper ratio. The ratio depends on the concentration and aging factor of Benedict’s Qualitative Solution. To initiate the chemical process heating acts as an external stimulant. During the chemical reaction, it changes color and it is sensed by the CCD color sensor [20–22]. The captured color information is in RGB format, these RGB color components are sensitive to color shade variation. Decision may vary due to color shade variation. The reason behind color shade variation is the concentration and aging factor of Benedict’s Qualitative Solution. When the supplied Benedict’s Qualitative Solution is either diluted or pretty old then excess volume of Benedict’s Qualitative Solution is required; this additional volume dilutes the color of end product and this leads to color shade variation. To overcome such situation, true color information (Hue) is extracted from RGB information. In addition to this, computation for classification is less for single feature rather than three features (R, G, and B value features). Fuzzy logic based color classification and blood sugar level estimation is the final step of this subsystem. Fig. 2 shows the mechanical arrangement of the reaction chamber where chemical process takes place. In this arrangement, a 20 mm diameter clear glass cylinder is fixed at the center of another clear glass cylinder with 40 mm diameter. The tops of these two cylinders are fused with each other. For illumination, a light source is placed inside the inner cylinder. In this arrangement, three inlets are placed at the top of the outer cylinder and one outlet is at its bottom. This arrangement looks like a cylindrical chamber with inlet and outlet. This entire arrangement is mounted upon a heater to stimulate the solution for chemical reaction. All the inlets and

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Fig. 3. Drawing of volume adjustment chambers.

outlets are connected to four solenoid valves [23]. V1, V2, and V3 solenoid valves are connected to three inlets and V4 is connected to the outlet to drain the solution. One color sensor array (CCD type)is attached to the outer surface of the glass reaction chamber. Outlet of volume adjustment chamber C1 (outlet 1), shown in Fig. 3 is connected to inlet of reaction chamber through the valve V1. Similarly outlet of C2 (outlet 2) in Fig. 3 is connected to reaction chamber inlet through the valve V2. Fixed volumes of Benedict’s Qualitative Solution and urine enter the reaction chamber from the chambers C1 and C2 respectively. V3 is connected to the water supply. The water flow is required to clean the system after draining the solution. A light source is placed inside the inner cylinder of the reaction chamber, for proper illumination of the solution. The relative position of volume adjustment chambers and reaction chamber and connection among them, which is described above, are shown pictorially in Fig. 4. A fixed volume of Benedict’s Qualitative Solution is necessary for chemical reaction with a fixed volume of urine. However, the volume of Benedict’s Qualitative Solution depends on the quality and concentration of the solution. So a predetermined (but adjustable) volume of reagent and urine sample should be added to the reaction chamber. Fig. 3 shows the diagram of two adjustable volume chambers used for quantifying reagent and urine sample. The left one (C1) is used for storing Benedict’s Qualitative Solution. It is a cylindrical volume chamber with a piston. The piston can move vertically within the cylinder, and adjusts the inner volume of the “volume chamber.” Top of the chamber is closed. It has one inlet through which Benedict’s Qualitative Solution enters in the “volume chamber.” The outlet is at the middle of the piston, which is connected to the inlet of the reaction chamber through V1 valve, shown in Fig. 2. The V5 solenoid valve is placed in between Benedict’s Qualitative Solution tank and inlet and this valve opens during filling of the chamber C1 with the reagent. Another solenoid valve V6 is attached at the top of the cylinder along with the fluid level sensor. This valve opens during the filling of C1 to allow the air to leave the chamber C1, during filling. On the other hand, during the evacuation of the chamber C1, air enters the chamber through the same valve V6. The fluid level sensor is used to avoid overflow of the liquid through valve V6. The right side chamber (C2) in Fig. 3 is a similar type of volume adjustment chamber for urine sample. It also has a cylinder and piston arrangement with top open. It has one inlet and two outlets. The inlet pipe is connected with toilet outlet through a solenoid valve

Fig. 4. Pictorial representation of the relative position of volume adjustment and reaction chambers along with the flow of liquid.

V7, to collect a urine sample. Out of two outlets, one is placed in the middle of the piston and connected with the reaction chamber through V2 valve; the other one is placed near the top of the volume cylinder and connected to the drain. Excess urine sample in C2 is drained through that outlet. The top open arrangement is preferred for better cleaning facility. For both the volume adjustment chambers, the inner volume is adjusted by moving the pistons. Successful completion of the chemical reaction and data acquisition depends on the synchronization between seven valves and one heater. The operations and synchronization among seven solenoid valves, heaters and color sensors are modeled using a Petri-net [24], shown in Fig. 5. A Petri-net is a form of mathematical modeling language used for explaining operations and synchronization of different components in a distributed system. In this Petri-net, 27 places (named as P followed by place number) and 17 transactions (named as T followed by transaction number) are used to explain the system. Timed transactions T2, T4, T6, T10, T11, T14, and T15, are used to denote the operation of seven solenoid valves. As per Petri-net terminology, T0, T5, T7, T8, and T9 are called immediate transactions. P0 denotes the external input that informs the system to start. T0 is the immediate transaction that starts the system. T1 is timed transaction used to maintain the time gap between two consecutive transactions. The filling of Benedict’s Solution in C1 is independent of the external “start” input, but filling of urine in C2 depends on this input (output from T0). Valve V5 (T6) and valve V6 (T11) are used to fill Benedict’s Solution in C1. Valve V6 (T11) is common for both filling and evacuation of the volume chamber. For this reason, T7, T8, T9, and P12, are used to synchronize the operation of valve

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Fig. 5. Petri-net model of the proposed mechanical unit.

V6 during filling and evacuation of the volume chamber. Immediate transaction T8 is used to absolve excess token in P12 place. It guarantees that, at any instant, at most only one token exist at P12. Transaction T2 is used to collect urine in C2. P14 is used to denote that the system is ready and the valve V2 (T4) and valve V1 (T11) operates for evacuation of the two chambers C1 and C2 through intermediate transactions T3 and place P7. In addition to this, valve V2 (T4) needs to be opened at the time of cleaning the reaction chamber. T3 and P7 are used to synchronize these two operations, evacuation, and cleaning. T5 is another immediate transaction which is initialized at the time of cleaning (drain). P8 and P5 indicate the required volume of urine and Benedict’s Solution are in reaction chamber. T12 transaction controls the heater and it is a timed transaction. P16 indicates the completion of reaction. T13 is associated with color sensor. P17, P25 and P26 are the places that indicates completion of reaction and end product can be drained. T14 and T15 are used to drain the end product and clean the reaction chamber with supplied water. During draining, valve V2 is open through an immediate transaction T5. T17 sends system ready tokens when P18 contains two tokens (obtained from T14 and T15) and one token from P24. Various control actions for synchronization of mechanical and chemical processes can be written as steps of an algorithm as described in Algorithm 1. The computing unit works on 5 volt DC power supply, and I/O port’s current driving ability is less than 50 mA. So it is not possible to drive solenoid valves directly with the I/O ports. The I/O port is connected with the opto-isolater (MCT2E) for electrical isolation from comparatively high voltage and current. The output of the opto-isolater is fed to the solenoid coil via a current amplifier, because the current provided by the MCT2E is not sufficient to drive solenoid coil. At the time of switching off the solenoid coil, a high inverse EMF is generated by the solenoid coil. A diode is used to bypass the inverse EMF. After completion of mechanical and chemical processes, the computation takes place, the color sensor, which consists of a CCD

Algorithm 1 Synchronization of mechanical and chemical processes. Input: An external signal that informs the control unit that the toilet is in use. Output: Color information of the precipitate. Step 1: All the valves V1 to V7 (shown in Figs. 2 and 3) are closed. 2: Open V5 and V6 (shown in Figs. 3) to collect required volume of Benedict’s Solution. The Liquid level sensor senses the volume of liquid and closes both of these valves when volume chamber is full. 3: Open V7 (shown in Figs. 3) for a limited period to fill urine volume chamber. 4: Open V1, V6, and V2 (shown in Figs. 2 and 3). Through these valves required a volume of Benedict’s Qualitative Solution and urine are mixed in the reaction chamber. 5: Close V2 and open V7. This will clean the urine volume chamber with flushed water. 6: Reaction chamber is heated to start the reaction using the heater. 7: Wait for the chemical process completion and then stop the heater. The color sensor reads the R, G, B information. 8: Start the cleaning process by closing V7 and then opening V2, V3 and V4. By opening V2, the remaining urine in C2 chamber is drained. As V3 is connected with water supply so by opening V3 and V4, the reaction chamber is cleaned. 9: Wait for a time period and then close all valves. 10: Stop

array with m rows and n columns, reads the color information in terms of R, G, and B component values. The array of sensors provides better precisions than a single sensor. 2.2. Computation section The color change of the Benedict’s Qualitative Solution is uniform but the color information acquisition through color sensors is generally noisy. To reduce noise, spatial averaging filter [25] is used separately on each of the Red(R), Green(G), and Blue(B) components of the color information using Equation (1).

g (x, y ) =

a/2 

b/2 

s=−a/2 t =−b/2

w (s, t ) f (x + s, y + t )

(1)

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where w is the convolution mask of size (a + 1) ∗ (b + 1), and w (s, t ) is mask weight, the value of s ranges from −a/2 to +a/2 and that of t ranges from −b/2 to +b/2 where a and b are nonnegative integers. The center of the mask w (0, 0) coincides with the center pixel location (x, y ), whose value is replaced by the weighted average of the neighborhood pixel values determined by the mask. The RGB component contains not only the color information but also the color intensity. So from RGB components it is tough to identify the color. To overcome this problem HSI [25] color format is used. Where H stands for Hue (Pure color), S for saturation, i.e. the degree by which the pure color is diluted by white light and I for intensity (Gray level). The RGB to HSI color format conversion process is performed using the following equations [25].



H=

θ...... if B ≤ G 360 − θ... otherwise 

θ = cos

−1

S =1− I=

1 3

√

1 [( R 2



− G ) + ( R − B )]

( R − G )( R − G ) + ( R − B )(G − B )

3

(R + G + B)

[min( R , G , B )]

(R + G + B)

(2)

 (3) (4) (5)

The array of color sensors returns more than one set of RGB values. However, for classification, only one set of data is required. Algorithm 2 provides a selection technique from the set of data. In step 1, RGB information is converted into a color shade free format using Equation (3). The value of Hue varies from 0 to 1 where 360◦ is interpreted as 1 and 0◦ as 0. In polar metric concept, the absolute difference between 0◦ and 360◦ is very less. So hue 0 and 1 represents almost similar color. However, numerically the difference is high, and it creates confusion during classification. To resolve this, minimized interpretation is introduced. This is explained in step 2. The median value is selected in step 3. Algorithm 2 Hue value selection from the color sensor data set. Input: RGB component values from the sensor array. R is the number of the sensors in the array. Output: A single Hue value (ActualHue) to be used for decision making. Step 1: Apply Equation (2) on each RGB data set separately and store the values in H array. 2: create another array H_I of same size as H such that H_I(i) = minimum (H(i), 1- H(i)). 3: Sort the H_I array elements and find the median value. Store the value in ”ActualHue” variable. 4: Return the ActualHue. 5: Stop

Algorithm 2 will provide a single value. This value will be used in color detection (after reaction) and urine sugar estimation. 2.2.1. Color detection and decision making Locally available low cost equipments are used to design the system. So the color and tonal adjustments of the color sensing unit is not always same for all sensors. The exact brand and batch of the sensor may not be available after few months. In addition to this color shade of the glass reaction chamber is an issue; glass color shade of the reaction chamber may change due to sediment layers of end product and urine. So calibration of the system during maintenance is preferable to provide better accuracy. For this reason it is very difficult to define a crisp set to identify the pure

color from Hue value. To overcome this issue, a fuzzy set is used. The fuzzy membership [26–28] functions are built from a reference data set. The advantage of this membership function is flexibility, i.e. by changing the reference data set or training data set the response of the membership function can easily be changed. The generalized equation for the membership function from training data set is shown in Equation (6).

f (X) =

⎧ 0, ⎪ ⎪ ⎪ ⎨

if X < minimum( DataSet ) OR X > maximum( DataSet )

⎪ ⎪ ⎪ ⎩n

i =1

Yi.

n

X − Xi j =1, j =i X − X j

(6)

otherwise,

where f(X) is the response of the membership function, X is the input value, DataSet is the training set, n is the number of data in the DataSet, each element of the DataSet is a pair of values ( X i , Y i ) where X i stands for input and Y i for corresponding output of the membership function. In the case of urine glucose estimation using Benedict’s solution, the color of the compound after the reaction is any of the following colors viz. Blue, Green, Yellow, and Reddish Orange. So, only four color class training data sets are required, to build separate membership functions for the four different colors, mentioned before. Outcomes of these membership functions, for an input hue value are compared, and color is decided according to the membership function producing highest output value. The Algorithm 3 describes the construction of the fuzzy membership functions for each color class. HueVec is a vector that contains the hue values and Val is another vector that contains the response of the fuzzy membership value to a particular color class. The dimension of the HueVec vector and val vector must be same. HueVcc and Val vectors together are used to define the fuzzy membership function for that particular class. X is an input hue value (ActualHue) whose color class has to be detected. colorValue is an unknown sample of color whose color class have to be determined from the training data set. Algorithm 3 Find the fuzzy membership functions and determining the color class of a new color sample. Input: ActualHue obtained from Algorithm 2 and all color class training data set to build the fuzzy membership functions. Output: Color class of ActualHue. Step 1: X = ActualHue 2: K = 1 3: Repeat step 4 to step 18 until k is greater than 4. 4: Repeat step 5 to step 17 with kth color class training DataSet consisting of HueVec vector controlling hue values of that colot class and val vector containing corresponding fuzzy membership values. 5: If X is less than minimum of the value in HueVec then colorClass = 0. Stop. 6: If X is greater than maximum of the values in HueVec then colorClass = 0. Stop. 7: Sum = 0 8: i = 1 9: Repeat step 10 to step 16 Until i is greater than the number of elements in HueVec. 10: product = 1. 11: j = 1. 12: Repeat step 13 to step 14 until j is greater than the number of elements in HueVec 13: while ( i = j) product = product ∗ (( X − HueV ec ( j ))/( HueV ec (i ) − HueV ec ( j ))) j = j + 1. Sum = Sum + val(i) ∗ product. i = i + 1. colorVal(k) = Sum. k = K + 1. Find the largest element in colorVal and its index gives the colorClass of ActualHue. 20: Stop

14: 15: 16: 17: 18: 19:

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Table 1 List of Sub processes along with description of related transactions and execution times. Sub process

Description of transactions

Execution time

SubP1

Start the system. T0 is associated with this sub process. It is just triggering a switch. Very less amount of time is required.

0.5 second

SubP2

Maintain minimum time delay between two consecutive start processes (SubP1). T1 is associated with this.

660 seconds

SubP3

Loading C1 chamber. C1 is previously loaded with Benedict’s Qualitative Solution before start processes and again it can load after unloading of C1 chamber. T6 (valve V5), T7, T8, T9, T11 (valve V6) are associated with the sub process. The loading time of C1 chamber depends on the liquid level of Benedict’s Qualitative Solution tank and volume of C1 chamber.

23 seconds

SubP4

Unloading of C1 chamber and send the Benedict’s Qualitative Solution into reaction chamber. T10 (valve V1) and T11 (valve V6) are associated with this sub process. The unloading time depends on the volume of C1. However T10 and T11 are opened for 25 seconds to unload.

25 seconds

SubP5

Loading and unloading of C2 chamber. T2 (valve V7), T3, T4 (valve V2), T5 are associated with this sub process. T2 is opened for 10 second and after that T4 is opened 20 second.

(10 + 20) = 30 seconds

SubP6

Start reaction in reaction chamber. T12 (heater) is associated with this. Heater is ON for 8 minutes.

480 seconds

SubP7

Sense color. T13 is associated with this. It include color sensing and sending the information to the computing unit.

0.623021 second

SubP8

Clean the system. After chemical reaction, the system is cleaned. T17, T14 (Drain valve V4), T15 (Water supply valve V3) are associated with the sub process. V3 is open for 90 seconds and V4 is for 120 seconds.

120 seconds

SubP9

Color Detection and Decision Making.

2.915083 seconds

Fig. 6. The timing diagram of occurrence of sub processes.

2.2.2. Setup for computation section Raspberry Pi 2 (ARM Cortex-A7 processor based credit card size single board computer) is used to implement the computation section. It is configured in consol mode to make it faster. In terms of computational complexity none of the operations (Equation (1), Algorithm 2 and 3) exceed O (n2 ); n is the number of elements. So time complexity is affordable when n is small; for this reason number of training set is minimized. For better explanation, the pictorial results are used in experimental result section. To generate this pictorial representation, data are imported from Raspberry Pi 2 to MATLAB. 2.3. Processing time From operational point of view, this system is a combination of sequential and parallel sub processes, which is explained with the help of a petri-net (Fig. 6). It can be divided into 9 sub processes. The list of sub processes along with corresponding transactions and execution times are given in Table 1. The sub process 1 is associated with the triggering of the system (start the system). It is denoted as SubP1. It requires very less amount of time. Sub process 2 is basically a delay process. This sub process maintains the minimum time gap between two consecutive executions of start process (SubP1). The execution time of this sub process is set to 660 second on the basis of experimental results. It basically delays the next initiation of SubP1 until the tasks for all remaining sub processes are over. Sub process 3 (denoted as SubP3) is used to load the volume adjustment chamber C1 by operating different valves. Sub process 4 (SubP4) is used to unload the volume adjustment chamber C1. That means reagent is send to the reaction chamber. SubP3 waits until SubP4 completes. C1 chamber is loaded prior to the initiation of “start the system” (SubP1). For this reason SubP3 loads the C1 chamber for next “start the system” (SubP1). Sub process 5 (SubP5) is used for loading and unloading of C2 chamber via several valves. It releases urine samples in reaction chamber. Like C1 chamber, C2 chamber cannot be loaded prior

to the “start the system”. This sub process is associated with collection of urine sample on real-time basis. Sub process 6 (SubP6) denotes the reaction in reaction chamber. Sub process 7 (SubP7) is initiated after completion of SubP6. In SubP7 the data acquisition from the end product, after reaction, has been initiated. Sub process 8 (SubP8) cleans the system. Sub process 9 (SubP9) denotes the color detection and decision making. SubP8 and SubP9 may run in parallel. Fig. 6 shows the timing diagram of occurrence of sub processes. It is clear from the figure that SubP1 initiates the whole process. SubP2, SubP4, and SubP5 run concurrently after completion of SubP1. SubP6 starts after completion of SubP4 and SubP5. As the same way, SubP8 and SubP9 run concurrently after completion of SubP7. SubP7 starts after SubP6. SubP3 starts after SubP4. 2.4. Maintenance The urine sample is collected from the urine drain outlet of no-mix toilet (Fig. 1). No-mix toilet (designed in 1996 by Larsen and Gujer) [17,29,18,19] is a special type of toilet where there is a provision for collecting urine separately unlike the conventional toilet, which docs not have such provision. Maintenance of the system is of two types. The transducer is attached on the outer surface of reaction chamber. That means there is a glass layer between the sensor and chemical, so normal toilet cleaner can be used to clean the C2 chamber and reaction chamber in a periodic interval. The other type of maintenance is calibration. The strength of reagent changes over time and also with variation in manufacturing process. So volume of reagent will vary. This issue is resolved by the volume adjustment property of the C1 and C2 chamber. Both of the chambers have a piston mechanism to adjust the inner volume of the chamber C1 and C2 (Fig. 3). In regular interval the system is calibrated using a known amount of glucose solution and adjusting the inner volume of C1 chamber to get the desired result from the system. In this way the issue caused by the variation in the quality of reagent is resolved. 3. Experimental results and discussion The data captured from CCD array is represented in the form of an image; shown in Fig. 6. Fig. 7(a) shows the sample images of sensed color and Fig. 7(b) shows the same image after removal of noise. After that, hue (pure color) is extracted from the filtered image using Equation (2) and value is 0.0257 (almost 0 degree). The computation complexity for this type of fuzzy classification (SubP9) is O (n2 ). So, to minimize the computation time, the

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Table 2 Comparative study of the result for different number of training data set. Number of training sample

Hue value of test sample

Calculated value

Actual value

Remarks

26 20 16 12 10

0.4464 0.4464 0.4464 0.4464 0.4464

∞ −∞

75.00 75.00 75.00 75.00 75.00

ill-conditioned ill-conditioned Value is much different from actual value Result is acceptable Value should be more accurate

64.2607 74.0515 72.3321

Table 3 Training data set to construct membership functions for each color class. Training set for Blue

Training set for Green

Training set for Yellow

Training set for Reddish Orange

Hue

Membership value (%)

Hue

Membership value (%)

Hue

Membership value (%)

Hue

Membership value (%)

0.534 0.51 0.5 0.481 0.452 0.417 -

100 100 80 60 30 20 -

0.5 0.481 0.452 0.417 0.394 0.333 0.314 0.272 0.245 0.214 0.192 0.184

20 40 70 80 90 100 100 100 80 70 20 10

0.245 0.214 0.192 0.184 0.166 0.148 0.127 0.106 0.082 0.06 0.039 -

20 30 80 90 90 70 60 50 40 30 20 -

0.166 0.148 0.127 0.106 0.082 0.06 0.039 0.007 -

10 30 40 50 60 70 80 100 -

value of n should be minimum. For this typical data set, where the number of training data set is 37, and 2.915083 second is required for computation. However, as per timing diagram, shown in Fig. 5, SubP9 can be given at most 120 seconds (until SubP8 completion) without hampering the final response time of the system. Apparently it seems that more training data provides more accurate result. But in reality it is not true. The color detection and decision making section approximate the fuzzy membership functions from training data sets. The degree of the polynomial of fuzzy membership function depends on the training data set. When the number of training data is K, then the degree of the polynomial is K-1. As per mathematician highorder polynomials tend to be ill-conditioned [30]. In addition to this the training data sets for green and yellow region estimation, form a special shaped curves (like a mountain pick), which is very similar to Rungi’s curve [31]. For this reason when the number of training data set is high then Rungi’s phenomenon [31] will occur. Experimental result reveals that when the number of training data set is 26 then the result is unacceptable. After that it is gradually reduced and checked with known samples. When the number of training data set is reduced to 12 then it provides satisfactory results. So the conclusion is that, the training data size should be kept at minimum to get a better fuzzy function. Table 2 shows a sample comparative study regarding the number of training data used for membership function construction for green color detection. This table shows the result is unacceptable when number of training sample is high. On the basis of trial and error the number of training data for each class is finalized. Table 3 shows the training sets. It is used to construct fuzzy membership functions for Blue, Green, Yellow, and Reddish Orange using Equation (6). The number of training data for each class is not optimum but they provide satisfactory results. In the Table 3 there are 8 columns, the 1st one shows the hue value and the 2nd column shows the membership values of the corresponding hue values for class Blue, the 1st and 2nd columns are used to build the fuzzy membership function for Blue color using Equation (6); similarly 3rd and 4th for Green, 5th and 6th for Yellow and finally 7th and 8th column for Reddish Orange classes respectively.

Fig. 7. The sensed color data (a) along with the noise removed data (b).

Table 4 shows a comparative study of misclassification between proposed single feature (Hue) based classification and three features based (R, G, B features) classification. The table has 9 columns; column 1 denotes serial number of test cases. Column 2, 3, 4, 5, and 6 denotes Red, Green, Blue, Hue, and Saturation values of test cases respectively. Hue and Saturation are in the scale of 0 to 1 range. Column 7 denotes actual color class of the test sample. Column 8 shows the response of the proposed hue based fuzzy classification method and Column 9 shows the response from popular K-means based multi class clustering technique where R, G, B components are used as feature values. The number of training sample is very less so K-mean is preferred. In this particular case when the number of features are increased (R, G, B features) then accuracy of classification decreases. Consider the test cases 5 and 6, both represents the same color green, but color shade is different. The proposed Hue base method handles this issue efficiently;

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Table 4 A comparative study between RGB based classification and Hue based (independent of color shade variation) classification. Serial number

Red component

Green component

Blue component

Hue

Saturation

Actual class

Hue based class prediction using proposed method

RGB based class prediction using K-means clustering

1 2 3 4 5 6 7 8 9 10 11

2 75 2 126 2 109 28 100 223 226 213

200 217 253 252 253 255 253 252 234 234 159

253 252 222 235 128 182 128 166 16 82 9

0.5333 0.5333 0.4777 0.4777 0.4166 0.4166 0.4055 0.4055 0.1750 0.1750 0.1222

0.99 0.70 0.99 0.50 0.99 0.57 0.88 0.66 0.93 0.65 0.95

Blue Blue Blue Blue Green Green Green Green Yellow Yellow Yellow

Blue Blue Blue Blue Green Green Green Green Yellow Yellow Yellow

Blue Blue Blue Blue Green Red Green Red Yellow Yellow Red

Fig. 8. The membership function along with detected value.

Fig. 9. Sensed color data set along with detected value using membership function.

but R, G, B features based clustering technique becomes confused and provides erroneous result. So, it can be concluded that Hue feature based color classification is much better than R, G, B features based color classification. The single Hue value, which is calculated from the processed image (shown in Fig. 7(b)), is fed into the Color Detection and Decision Making sub process (SubP9) and corresponding graphical representation is shown in Fig. 8. In Fig. 8, the red vertical line maps the position of the single hue value of the test sample. The red curve line shows the reddish orange membership function’s response decaying against hue value. Yellow line shows the response of the yellow fuzzy set and similarly green and blue lines for the response of green and blue fuzzy set. For the input Fig. 7(a), red-

dish orange component has the highest membership value, and it is 85.6541. So the final decision is that the color is ’Reddish Orange’. According to the pathologist, the approximate blood sugar level will be greater than 281 mg/dl for that particular color shown in Fig. 7(a). Fig. 9 and 10 shows another two test cases along with pictorial representation. These two images are divided into two parts. The left side denotes the color information of the sample and right side denotes corresponding position in fuzzy membership function. The color information in left part of Fig. 9 have been classified as blue and it is shown by the vertical red line in right part of Fig. 9. Its medical interpretation is that urine sugar is nil. On the other hand Fig. 10 suggests urine sugar is present (in the range of green)

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Fig. 10. Sensed color data set along with detected value using membership function.

Table 5 Comparative study of performances the proposed method and the manual method. Metric and parameters

False positive False negative True positive True negative Error rate Precision Sensitivity Specificity

Less than 180 mg/dl (Blue)

Within 180 - 220 mg/dl (Green)

PSys

MSys

PSys

MSys

PSys

MSys

PSys

MSys

2 3 39 86 3.84% 95.12% 92.85% 97.72%

4 4 38 84 6.15% 90.47% 90.48% 95.45%

7 5 41 77 9.23% 85.41% 89.13% 91.66%

9 8 38 75 13.07% 80.85% 82.60% 89.28%

4 6 19 101 7.69% 82.60% 76.00% 96.19%

6 7 18 99 10.00% 75.00% 72.00% 94.28%

2 1 16 111 2.30% 88.88% 94.11% 98.23%

2 2 15 111 3.07% 88.23% 88.23% 98.23%

and corresponding estimated blood sugar level is within 180 - 220 mg/dl. Table 5 shows a comparative study between proposed system (PSys) and manual urine sugar testing process using Benedict’s Qualitative Solution (MSys). A laboratory technician from KMC assists to complete the manual testing (MSys). The perfection of results for the MSys, totally depends on personal skill. Data from actual invasive blood sugar measuring system is considered as ground truth data. Total 130 urine samples along with actual blood sugar data are used to test the system. The total numbers of samples are categorized into four categories viz. blood sugar level less than 180 mg/dl (Blue), blood sugar level within 180 - 220 mg/dl (Green), blood sugar level within 221 - 280 mg/dl (Yellow), and blood sugar level higher than 280 mg/dl (Reddish Orange). The number of samples for each category is as follows 42, 46, 25, and 17. In this table, comparisons of metric for four different classes are described. As already mentioned, 42 samples are considered for blue positive class; so rest of the samples (130 − 42) = 88 are considered for blue negative class. Similarly the negative classes for green, yellow, and reddish orange are 84, 105, and 113 respectively. In the proposed system, for blue class, 2 samples from blue negative class have been classified blue positive class. For this reason 2 samples are considered as false positive for the PSys in blue class. Similarly, 3 samples from blue positive class have been treated as blue negative class. This is considered as false negative. The true positive samples are (42 − 3) = 39 and true negative samples are (88 − 2) = 86. The results for MSys are comparatively poor due to human perception. Human eye detects the color from RGB information, which is highly confused by color shade variation. The false positive of PSys for green class is 7; out of which 3 comes from false negative of blue class and 4 from false negative of yellow class. Similarly false negative of PSys for green class is 5; out of which 2 and 3 comes from false positive of blue and yellow

Within 221 - 280 mg/dl (Yellow)

Higher than 280 mg/dl (Reddish Orange)

class respectively. The true positive and true negative for green class are (46 − 5) = 41 and (84 − 7) = 77 respectively. The false positive, false negative, true positive, and true negative of PSys for yellow class are as follows 4, 6, 19, and 101. Out of 4 false positive 3 comes from false negative of green class and 1 from reddish orange class. Similarly out of 6 false negative class 4 and 2 comes from false positive of green and reddish orange class respectively. The metrics for the yellow class are poor than other classes. This is because the range of Hue value for yellow class is less than other classes; this leads more confusion in the proper classification of yellow class. One of the main reasons for false positive and false negative is the renal threshold. In general when blood sugar level is above 180 mg/dl then kidney releases glucose in the urine. This threshold is called renal threshold. But diabetic patient, who are suffering for a long time, may have different renal threshold [32]. As per the medical guideline, for a diabetic patient, the blood sugar level is said to be under control if it is within 180 mg/dl after meals [33]. But if the renal threshold of the patient is decreased to 160 mg/dl then sugar will be found in his/her urine and it will be treated as in the range of 180 - 220 mg/dl. This is basically misclassification. PIPEv4.3.0 is used to analysis the Petri-net. As per the analysis report (shown in Fig. 11), the system is safe (reliable) and bounded. As the initial state (like P0) is not connected with the output of any transition; so deadlock situation may arise if there is no token (external signal to start the system) present in initial state. So the system will wait until the toilet is in use. Finally it can be concluded that the proposed system is stable. 4. Conclusion This system is efficient to estimate blood sugar level from urine. This system senses the urine sugar level indirectly using the color

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Acknowledgements

Fig. 11. Analysis report of the Petri-net using PIPEv4.3.0.

sensor. The color sensor is not directly in touch with the chemical of the reaction chamber. The normal toilet cleaning (acidic) solution can be used to clean the chambers. So, maintenance process is quite easy. Whereas the maintenance process of other automatic urine sugar monitoring systems like bio-sensors[9] are quite complex and costly. The proposed system can reduce the probability of glaucoma, kidney problem etc. by assisting doctors to control high blood sugar level through regular monitoring of urine sugar level. Future plan of this work is to enhance the learning as well as decision making algorithms. As a next phase of work, authors are planning to incorporate the techniques to identify the family members from non embarrassing data. “Non embarrassing” data may be body weight, toilet door operating pattern etc. Human and animal rights The authors declare that the work described has been carried out in accordance with the Declaration of Helsinki of the World Medical Association revised in 2013 for experiments involving humans as well as in accordance with the EU Directive 2010/63/EU for animal experiments. Informed consent and patient details The authors declare that this report does not contain any personal information that could lead to the identification of the patient(s). The authors declare that they obtained a written informed consent from the patients and/or volunteers included in the article. The authors also confirm that the personal details of the patients and/or volunteers have been removed. Funding This work did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions All authors attest that they meet the current International Committee of Medical Journal Editors (ICMJE) criteria for Authorship. Declaration of competing interest The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper. CRediT authorship contribution statement P. Ghosh: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Visualization, Writing - original draft. D. Bhattacharjee: Supervision, Validation, Writing - review & editing. M. Nasipuri: Formal analysis, Supervision, Validation, Writing - review & editing.

Dr. Soumendu Datta (Medical practitioner) and Dr. Abhijit Sen (pathologist) imparts relevant knowledge regarding type 2 diabetes; without their support it is difficult to proceed. Samples are supplied by Kolkata Municipal Corporation (KMC). Authors are thankfully acknowledging their support. There is no conflict of interest for this work. References [1] Wintrobe MM, Greer JP. Wintrobe’s clinical hematology, vol. 1. Lippincott Williams & Wilkins; 2009. [2] Nathan DM, Delahanty LM. Beating diabetes (a Harvard Medical School book): lower your blood sugar, lose weight, and stop diabetes and its complications in their tracks. McGraw Hill Professional; 2006. [3] Wass JA, Stewart PM. Oxford textbook of endocrinology and diabetes. Oxford University Press; 2011. [4] Veves A, Giurini JM, LoGerfo FW. The diabetic foot: medical and surgical management. Springer Science & Business Media; 2012. [5] World Health Organization. Global report on diabete; 2016. [6] Akben S. Early stage chronic kidney disease diagnosis by applying data mining methods to urinalysis, blood analysis and disease history. IRBM 2018;39(5):353–8. [7] Leboulanger B, Guy RH, Delgado-Charro MB. Reverse iontophoresis for noninvasive transdermal monitoring. Physiol Meas 2004;25(3):R35. [8] Cameron BD, Baba JS, Cote GL. Optical polarimetry applied to the development of a noninvasive in-vivo glucose monitor. In: BiOS 2000 the international symposium on biomedical optics. International Society for Optics and Photonics; 2000. p. 66–77. [9] Park HD, Lee KJ, Yoon HR, Nam HH. Design of a portable urine glucose monitoring system for health care. Comput Biol Med 2005;35(4):275–86. [10] Schlebusch T, Leonhardt S. Intelligent toilet system for health screening. In: Ubiquitous intelligence and computing; 2011. p. 152–60. [11] Dong T. Design consideration of a health-information-technology-supported intelligent urinalysis system. Adv Mater Res, vol. 989. Trans Tech Publ; 2014. p. 1077–81. [12] Choden P, Seesaard T, Dorji U, Sriphrapradang C, Kerdcharoen T. Urine odor detection by electronic nose for smart toilet application. In: Electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), 2017 14th international conference on. IEEE; 2017. p. 190–3. [13] Schlebusch T, Fichtner W, Mertig M, Leonhardt S. Unobtrusive and comprehensive health screening using an intelligent toilet system. Biomed Eng/Biomed Tech 2015;60(1):17–29. [14] Benedict SR. The detection and estimation of glucose in urine. J Am Med Assoc 1911;57(15):1193–4. [15] Butterfield W, Keen H, Whichelow M. Renal glucose threshold variations with age. Br Med J 1967;4(5578):505. [16] Free AH, Adams EC, Kercher ML, Free HM, Cook MH. Simple specific test for urine glucose. Clin Chem 1957;3(3):163–8. [17] Larsen TA, Peters I, Alder A, Eggen R, Maurer M, Muncke J. Re-engineering the toilet for sustainable wastewater management; 2001. [18] Karak T, Bhattacharyya P. Human urine as a source of alternative natural fertilizer in agriculture: a flight of fancy or an achievable reality. Resour Conserv Recycl 2011;55(4):400–8. [19] Green W, Ho G. Small scale sanitation technologies. Water Sci Technol 2005;51(10):29–38. [20] Dillon PL, Lewis D, Kaspar FG. Color imaging system using a single ccd area array. IEEE Trans Electron Devices 1978;25:102–7. [21] Su K, Zou Q, Zhou J, Zou L, Li H, Wang T, et al. High-sensitive and highefficient biochemical analysis method using a bionic electronic eye in combination with a smartphone-based colorimetric reader system. Sens Actuators B, Chem 2015;216:134–40. [22] Fang J, Qiu X, Wan Z, Zou Q, Su K, Hu N, et al. A sensing smartphone and its portable accessory for on-site rapid biochemical detection of marine toxins. Anal Methods 2016;8(38):6895–902. [23] Van Varseveld RB, Bone GM. Accurate position control of a pneumatic actuator using on/off solenoid valves. IEEE/ASME Trans Mechatron 1997;2(3):195–204. [24] Peterson JL. Petri net theory and the modeling of systems. [25] Jain AK. Fundamentals of digital image processing. Prentice-Hall, Inc.; 1989. [26] Ghosh P, Bhattacharjee D, Nasipuri M. Automatic system for plasmodium species identification from microscopic images of blood-smear samples. J Healthc Inform Res 2017;1(2):231–59. [27] Datta S, Chaki N, Modak B. A novel technique to detect caries lesion using isophote concepts. IRBM 2019;40(3):174–82. [28] Ghosh P, Bhattacharjee D, Nasipuri M. Blood smear analyzer for white blood cell counting: a hybrid microscopic image analyzing technique. Appl Soft Comput 2016;46(3):1–10.

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