A self-adaptive foot-drop corrector using functional electrical stimulation (FES) modulated by tibialis anterior electromyography (EMG) dataset

Medical Engineering & Physics 35 (2013) 195–204

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A self-adaptive foot-drop corrector using functional electrical stimulation (FES) modulated by tibialis anterior electromyography (EMG) dataset Mo Chen a,b , Bian Wu a,b , Xinxin Lou a,b , Ting Zhao b , Jianhua Li c , Zhisheng Xu c , Xiaoling Hu d , Xiaoxiang Zheng a,b,∗ a

Department of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China Qiushi Academy for Advanced Studies, Zhejiang university, Hangzhou, Zhejiang, China c Department of Medical Rehabilitation, Sir Run Run Shaw Hospital, Affiliated with School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China d Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China b

a r t i c l e

i n f o

Article history: Received 28 April 2011 Received in revised form 16 April 2012 Accepted 28 April 2012 Keywords: Foot-drop Electromyography Functional electrical stimulation Step frequency prediction Stimulation envelope Rehabilitation

a b s t r a c t We developed a functional electrical stimulator for correcting the gait patterns of patients with footdrop problem. The stimulating electrical pulses of the system are modulated to evoke contractions of the tibialis anterior muscle, by emulating the normal patterns. The modulation is adaptive, i.e. the system can predict the user’s step frequency and the generated stimulation can match each step in real-time. In this study, step data from 11 young healthy volunteers were acquired, and five prediction algorithms were evaluated by the acquired data, including the average of Previous N steps (P-N), the Previous Nth step (P-Nth), General Regression Neural Network (GRNN), Autoregressive (AR) and Kalman filter (KF). The algorithm with the best efficiency-accuracy trade-off (P-N, when N = 5) was implemented in the FES system. System evaluation results obtained from a post-stroke patient with foot-drop showed that the system of this study demonstrated better performance on gait pattern correction than the methods widely adopted in commercial products. © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

1. Introduction After stroke, many patients experienced impaired motor function in the lower limbs [1,2]. Foot-drop, a result of altered neural transmission and reduced active control of the foot during walking, is one of the symptoms of impaired motor function [3]. Patients with foot-drop commonly lose the ability of ankle dorsiflexion in the affected limb, i.e. the upward movement of the ankle and toes. As a result, patients hitch their hip to prevent the toes from touching the ground. Foot-drop is also a reason for the instability of the gait [4]. There are many treatments on foot-drop, such as ankle-foot orthosis (AFO), treadmill walking training therapy and functional electrical stimulation (FES). FES is a widely used technique that uses electrical current to activate target muscles directly. In comparison with other methods, FES has several advantages, e.g. the foot-drop patients can achieve an adequate foot elevation during stimulation; the hip and knee flexion angles during walking can increase to normal ranges with an increase in ankle push-off;

∗ Corresponding author at: Department of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China. E-mail address: [email protected] (X. Zheng).

the ability to avoid a sudden obstacle also can be improved [5]. With FES, the gait pattern of patients with foot-drop became more symmetrical and stable than with the AFO. Different from AFO that needs to be tailor-made for individual patients, an FES system is generally applicable to patients with different limb sizes [4]. FES was first introduced to the treatment on foot-drop by Liberson et al. in 1961 [6]. They applied electrical charge via a pair of electrodes attached to the surface of the skin above the tibialis anterior (TA) muscle. During walking, the electrical stimulation would be triggered by a switch under user’s heel, when the affected foot entered the swing phase; and the stimulation would be turned off when the foot entered the stance phase. This helped the patients lift their feet during walking. After its first introduction, FES systems for correcting foot-drop have been greatly improved. In 1965, Vodovnik et al. developed a stimulator, which adopted adjustable trapezoidal envelope. It solved the problem of rapid contraction of the TA muscle and foot-flap caused by sudden shut-off of the stimulus [7]. Following this idea, a number of commercial foot-drop correcting systems became available on the market, such as NESS L300 (Bioness Inc., USA) and the devices described by Acimovic et al. in 1987 and Burridge et al. in 1997 [1,8,9]. In 2000, Lyons et al. modified the stimulation output by replacing the trapezoidal envelope with

1350-4533/$ – see front matter © 2012 IPEM. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.medengphy.2012.04.016

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Fig. 1. The system (a) and a sketch of its usage (b).

envelopes of TA muscle electromyography (EMG) from unimpaired persons. This system can make the affected TA muscle contract in a way similar to that of a healthy person. Thus, a patient could lift the affected foot close to the normal pattern [10]. However, one of the problems in the system design is lacking of adaptation to the walking speed, which has been identified as a significant factor of determining TA EMG intensity envelope patterns [11]. Usually, the walking speed of a person varies from step to step. For persons with foot-drop, their walking speeds would also vary during a training course with the improvement of their mobility [12]. To cope with the varied walking speed, Breen et al. developed a self-adaptive system, which temporally scaled the stimulation envelope proportional to the gait cycle duration [11]. However, it had been found that the TA EMG envelopes from different speeds could not be simply scaled with the same shape. For example, the ratio of the peak value for Toe-off burst to that of heel-strike burst is larger at a higher speed [13]. In order to design stimulation outputs closer to unimpaired gait patterns with varied speeds, it is necessary to build up a dataset of TA EMG envelopes at multiple speeds, so that the stimulation pattern at a given speed can be looked up from it. For this purpose, Byrne et al. recorded TA EMG of 10 young healthy subjects during their walking on a treadmill at 11 different speeds [13]. They suggested that with this dataset, a system could select suitable stimulation envelopes for different walking speeds. However, there are two problems should be solved before implementing the idea of envelope selection in practical FES applications, i.e. (1) to obtain the instantaneous walking speed before selecting the stimulation envelope, and (2) to conduct overground walking, rather than using a treadmill. Walking speed is the product of step frequency and step length, but only step frequency can be captured by the current systems [14]. Thus most of the current foot-drop correcting FES systems are unable to predict walking speed accurately. Furthermore, for the same person, the gait of walking on a treadmill is different from overground walking. On a treadmill, a person demonstrated less dorsiflexor moments, compared with overground walking; and differences in muscle activity were also observed between treadmill walking and overground walking, particularly in the tibialis anterior in stance phase, and in the hamstrings, vastus medialis and adductor longus in swing phase [15]. Another challenge in developing a self-adaptive foot-drop correcting system is to accurately predict the upcoming step frequency, which is necessary for selecting a proper stimulation envelope for the upcoming step. Some systems used the step frequency of the current gait cycle as the predicted value of the upcoming one [11,16]. However, their effectiveness and accuracy in prediction have not been evaluated by comparing with other methods.

In this work, we proposed to use TA EMG with different step frequencies during overground walking as the reference for stimulation generation, and we also compared the performance among different prediction methods on step frequency. Based on the results, a new foot-drop correcting FES system was developed to overcome the aforementioned limitations of the current FES systems. System evaluation on a post-stroke patient with foot-drop was also carried out to evaluate the efficacy of the new stimulator design. 2. Methodology In the experiment, we recorded the TA EMG of 10 healthy subjects while they were walking over ground in order to build up a dataset of stimulation envelopes. For step frequency prediction, step frequency data from 11 subjects were acquired and 5 algorithms were compared. The algorithm with the best efficiencyaccuracy trade-off was selected for our new FES system. To show its effectiveness, the new system was compared with a system using the common stimulating method (trapezoidal envelope) of commercial products in the system evaluation on a post-stroke patient with foot-drop. 2.1. Hardware description The FES system of this study consists of a DSP (TMS320F2812, Taxes Instrument Inc., USA) controlled electrical pulse generator, a pair of FES electrodes and a heel pressure sensor (Fig. 1). The pulse frequency is 40 Hz and the output voltage is fixed at 150 V. The stimulation intensity is adjusted by changing the pulse width from 20 ␮s (micro-second) to 400 ␮s. These values are consistent with typical protocols reported in the literature [17]. To simulate the variation of the EMG intensity, we used pulse width modulation (PWM) to generate a sequence of rectangular electrical pulses during a gait cycle. The width of each pulse is proportional to the corresponding intensity of the selected envelope. The FES circuit diagram is shown in Fig. 2. The whole system is portable with a weight of 193 g (without battery) and with a size of 9 cm × 9 cm × 2.4 cm. Heel pressure measured by a pressure sensor (FSR402, Interlink Electronics Inc., USA) is sent to a comparator with a preset threshold for identifying gait cycle status. Any pressure value below/above the threshold indicates a swing/stance phase. In our system, the comparator is built based on an operational amplifier (OPA2604, Taxes Instrument Inc., USA) and the threshold value is empirically set to 2 N. The DSP uses the heel pressure information to calculate the instantaneous step frequency of every step. According to the frequency information, the system selects the corresponding

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each individual subjects. The maximum output intensity was set when the subject feels uncomfortable with it. And then, the maximum value was used to normalize the stimulation intensities, ranging from 0 to 100%, when developing the envelope dataset. 2.2. Stimulation envelope design

Fig. 2. FES circuit diagram. The PWM signal drives the gate of the MOSFET to control the current in the transformer. The voltage of the 12 V source is amplified to nearly 800 V and then cut by D1 to 150 V as the final output. The voltage between Out and Out is 150 V and has the same pulse pattern as the PWM signal.

envelope from the dataset stored in the flash memory of DSP. The envelope modulates the output pulses that stimulate the stroke patient’s TA muscle through the pair of electrodes (Fig. 1). The flowchart of the DSP control program is shown in Fig. 3. The output range of the system was calibrated for

2.2.1. Experiment setup To develop the FES envelope dataset, 10 healthy subjects, including 5 males and 5 females, were recruited after signing a consent form of this study. Their ages ranged from 23 to 55 and the mean was 37 [16]. During the experiment the subjects were instructed to walk overground at 4 different step frequencies (60 steps/min, note as s/min, 80 s/min, 100 s/min and 120 s/min), which were controlled by a metronome (JM-62, Joyo Electric Instrument Inc., China). The walking distance was 50 meters for each frequency. To reduce the effect of fatigue, the order of walking frequencies was randomized and each subject had a 5-min rest after each 50-m walk. To eliminate the effect of shoes, in all experiments, including the evaluation test, subjects walked without shoes, only wearing a pair of thin socks. This barefoot test protocol has already been used in gait analysis related studies [18–20]. The TA EMG of

Fig. 3. Working flowchart of the system. In step 2, the initial step frequency is the normal step frequency of the subject without FES system. Steps 5–13 are the working loop of the program; steps 5–6 are running while the affacted leg is in the swing phase; steps 8–10 are runing while the affacted leg is in the stance phase. The system calculates the duration of the swing phase once the phase ends (step 7), then uses the duration to select the second part of the stimulation envelop from the envelope dataset (step 8). When the affacted leg enters stance phase, the system stimulates the TA muscle using the second part of the stimulation envelope selected in step 8 (step 9). When stance phase ends, the system calculates the duration of the whole cycle of the last gait (step 11), then using the duration and predicting algorithm (as described later), the system calculates the predicted step frequency of the next gait cycle (step 12). Using the predicted step frequency, the system selects the first part of the stimulation envelop from the envelop dataset (step 13). When the affacted leg enters the swing phase, the system stimulates TA mscle using the first part of the stimulation envelop selected in step 13 (step 5).

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Fig. 4. TA EMG intensity of a normal gait cycle. The vertical dashed line in the middle of the figure represents the “heel contact”, which separates the gait cycle into two phases, the swing phase and the stance phase.

each subject was amplified 1000 times by an amplifier (embedding an instrumentation amplifier INA128 and two operational amplifiers OPA2604, Taxes Instrument Inc., USA). Two monitoring electrodes (M2223, 3M Inc., USA) were attached to the skin surface above the TA muscle belly; one reference electrode (M2223, 3M Inc., USA) was attached to the skin surface near the lateral epicondyle of femur. During walking, the pressure sensor under each subject’s heel recorded the heel pressure for the calculation of step frequency. For evaluation, a goniometer (S700, Measurand Inc., Canada) was used to measure the ankle joint angle. All signals listed above were acquired by a wireless ADC (WLS-9163, National Instrument Inc., USA). Since the ADC cannot be operated at two (or more) sampling rates, considering the consistency of the data, the sampling rate was uniformly set to 1000 Hz. All data were sent to a laptop with Wi-Fi (Compaq Presario V3765, CUP: Pentium D at 1.6 GHz; RAM: DDR-II 1GB; Hewlett Packed, HP Inc., USA).

Table 1 Subjects’ ages range from 21 to 27 (24.73 ± 1.74), 6 males, 5 females.

2.2.2. EMG data processing Raw EMG data were rectified and passed through a moving average window with the width of 100 points [16]. We denoted the averaged EMG as aEMG. aEMG from each subject was then normalized by the MAX-value normalization method [21]. In this method, the normalized EMG (NEMG) is obtained by being divided by maximum value of aEMG. The NEMG curve of each gait cycle was extracted and scaled to have the same length, by re-sampling (linear interpolation or down sampling), for the same step frequency setup (2000 ms for 60 s/min, 1500 ms for 80 s/min, 1200 ms for 100 s/min and 1000 ms for 120 s/min). By averaging the NEMG curves across all steps of all subjects for each step frequency, we get four average NEMG curves corresponding to the four step frequencies. The time lengths of the four curves were then scaled to 1 (100%). Although the four NEMG curves of the healthy subjects were only available at discrete frequencies with the interval of 20 s/min, the NEMG curves for any other frequency can be obtained by linear interpolation between two adjacent curves.

A common way of predicting step frequency is using the step frequency of the previous step as the predicted frequency of the upcoming one (P-1st which is P-Nth as presented later, when N = 1) [11,16]. To optimize the prediction on the step frequency of the upcoming step, we generalized the method by taking into account multiple preceding steps, and the number of steps is a parameter to be determined. We also compared the outcome with Auto Regression (AR) model, General Regression Neural Network (GRNN) and Kalman filter (KF). All the algorithms compared in this work are listed below (where, N = 1, 2, 3,...,10):

2.2.3. FES envelope Each NEMG intensity curve has two parts (Fig. 4), corresponding to the swing phase and the stance phase of a gait cycle respectively. At the end of the swing phase, the system can select a NEMG curve from the dataset according the duration of the swing phase. The second part (i.e. the stance phase) of this selected curve can then be used as the output envelope for the stance phase. For the swing phase, we have to predict the step frequency from the previous steps.

No.

Gender

Age

Height (cm)

Weight (kg)

S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11

Female Male Male Female Male Female Male Female Female Male Male

24 24 26 24 21 26 27 24 27 25 24

158 168 175 165 179 158 173 157 164 170 180

47 57 76 55 72 55 75 45 54 60 68

3. Step frequency prediction 3.1. Comparison of candidate algorithms

1) P-Nth. Use the step frequency of the previous Nth step as the predicted step frequency; 2) P-N. Use the average value of the step frequencies of the previous N steps as the predicted step frequency; 3) AR-N. Use the step frequencies of the previous N steps as the input of the AR model to predict the step frequency; 4) GRNN-N. Use the step frequencies of the previous N steps as the input of the GRNN model to predict the step frequency; 5) KF-N. Use the step frequencies of the previous N steps as the input of the Kalman filter to predict the step frequency. 3.2. Experiment setup To collect data for evaluating the algorithms, 11 young healthy volunteers (Table 1) were recruited after signing a consent form of this study. Each subject was asked to walk comfortably over ground at two modes, i.e. low-frequency mode and high-frequency mode.

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Table 2 Subject can walk slowly without assistance. No.

Sex

Age

Time after stroke

Lesion site

Affected side

BS-L

FMA-L

BBS

MWS (m/min)

PS01

Female

40

3 months

L cerebral cortex

Right

IV

17/34

43/56

13.8

BS-L, Brunnstrom stage for lower limb; FMA-L, Fugl-Meyer Assessment score for lower limb (full score is 34); BBS, Berg Balance Scale (full score is 56); MWS, 10-m maximum walking speed (m/min).

In the low-frequency mode, the subjects were instructed to walk with a self-recognized low speed according to their own walking experiences. In the high-frequency mode, the subjects were asked to walk in a hurry. The aforementioned pressure sensor was attached under the right heel to record the heel pressure. There were 10 trials for each step frequency. In each trial, the subjects were asked to walk 100 steps straight and then had a 2-min rest to prevent fatigue. The trials were carried out in a random order. The heel pressure was transmitted by aforementioned wireless ADC and was recorded in the aforementioned laptop. 3.3. Step data processing The real step frequency rSF (x) can be calculated using the duration of each gait cycle (denoted as D (x) ms), which can be obtained from the heel pressure. The formula to calculate rSF (x) is: rSF(x) =

For P-N, the predicted step frequency pSF (x) is: pSF(x) =

m=1

rSF(x − m) N

4. System evaluation on post-stroke subject with foot-drop One post-stroke inpatient with foot-drop was recruited for evaluating the system after signing a consent form of this study. The detailed information of the patient is listed in Table 2, where BS-L is Brunnstrom stage for lower limb; FMA-L is Fugl-Meyer Assessment score for lower limb (full score is 34); BBS is Berg Balance Scale (full score is 56); MWS is 10-m maximum walking speed (m/min). All assessment tests were carried out two days before the system test experiment.

4.1. Experiment setup

1000 × 60 × 2 D(x)

N

The correlation coefficient between the predicted step frequency pSF (x) and the real step frequency rSF (x) was calculated to measure the performance of the algorithms.

;

For P-Nth, the predicted step frequency pSF (x) is: pSF(x) = rSF(x − N); For AR-N, KF-N and GRNN-N, the predicted step frequency is the output of the model or filter. For the algorithms that need training (KF-N and GRNN-N), the data of each subject were divided into five parts with equal time length. We used four of them for training and the other part for testing. Therefore, there were five possible ways of separating the training data and the testing data. The final result was the average of the five.

In the experiment, the subject was asked to conduct two trials under each of the three following system setup conditions: • Condition 1 (C1): Without FES; • Condition 2 (C2): With a trapezoidal FES envelope; • Condition 3 (C3): With TA EMG modulated FES envelope and P-5 algorithm, which is the P-N algorithm when N = 5. In the first trial (T1), the subject was asked to walk at her maximum step frequency; in the second trial (T2), the subject was asked to walk at the most comfortable step frequency. The experiment was carried out in the same day. All trials were carried out in a random order. Between two successive trials the patient was asked to have a 10-min rest to avoid fatigue. The heel pressure and ankle joint angle of the affected side were sampled by the aforementioned wireless ADC, and was recorded in the aforementioned laptop.

Fig. 5. Ankle joint angle acquisition (a) and definition of the ankle joint angle (b). (a) The goniometer was attached tightly to a cuff. The block-I of the goniometer was right on the top of the anklebone (fibula) and the block-II of the goniometer was right beside the fifth metatarsal. (b) The ankle joint angle (denoted as ) at the standing posture is taken as 0◦ . The angle increases with dorsiflexion and decreases with plantarflexion.

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Fig. 7. Step frequency of prediction experiment. For each subject (S01–S11), the black bar shows the mean and standard deviation (SD) of the step frequencies in the low-frequency mode and the gray bar shows the information for the high-frequency mode.

Fig. 6. NEMG intensity dataset. The x-axis is the normalized time of a gait cycle. The y-axis is the step frequency, in which 60 s/min, 80 s/min, 100 s/min and 120 s/min are calculated based on the data we sampled and the other frequencies are calculated by linear interpolation. The z-axis is the NEMG amplitude percentage which is normalized by the MAX-value normalization method. The vertical gray surface in the middle of the figure is the “heel contact” time point for each step frequency.

the calibration data were collected while the subject was standing straightly, and then averaged to be the base measurement corresponding to the 0◦ . Any raw data recorded from the goniometer during the experiment would be subtracted by the base measurement to produce values of ankle joint angle. After calibration, the ankle joint angle data were processed with the following steps: (1) ankle joint angle data of each step were extracted; (2) the maxvalue of ankle joint angle in each step was collected; 3) the mean value and SD of the max-value of ankle joint angle in each step were calculated for the comparison of the 3 conditions (C1, C2 and C3).

4.2. Data processing For T1, the step frequencies of the 3 conditions (C1, C2 and C3) were compared; for T2, the maximum ankle joint angles of each step of the 3 conditions were compared. Furthermore, the maximum ankle joint angle (H1) of a healthy subject (S1, described in [16]) was shown together as a reference.

5. Statistical evaluation 4.2.1. Step frequency data processing The method of calculating the step frequency was the same as described in Section 3.3. All step frequencies here were real step frequency rSF (x). The mean value and SD of rSF (x) were calculated for the comparison under the 3 conditions (C1, C2 and C3).

The Student’s t-test method was used to evaluate the difference between the correlation coefficients of rSF (x) and pSF (x) from each two prediction methods. The differences in step frequency and maximum ankle joint angle among C1, C2 and C3 were analyzed by the ANOVA with the Bonferroni post hoc method. All the test data were processed by SPSS v16.0. The significance level was set at 0.05 in this work.

4.2.2. Ankle joint angle calibration and data processing The device for measuring the ankle joint angle is shown in Fig. 5a. A goniometer was attached to a thin soft cuff, which was wrapped tightly around the shank and foot by a stitched nylon tape (Velcrolike). To eliminate the error between different subjects, the cuff was carefully adjusted to make sure that the block-I of the goniometer was right on the top of the anklebone (fibula), and the block-II of the goniometer was right beside the fifth metatarsal as shown in Fig. 5a. The quantification of ankle joint angle, which requires calibration at the standing posture, is defined in Fig. 5b. In each experiment,

correlation coefficient

0.5

6. Results The stimulation envelope dataset is shown in Fig. 6. This dataset was installed in DSP flash memory for stimulation envelope selection. During walking, the system predicts the step frequency of the upcoming step, and then finds the corresponding envelope curve to modulate the stimulation using the PWM technique.

0.5

P-Nth

0 0.5

KF-N

0 0.5

GRNN-N AR-N

0 0.5

0 0

P-N 5 N

0 0

5 N

10 high low

10

Fig. 8. The correlation coefficients change with N. The solid lines are the data in the low-frequency mode; the dashed lines are the data in the high-frequency mode.

120

140

160

180

Steps 20

0.49 ± 0.18 0.42 ± 0.20 0.50 ± 0.18 0.43 ± 0.20

0.12 ± 0.10 0.10 ± 0.07 0.14 ± 0.12 0.09 ± 0.06

0.44 ± 0.18 0.34 ± 0.23

0.50 ± 0.17 0.40 ± 0.23

200

N=8

High step frequency

10 0

0.50 ± 0.18 0.44 ± 0.20

0.17 ± 0.13 0.09 ± 0.06

0.51 ± 0.18 0.43 ± 0.20 0.50 ± 0.18 0.42 ± 0.21 0.50 ± 0.19 0.41 ± 0.23 0.44 ± 0.20 0.33 ± 0.27 0.31 ± 0.22 0.20 ± 0.31 Low High KF-N

0.48 ± 0.19 0.38 ± 0.24

0.18 ± 0.14 0.10 ± 0.08 0.20 ± 0.14 0.13 ± 0.08 0.22 ± 0.14 0.14 ± 0.11 0.20 ± 0.17 0.11 ± 0.17 0.08 ± 0.17 0.08 ± 0.20 Low High GRNN-N

0.25 ± 0.16 0.15 ± 0.12

0.49 ± 0.18 0.35 ± 0.26 0.48 ± 0.19 0.36 ± 0.27 0.47 ± 0.19 0.34 ± 0.28 0.39 ± 0.22 0.26 ± 0.31 0.29 ± 0.22 0.17 ± 0.30 Low High AR-N

0.45 ± 0.20 0.31 ± 0.28

0.42 ± 0.21 0.29 ± 0.29 0.29 ± 0.22 0.17 ± 0.30 Low High P-N

0.41 ± 0.18 0.34 ± 0.20 0.29 ± 0.22 0.17 ± 0.30

N=3 N=2 N=1

Low High P-Nth

Correlation coefficient Mode Algorithm

Table 3 Performance of the prediction methods.

Fig. 10. Step frequency of T1 in 3 conditions. The mean and SD are 41.69 ± 1.50 for C1 (without FES), 44.17 ± 2.14 for C2 (trapezoidal FES), 46.00 ± 1.78 for C3 (TA EMG modulated FES).

N=4

For the step frequency prediction experiment, the step frequency information of the subjects is shown in Fig. 7. Based on the results (Table 3 and Fig. 8), we chose P-5 as the final prediction algorithm for its simplicity and relatively high prediction accuracy. Especially, significant improvement was observed in both frequency modes by the traditional prediction method (P-1st) (p < 0.001 for the low-frequency mode; p < 0.001 for the highfrequency mode). Some representative prediction trials of subject S01 is shown in Fig. 9. The only algorithm that appeared to be better than P-5 is KF-N when N > 4. However, Kalman filter is not well-suited for real-time applications, since it requires training. Fig. 10 shows the results from the post-stroke patient. When the patient walked at the maximum speed (T1), the step frequency of C3 is significantly higher than those of C1 and C2 (p < 0.001 and p = 0.05 respectively).

0.50 ± 0.18 0.38 ± 0.26

N=5

N=6

Fig. 9. (a) Part of the prediction results of subject S01 for algorithm P-5. The upper part is the result of the high-frequency mode and the lower part is the result of the low-frequency mode. The solid lines are pSF (x) and the dashed lines are rSF (x). (b) Error between pSF (x) and rSF (x) of subject S01 for algorithm P-5. The upper part is the error between pSF (x) and rSF (x) of the high step frequency mode and the lower part is the error between pSF (x) and rSF (x) of the low step frequency mode.

0.50 ± 0.18 0.39 ± 0.25

200

0.48 ± 0.19 0.36 ± 0.27

150

0.46 ± 0.20 0.34 ± 0.28

100

Steps

0.34 ± 0.19 0.28 ± 0.20

50

0.36 ± 0.17 0.28 ± 0.22

-20 0

0.48 ± 0.18 0.36 ± 0.24

N=7

0 -10

0.50 ± 0.17 0.40 ± 0.24

Low step frequency

10

0.33 ± 0.17 0.28 ± 0.20

-10

P-1st is the most commonly used prediction method. Its correlation coefficient values are 0.29 ± 0.22 for the low-frequency mode and 0.17 ± 0.30 for the high-frequency mode. P-5 is the method selected in the developed system. Its correlation coefficient values are 0.50 ± 0.18 for the low-frequency mode and 0.38 ± 0.26 for the high-frequency mode.

100

0.50 ± 0.17 0.43 ± 0.19

80

0.15 ± 0.14 0.10 ± 0.37

60

0.46 ± 0.18 0.34 ± 0.26

40

0.50 ± 0.17 0.40 ± 0.23

20

0.32 ± 0.17 0.28 ± 0.18

80 0

0.46 ± 0.17 0.37 ± 0.21

N=9

90

0.50 ± 0.17 0.39 ± 0.39

N = 10 estimated step frequency real step frequency (low)

100

0.29 ± 0.19 0.23 ± 0.23

110

0.37 ± 0.19 0.31 ± 0.21

Error between pSF(x) and rSF(x)

b

estimated step frequency real step frequency (high)

120

0.38 ± 0.19 0.31 ± 0.21

Step frequency/ s/min

a 130

201

0.31 ± 0.17 0.27 ± 0.18

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(0.015) is much smaller than the other two conditions (0.079 for both C1 and C2). This suggests that the gait of the subject under C3 is more stable than the two other conditions. From the comparison with the data of H1, we will discuss the three conditions separately as follows:

Fig. 11. Maximum ankle joint angle of T2 in 3 conditions. The mean and SD are 8.76 ± 0.69 for C1 (without FES), 14.13 ± 1.11 for C2 (trapezoidal FES), 15.70 ± 0.23 for C3 (TA EMG modulated FES), 24.55 ± 1.08 for H1 (data of healthy subject S1 described in [16]).

Fig. 12. Normalized SD value of ankle joint angle. The normalized SD values are 0.079 for C1 (without FES), 0.079 for C2 (trapezoidal FES), 0.015 for C3 (TA EMG modulated FES), 0.044 for H1 (data of healthy subject S1).

For the T2 trial, in which the patient walked at the most comfortable speed, the maximum values of ankle joint angle in 3 conditions and the data of a healthy subject (S1 in [16]) were shown in Fig. 11. Both of the improvements of C3 from C1 (p < 0.001) and C3 from C2 (p < 0.001) are significant. 7. Discussion We have improved foot-drop correcting FES systems by incorporating an effective step frequency prediction algorithm. The P-5 algorithm was selected based on the evaluation of several methods with various parameter settings. Step data from 11 healthy subjects were acquired for the testing. During step recording, no clock cue was provided to the subjects. They walked either in their most comfortable ways or in a hurry. Any external cue, such as metronome signal, may affect the gait data, since the subjects might adjust their step frequency when a gait cycle is not consistent with the cue. The goal of the study is to predict the step frequency of normal walking. Therefore, the gait data acquired under the control of a metronome is not suitable. Although our system was only tested on one post-stroke patient so far, the results are encouraging. As demonstrated by the SD bar in Fig. 11, the variation of C3 is 0.23; the variation values of the other two conditions are 0.69 for C1 and 1.11 for C2. To make the SD values comparable, we normalized the SD values by their corresponding mean values. Fig. 12 shows the normalized SD values from the three conditions, in comparison with the data (H1, mean value of maximum ankle joint angle is 24.55◦ , SD is 1.08) of a healthy subject (S1, described in [16]). We can see that the normalized SD value of C3

• C1 vs. H1. Under C1 condition, the post-stroke patient walked without FES assistance. The normalized SD value of C1 (0.079) is higher than H1 (0.044). This might be mainly caused by the loss of muscle control ability and spasticity of the muscle group during walking. • C2 vs. H1. Under C2 condition, the post-stroke patient was assisted by an FES system with a trapezoidal envelope. This type of envelope does not change the intensity of stimulation step by step. Thus, the fixed stimulation intensity of C2 might be the cause of the larger variation of ankle joint angle than H1. • C3 vs. H1. Under C3 condition, the post-stroke patient was assisted by the developed system, which can provide adjustable stimulation envelope with the intensity varying step by step in real time. The step frequency prediction algorithm (P-5) uses the averaged value of the previous five steps for the prediction of the upcoming one. This resulted in a smaller variation of ankle joint angle than H1. It probably indicated that such improvements are generally applicable to patients with foot-drop. Of course, more clinical tests are needed to make the conclusion more convincing; particularly, it is worthwhile to compare C2 and C3 conditions for more foot-drop patients in future work. From the result shown in Table 3, we found that for all algorithms and N values, the correlation coefficient value of the low-frequency mode is always larger than that of the highfrequency mode, as shown in Fig. 8. This implied that with the increase of the walking speed, the step frequency becomes more difficult to predict. It also suggests that the prediction is more reliable in gait-based identity recognition tasks when the target subject is walking in a lower speed. For all algorithms (Fig. 9), the correlation coefficient values increased significantly when N increased from 1 to 2. The correlation coefficient differences between two successive N values are also shown in Fig. 13. It shows that the difference between N = 2 and N = 1 (denoted as D21 ) is larger than those for D32 , D43 or others. Therefore, using P-1st to predict step frequency is not a good choice for an accurate prediction. Curves in Fig. 13 also demonstrate that although the prediction accuracies are varied for different step frequencies, the correlation coefficient differences of the two modes are almost the same. This suggests that the N value is insensitive to step frequency. The overall trend is that the prediction performance improves as the N value increases. However, when N > 5, the improvement slows down significantly or even stops in some cases, indicating that P-5 is an optimal choice of step frequency prediction in this study. When collecting the EMG data, the whole experiment was conducted in the same day for each subject without re-attaching the EMG electrodes. This eliminates the possibility of variations introduced by electrode replacement. Therefore, for simplification on the experiment procedure, we used the MAX-value method to normalize the EMG data, instead of dividing by the EMG amplitude during maximum voluntary contraction (MVC) [21]. There are some limitations in the current study. In our current system, there is no feedback during correction. Including some feedback, such as the ankle joint angle, can make the system even more adaptive. For example, a rapid onset of stimulation used in the current system might trigger spasticity in the muscle due to the fast muscle contraction. With the ankle joint angle as the feedback, the system would be able to detect induced spasticity right away and take actions such as resetting the stimulation intensity.

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Correlation coefficient difference

P-Nth

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6 N

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Fig. 13. Correlation coefficient differences. The solid lines are the data in the low-frequency mode; the dashed lines are the data in the high-frequency mode.

The system uses a pressure sensor to obtain step frequency information. The sensor is attached to the user’s heel. This causes two problems. First, the user has to take off a shoe to wear the system, which is usually difficult for a post-stroke patient. Second, either a wireless module or a long wire has to be used to connect the sensor and the stimulator, which is attached near the knee. Using a goniometer or an accelerometer instead of a pressure sensor can solve these problems [22] since it can be attached near the knee as well. Another limitation of the study is that there is only 1 output channel in the system and TA muscle is the only stimulated muscle. The evoked contraction of TA not only resulted in ankle dorsiflexion but also had the chance to cause inversion of foot. In a normal gait, ankle dorsiflexion is due to the co-contraction of TA, peroneus longus (PL) and peroneus brevis (PB), which stabilize the ankle joint during the gait. Using a multi-channel system to stimulate TA, PL, PB and other dorsiflexion-related muscles simultaneously might be a solution to the problem of foot inversion. For further improvement, the mechanism of the co-contraction of the dorsiflexion-related muscles should be investigated and a multi-channel system for a better balanced foot lift is worth developing. 8. Conclusion In this paper, we developed a real-time self-adaptive foot-drop correcting system. Compared with other correcting systems, the system in this study can evoke more natural gait patterns due to two new strategies: First, the output stimulation is modulated by envelops selected from real TA EMG dataset. Second, we improved the algorithm for the step frequency prediction, which significantly outperformed the traditional method (P-1st). Therefore, as long as the user walks with normal speeds, the system can reliably predict the frequency of each step and thus produce stimulation close to normal gait. The evaluation of the system on a post-stroke patient with foot-drop shows that the FES system with the new prediction algorithm of this study performed better in assisting gait of the post-stroke patient than systems using other commonly configured algorithms. Funding This work was supported by grants from: the Zhejiang Provincial Natural Science Foundation (2010C13026), Zhejiang provincial key science and technology program for international cooperation (2011C14005), the National Basic Research Program of China (2011CB504400), the National Natural Science Foundation of China (61031002), the National High Technology R&D Program of China

(No.2012AA011602) and the Fundamental Research Funds for the Central Universities.

Ethical approval All the subjects in this study signed a consent form of the experiments after being informed that the data acquired from them would be used for research purposes only. This study was approved by the Ethics Committee of Zhejiang University.

Conflict of interests There is no conflict of interest in this work.

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