Real-time gait subphase detection using an EMG signal graph matching (ESGM) algorithm based on EMG signals

Accepted Manuscript

Real-Time Gait Subphase Detection Using an EMG Signal Graph Matching (ESGM) Algorithm Based on EMG signals Jaehwan Ryu , Deok-Hwan Kim PII: DOI: Reference:

S0957-4174(17)30322-6 10.1016/j.eswa.2017.05.006 ESWA 11298

To appear in:

Expert Systems With Applications

Received date: Revised date: Accepted date:

8 November 2015 29 March 2017 4 May 2017

Please cite this article as: Jaehwan Ryu , Deok-Hwan Kim , Real-Time Gait Subphase Detection Using an EMG Signal Graph Matching (ESGM) Algorithm Based on EMG signals, Expert Systems With Applications (2017), doi: 10.1016/j.eswa.2017.05.006

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Highlights

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 The algorithm uses the curve matching between input EMG and reference EMG signals.  The proposed method reflects better timing characteristics than the time features.  The method provides real-time recognition of the gait subphase using EMG signals.

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Real-Time Gait Subphase Detection Using an EMG Signal Graph Matching (ESGM) Algorithm Based on EMG signals

Fig. 1. Examples of the generating meaningless feature values and the activation of EMG signal at the muscle of sartorius during gait cycle.

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Abstract — This study presents a gait subphase recognition method using an electromyogram (EMG) with a signal graph matching (ESGM) algorithm. Existing pattern recognition and machine learning using EMG signals has several innate problems in gait subphase detection. With respect to time domain features, their feature values may be analogous because two different gait steps may have similar muscle activation. In addition, the current gait subphase might not be recognized until the next gait subphase passes because the window size needed for feature extraction is larger than the period of the gait subphase. The ESGM algorithm is a new approach that compares reference EMG signals and input EMG signals according to time variance to solve these problems and considers variations of physiological muscle activity. We also determined all the elements of the ESGM algorithm using kinematic gait analysis and optimized the algorithm using experiments. Therefore, the ESGM algorithm reflects better timing characteristics of EMG signals than the time domain feature extraction algorithm. In addition, it can provide real-time and user-adaptive recognition of the gait subphase by using only EMG signals. Experimental results show that the average accuracy of the proposed method is 13% better than existing methods and the average detection latency of the proposed method was 5.5 times lower than existing methods.1

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Jaehwan Ryua, Deok-Hwan Kimb

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Key words — EMG Signal, Gait subphase detection, EMG signal graph matching, Gait assistant robot, Rehabilitation.

INTRODUCTION

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Electromyogram (EMG) signals are the electrical potential generated by muscle cells when these cells are electrically or neurologically activated (Popovi, 2013; Zhang & Huang, 2013). Recently, there have been a few studies on bionic legs, smart walkers, gait assistant robots, and gait cycle recognition using EMG signals (Hargrove et al., 2013; Kim, Cho, & Ryu, 2014; Chen et al., 2013). EMG signals are used to classify locomotion modes such as walking on level ground and walking up and down stairs during rehabilitation using a gait assistant robot (Hargrove et al., 2013; Zhang & Huang, 2013; Hargrove et al., 2013; Ivanenko et al., 2013; Hargrove et al., 2013; Huang et al., 2011). The EMG signal has been less popular than other pressure a Department of electronic engineering, Inha University, Incheon 402-751, Korea, e-mail: [email protected](J. Ryu). b Department of electronic engineering, Inha University, Incheon 402-751, Korea, * Corresponding Author, e-mail: [email protected], (D-H. Kim).

sensors and attitude and heading reference systems (AHRS) because of its inherent higher complexity in acquisition and post-processing (Taborri et al., 2016). However, EMG signals are useful for gait subphase detection since the muscle activity of the human leg occurs in a repeatable way during a gait cycle (Taborri et al., 2016; Moon et al., 2016; He et al., 2007; Sutherland et al., 2001; Neumann et al., 1950). In addition, using EMG signals to monitor muscle activity permits the detection of complex human motion, unlike the AHRS, pressure sensor, and foot switch. Therefore, the EMG signal has been used to determine a person’s intent and to detect complex lower-limb human motion in many studies (Hargrove et al., 2013; Zhang & Huang, 2013; Taborri et al., 2016; Hargrove et al., 2013; Ivanenko et al., 2013; Hargrove et al., 2013; Huang et al., 2011; Benedetti et al., 2012; Ivanenko et al., 2013). The gait cycle of a human is divided into stance and swing phases (Winter, 2009; Pappas et al., 2001; Ryu & Kim, 2014; Neumann & David, 2009; Skelly & Chizeck, 2001). In general, using EMG signals, the gait subphase is recognized as at least four steps–initial contact, mid stance, propulsion, and swing (Winter, 2009; Pappas et al., 2001; Ryu & Kim, 2014; Neumann & David, 2009; Skelly & Chizeck, 2001). In previous studies of gait assistant robots, authors usually classified the gait subphase by using physical sensors (Hargrove et al., 2013; Zhang & Huang, 2013; Hargrove et al., 2013; Ivanenko et al., 2013; Hargrove et al., 2013). However, gait subphase detection using physical sensors has drawbacks in that it causes unnatural gait behavior and does not sufficiently recognize a subject’s intentions; there may be a delay based on a sensor’s contact position with the foot and the sensing of environmental factors, such as the ground state, the gait habit, and the shoe state (Ivanenko et al., 2013; Lee & Lee, 2005).

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TABLE I INFORMATION OF VARIOUS EMG-SIGNAL-BASED HUMAN MOTION DETECTION SYSTEMS

Target

Feature domain

Kim, Cho, & Ryu (2014) Ryu & Kim (2014) Phinyomark, Limsakul & Phukpattaranon t (2009)

Locomotion mode

Frequency

Gait subphase

Time

Hand motion

Time and Frequency

Kim et al. (2011) Alkan & Gunay (2012) Wentink et al. (2014)

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Recognition method Classification (LDA) Classification (LDA) Classification (LDA)

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Hand motion

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Hand motion

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Gait initiation

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Classification (LDA, kNN, QDA) Classification (SVM)

Muscle activity

extraction algorithms. The ESGM algorithm consists of a training-data generation stage and a gait-subphase detection stage. In the training-data generation stage, we generated a reference EMG signal according to the gait subphase, the subject’s gait habit, and the muscle status. Subsequently, in the gait-subphase detection stage, the ESGM algorithm was used to recognize each gait subphase by matching the reference EMG signals with the input EMG signals.

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To solve this problem, only EMG-signal-based gait subphase recognition has been studied (Ryu & Kim, 2014). This approach extracts feature vectors for each gait subphase in the training stage and classifies the gait subphase using a feature vector during the recognition stage. However, gait subphase detection approaches using traditional classifiers and feature vectors based on EMG signals still have many problems. It is not easy to detect gait subphases in real time, and accuracy may be low. Fig. 1 shows an example of the meaningless feature values generated by the sartorius muscle during a gait cycle. When feature mean absolute values between initial contact and propulsion are similar, or feature values of mid stance are close to zero, these features are meaningless because the degree of class separation decreases as these kinds of features values are used (Fawcett et al, 2006). Real-time gait subphase detection requires some timing constraints, in that pattern recognition and machine learning should be done in less than 300 ms (Kim, Cho, & Ryu, 2014; Wachs et al., 2011; Englehart & Hudgins, 2003). If some subjects have a period of initial stance less than 144 ms as shown in Fig. 1, the method incurs a delay problem in obtaining results during the next gait subphase. Therefore, if the amplitudes of the EMG signals are low or similar, it is not easy to recognize a gait subphase by using EMG signals. Last, EMG signal patterns vary slightly depending on a subject’s gait habit, physiological boundaries, and muscle fatigue (Lee & Lee, 2005; Englehart & Hudgins, 2003; Farina, Gazzoni, & Merletti, 2003; Georgakis, Stergioulas, & Giakas, 2003; Martelli et al., 2017; Supuk et al., 2014). However, it has been observed that EMG signal acquisition from the same subject over a long period shows similar activity patterns with respect to all gait cycles (Artemiadis & Kyriakopoulos, 2007; Bovi & Ferarin, 2011). Therefore, it is necessary to develop a user-adaptive classifier that effectively reflects a subject’s gait characteristics. To solve these problems, we proposed a new gait subphase detection method using an EMG signal graph matching (ESGM) algorithm based on EMG signals. As shown in Fig. 1, the difference in muscle activity of onset/cessation times varied according to the type of gait subphase. For example, in the case of initial contact, the maximum activity occurs first when the toe touches the ground. After that, the muscle activity declines between the initial contact and the mid-stance state. However, in the case of propulsion, the maximum activity occurs at the end when the toe separates from the ground. Therefore, we proposed a new ESGM algorithm considering the period of maximum activity and activity changes in EMG signals corresponding to time variation for representing the features of gait subphase detection and locomotion mode recognition. It does not use feature extraction and classification as is but instead uses the calculated shape similarity values of EMG patterns and measures their matching accuracy according to time variations in real-time. Therefore, the ESGM algorithm can reflect EMG activity well and provide better real-time detection compared to existing feature

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BACKGROUND

Human motion classification using EMG signals is usually performed by pattern recognition and machine learning (Kim, Cho, & Ryu, 2014; Ryu & Kim, 2014; Phinyomark, Limsakul, & Phukpattaranont, 2009; Kim et al., 2011; Wentink et al., 2014; Alkan & Gunay, 2012). Table 1 shows the information of various EMG-signal-based human motion detection systems. An EMG signal based on pattern recognition and machine learning extracts feature vectors in accordance with human motion by using various feature extraction algorithms. Subsequently, in the training and motion recognition phases, a classifier is learned using feature vectors of a training data set and then used to detect human motion using feature vectors of the test data set. Most pattern recognition and machine learning approaches using EMG signals focus on the development of new feature extraction algorithms and performance enhancement of motion detection. Phinyomark et al. (Phinyomark, Limsakul, & Phukpattaranont, 2009) proposed hand-motion recognition using 16 feature extraction algorithms based on time domain, frequency domain, and linear discriminant analysis (LDA). Kim et al. (2011) compared the classification performance of LDA, quadratic discriminant analysis (QDA), and a k-nearest neighbor (kNN) classifier for EMG signal-based pattern recognition and machine learning. Englehart and Hudgins (2003) presented feature vectors using a histogram and autoregressive coefficients of EMG signals and a dimensionreduction algorithm using a self-organizing feature map for

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Fig. 3. Block diagram of generation of reference EMG signals.

Fig. 2. Electrode mounting position.

DATA ACQUISITION

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feature projection (Phinyomark, Limsakul, & Phukpattaranont, 2009). These approaches showed high accuracy in classification of hand motion, gestures, and locomotion mode. However, they were difficult to use for gait subphase detection. These approaches take at least 250 to 300 ms to collect EMG signals for feature extraction. However, the period of the average human gait is about 1300 ms, and that of a minimum gait subphase is about 130 ms. Therefore, because of the delayed EMG signal acquisition, it is difficult to detect a gait subphase at the proper time. If the period of the initial stance was 130 ms, the period of mid stance was 260 ms, and the data acquisition time for feature extraction was 250 ms, at worst, the detection of the initial stance phase would be deferred until mid stance.

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Twenty subjects aged 21–27 participated in this experiment. The average age of the subjects was 24. We used a commercial EMG device (MP150) to amplify the EMG signals and six BioPac BN-EMG2 (12 channels). For data acquisition, nine EMG channels were attached to the sartorius, rectus femoris, vastus medialis, vastus laterilis, tibialis anterior, semitendinosus, biceps femoris, peroneus longus, and gastrocnemius, and two pressure sensors and an AHRS sensor were used to detect the heel and toe status. The subjects had no history of lower extremity or other musculoskeletal disorders. Subjects provided their written informed consent prior to the experimental procedures. The experimental procedures were in accordance with the Declaration of Helsinki and approved by the Inha University Institutional Review Board (approval: 150603-1A). The training and test data were collected as each

subject performed 250 steps of a level walk. Fig. 2 shows the mounting positions of the nine sEMG (surface EMG) electrodes (Zygote Media Group, 2015). 4.

PROPOSED METHOD

A new ESGM algorithm was proposed for gait subphase detection using only EMG signals. The proposed ESGM algorithm consisted of a reference EMG signal-generation stage and a gait-subphase detection stage. In the training-data generation stage, the algorithm generated reference EMG signals corresponding to each gait subphase. The reference EMG signals form an activity curve representing the muscle activity produced by a gait subphase. In the gait-subphase detection stage, it calculated a matching ratio by comparing the input EMG signals with the corresponding reference EMG signals. The ESGM algorithm provided high recognition accuracy and real-time processing because it successfully reflected the muscle activity according to time change; it also provided fast matching of the gait subphase using EMG signals. 4.1. Training data generation First, in the training-data generation stage, we collected EMG signals during several gait cycles. The reference EMG signals were generated by applying the absolute value function and normalization with respect to amplitude values of the EMG signals at each subphase and then averaging the transformed values corresponding to each gait cycle. Fig. 3 shows a block diagram of the generation of reference EMG signals. In Fig. 3 (a), EMG signals were collected and labeled using pressure sensors and an attitude and heading reference system (AHRS) sensor. Two pressure sensors and one AHRS sensor

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normalization was calculated using Eq. (1): Cj = (Sj / max(S)) × 100

(1)

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where Sj is the value of the j th absolute signal, j = 1, 2, 3, … n (n is the length of a signal), max(S) is the maximum value of the absolute signals of the gait subphase, and Cj is the ratio of the maximum value and Sj. As shown in Fig. 4 (a), the amplitude values of EMG signals at certain times were different, even though they were collected from the same subject during the same gait subphase. The relative curve obtained using Eq. (1) solves this problem according to the gait cycle. Next, the relative curves of the EMG signals were converted to A-curves by performing quantification. Quantification involves performing interpolation on the values of the relative curves between the front and rear crests using values of the front and rear crests. The A-curve is capable of detecting the gait subphase precisely because it represents the activity of a muscle according to time. In Fig. 3 (d), we generated reference EMG signals. The reference EMG signals calculated the average and normalization of the EMG signal A-curves during each gait subphase. The reference EMG signals were generated in accordance with each subject. Fig. 4 (c) shows the reference EMG signal A-curves at the initial contact subphase of the reference EMG signals of Subject 2.

Fig. 4. (a) Curves of EMG signals of subject 2 at initial contact, (b) curves of the corresponding absolute EMG signals of subject 2, and (c) curve of the corresponding reference EMG signals of subject 2.

were used to check when the heel and toe touched the ground. Fig. 4 (a) shows the curves of EMG signals at initial contact over 100 gait cycles. In Fig. 3 (b), the amplitude values of EMG signals were converted into absolute values to represent the amplitude variation of the EMG signals. Fig. 4 (b) shows the curves of absolute EMG signals of Subject 2 at initial contact over 100 gait cycles. In Fig. 3 (c), we normalized the absolute values and fit them to an absolute curve (A-curve) to resolve the amplitude bias problem. The relative curve for

4.2. Gait-subphase detection using ESGM We assumed that input EMG signals accumulated between the initial time of the gait cycle and the current time. Therefore, the size of the input EMG signals increased as the subject walked. The ESGM algorithm consisted of four steps in gait-subphase detection. In the first step, it adjusted the size of the input EMG signals to match the size of the reference EMG signals at the initial contact subphase. For example, we assumed that the length of the input EMG signals was 200 ms or 300 ms, and the length of the reference EMG signals was 250 ms at the initial contact step. We needed to adjust the size of the input EMG signals to interpolate them with the reference EMG signals. The input signal was resized from 200 ms or 300 ms to 250 ms using linear interpolation. The EMG signals acquired from the same subject may exhibit a similar pattern after conversion based on relative time even though the periods were different. Subsequently, in the second and third steps, we transformed the adjusted input EMG signals into Acurves by using the absolute value function and normalization to match them with the reference EMG signals. This was performed as in the training-data generation stage. Finally, in the fourth step, we calculated the matching ratio between the A-curve of the reference EMG signals and the A-curve of the input EMG signals. The matching ratio was calculated using Eq. (2):

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Fig. 6. Changes in the matching ratio according to time variation at initial contact.

Fig. 5. A-curves of reference EMG signals (solid line) and input EMG signals (dotted line) of Subject 2 at initial contact.

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1 n ∑b n i =1 i 1, if 0.7 ≤ Ai / Ri ≤1.3 0, otherwise

Matching ratio (%) = bi = {

(2)

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where Ai is the value of the ith A-curve of the input EMG signals, Ri is the ith value of the reference EMG signals at any gait subphase, n is the length of the corresponding curve of the reference EMG signals, and bi is the difference between the input A-curve and the ith value of the corresponding reference EMG signals. If the difference between the input Acurve and the ith value was between 0.7 and 1.3, the value of bi was set to 1; otherwise, we set the value of bi to zero. Physiological boundaries must be considered to recognize the gait subphase using the EMG signal (Martelli et al., 2017; Supuk et al., 2014). Physiological boundaries vary from person to person, but the higher the muscle activity, the wider the boundaries. Eq. (2) was designed to allow sufficient physiological boundaries and to reduce computational complexity. For example, we assumed that R1 was 0.2, R2 was 0.4, and R3 was 0.7. The threshold range of R1 was 0.14 to 0.26, the threshold range of R2 was 0.28 to 0.52, and the threshold range of R3 was 0.49 to 0.91. In addition, 0.7 to 1.3 of the threshold range was determined from pre-experiment in Section 5.3. Fig. 5 shows reference EMG signals of Subject 2 and the corresponding A-curves of input EMG signals of Subject 2 at the initial-contact subphase. This process was repeated at regular intervals; that is, we used a regular 1-ms interval that was the same as the sampling rate of the analog-to-digital converter in the EMG electrode. We considered the highest point of the matching ratio as the end of initial contact. When the matching ratio did not change within 10% of the current period of reference EMG signal after finding the maximum matching ratio, we determined that point in time to be the boundary of the gait subphase. We

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called that the switching time. The 10% of switching time was determined from pre-experimentation in Section 5.3. A discussion of the switching time is given in Section 5.3. In addition, the calculation of the matching ratio was suspended until a time less than 50 ms from the end point of a recognized previous gait subphase. Since the minimum time of the human gait cycle is at least longer than 550 ms, the gait subphase requires a minimum of 50 ms or more; therefore, proceeding with the above process was an obvious waste of resources. Fig. 6 shows the example of changes in the matching ratio according to time variation at initial contact. Last, the gait initiation during initial contact was recognized using the vastus lateralis and vastus medialis muscles. Wentink et al. recognized gait initiation of initial contact by using muscle activity (Wentink et al., 2014). The vastus medialis and vastus lateralis muscles are activated when the heel touches the ground. It is possible to recognize the starting point of the initial contact. The proposed algorithm was able to determine the gait initiation of the initial contact subphase and thus greatly reduce the period error. 5.

EXPERIMENT AND DISCUSSION

5.1. Experimental Environment We implemented the ESGM algorithm in Matlab 2013. The existing method was implemented using an sEMG signal pattern recognition algorithm applied to human motion recognition. To compare these results with existing methods, an EMG feature extraction and pattern recognition algorithm using LDA (Huang et al., 2011; Phinyomark, Limsakul, & Phukpattaranont, 2009), QDA (Kim et al., 2011), and kNN (Phinyomark, Limsakul, & Phukpattaranont, 2009) were implemented. In addition, 12 feature extraction algorithms (variance, Willison amplitude, zero crossing, root mean square values, slope sign changes, mean absolute values, integrated EMG, modified mean absolute value 1, modified mean absolute value 2, mean absolute value slope, waveform length, and simple square integrals) were used in the existing methods (Huang et al., 2011; Phinyomark, Limsakul, & Phukpattaranont, 2009; Kim et al., 2011).

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Fig. 9. Detection accuracy of gait subphase according to switching time.

sartorius muscle of five subjects. We can see that the muscle activity of each gait subphase varied depending on the subject.

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The target muscles with the highest matching ratio according to gait subphase were consistent with the muscles demonstrated in previous studies (Moon et al., 2016; He et al., 2007; Sutherland et al., 2001; Neumann et al., 1950). However, we can see that different muscles are required to produce clear reference EMG signals according to gait subphase and subjects. For example, Subjects 8 and 19 were confirmed to show sEMG signal activity corresponding to a normal gait cycle from initial contact to the swing stage (Moon et al., 2016; He H et al., 2007; Sutherland et al., 2001; Neumann et al., 1950; Pourmoghaddam et al., 2013; Huang et al., 2011), whereas sEMG signals of the other subjects showed similar patterns only during the stance stage. This was because the sEMG signal patterns of subjects measured at the lower-limb muscles varied depending on the subject’s gait habit, skin fat, and skin type. The EMG signals of each subject shown in Fig. 4 (a) show similar patterns. Therefore, we can see that the reference EMG signal and classifier for gait subphase detection should be generated and learned from the walking data of the same subject because various EMG signal patterns appear depending on a subject’s gait habit. We will use these target muscles for further experimentation. In addition, we used representative activity muscles according to gait subphase in the training-data generation stage to simplify the process.

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Fig. 7. Target muscles with the highest accuracy.

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Fig. 8. Activity of sEMG signals extracted from the sartorius muscle.

In total, 250 gait-cycle data were used in this experiment. One hundred gait-cycle data were used for the training-data generation stage, and the other 150 gait-cycle data were used for gait-subphase detection. 5.2. Effect of subject characteristics We evaluated the graph matching ratio of the nine target muscles according to gait subphase and subject. Fig. 7 shows the target muscle with the highest matching ratio chosen from the nine muscles with respect to each subject and gait subphase. Fig. 8 shows the activity of sEMG signals extracted from the

5.3. Effect of switching time In the pre-experiment in Section 5.3 and Section 5.4, physical sensors were used to find the optimal switching time and threshold range. We evaluated the accuracies of the proposed method according to the switching time. Fig. 9 shows the detection accuracy of the proposed method in the case where the switching time varies from 1 to 50% of the current-gait subphase. For example, we assumed that the period of the current-gait subphase was 200 ms. Ten percent of switching time is 20 ms, and 20% of switching time is 40 ms. The average accuracy of the proposed method when switching time was set to 10% or more over the length of the reference signal was 97%, whereas when switching time was

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Fig. 10. Detection accuracy of gait subphase according to threshold range.

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set to 9% over the length of the reference EMG signal, the accuracy was less than 93.2%. According to the analysis of variance (ANOVA) result, the variation in accuracy of switching time was significant (F = 40.8, P < 0.01). The accuracy of the proposed method remained almost unchanged when a switching time equal to or greater than 10% was used (ANOVA, F = 1.45, P > 0.01). We found this phenomenon occurred because of the relationship between the length of the gait subphase and muscle activity. The muscle activity of each subject had a length difference of 5 to 10%. It is possible that the graph matching process ends while the muscle activity is being reproduced corresponding to the gait subphase when the switching time is less than 5%. However, the average accuracy of the proposed method was 97.9% when the switching time was set to more than 10% because the muscle activity length was too short to reproduce the reference EMG signals. Therefore, we chose a switching time of 10%.

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5.4. Effect of threshold range We evaluated the accuracies of the proposed method according to the threshold range. Fig. 10 shows the detection accuracy of the proposed method in the case where the threshold range used varied from ±1% (0.99–1.01) to ±50% (0.5–1.5). The average accuracy of the proposed method using ±30% (0.7–1.3) of the threshold range was 97.8%, whereas the accuracy using ±25% (0.75–1.25) and ±35% (0.65–1.35) of the threshold value were 91.8% and 90.6%, respectively.

Fig. 11. (a) Sensitivity, and (b) specificity of proposed and existing methods.

According to the ANOVA result, the variation in accuracy over threshold range was significant (F = 72.31, P < 0.01). The sensitivity decreased as the threshold range increased. Experimental results show that if the threshold range was less than 25%, the sensitivity was high and the true positive rate decreased. However, if the threshold range was more than 35%, the sensitivity was low and the false positive rate increased. Therefore, we chose the threshold range of the ESGM algorithm to be ±30% (0.7–1.3). 5.5. Gait-subphase detection The experiments of Section 5.5 and Section 5.6 used realtime input data of 1-ms period for comparison with existing methods. We evaluated the sensitivity, specificity, and accuracy values of the proposed and existing methods with respect to gait subphase. Fig. 11 shows the sensitivity and specificity of the proposed and existing methods in terms of gait.

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Fig. 13. Latency in gait detection of (a) proposed method, (b) existing method_LDA, (c) existing method_QDA, (d) existing method_kNN, and (e) average latency.

Fig. 12. Average accuracy of proposed and existing methods.

The results show that the sensitivity and specificity of the proposed method were higher than those of existing methods

in all cases. The average sensitivity of the proposed method was 85%, whereas those of existing methods using LDA, QDA, and kNN were 67%, 67%, and 68%, respectively. The average specificity of the proposed method was 93%, whereas those of existing methods using LDA, QDA, and kNN were 84%, 84%, and 85%. We can see that the proposed method can separate true datasets and false datasets better than existing methods because the proposed method reflected the gradient of the amplitudes of the EMG signals according to time variation and monitored the active muscles of each gait subphase. The sensitivity, specificity, and precision of the proposed method were higher than those of existing methods in all gait subphases. We can see that the proposed method detected false alarms better than existing methods because the ESGM algorithm compared the inclination of the amplitude of the EMG signal according to time variation. In addition, the reference EMG signal was generated by monitoring the muscles with the most activity per subject. Last, the ESGM

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5.6. Gait-detection latency time

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Second, we tested 20 subjects to evaluate the latency in gait detection for the proposed method and existing methods. Fig. 13 shows the latency for gait detection in terms of four gait subphases. The results show that the average latency of the proposed method for gait subphase detection was 37.6 ms at initial contact, 38.4 ms in mid stance, 28.6 ms in propulsion, and 45.4 ms in swing, whereas that of existing methods using LDA, QDA, and kNN were at initial contact 220.9 ms, 221.6 ms, and 226.8 ms, respectively; at mid stance 88.5 ms, 89.8 ms, and 94.7 ms, respectively; at propulsion 142.3 ms, 143.1 ms, and 151.7 ms, respectively; and at swing 336.4 ms, 366.7 ms, and 367.3 ms, respectively. In addition, the maximum latency for gait subphase detection with the ESGM algorithm was 37.6 ms at initial contact, 38.4 ms in mid stance, 28.6 ms in propulsion, and 45.4 ms in swing. Therefore, we confirmed that gait subphase detection with the ESGM algorithm was performed in real time. The results show that the detection latency time of the proposed method is 5.5 times shorter than that of existing methods because existing methods incur longer detection latency because they use multi-class classifiers and feature generation using window sizes. The graph matching technique using reference EMG signals makes our GPES algorithm suitable for use in real-time detection. 6.

to the time-domain feature. It also provides fast matching of the gait subphase using EMG signals. From this study, we confirmed that it is possible to classify detailed stages of the gait subphase by using only EMG signals. In a future study, we will develop a multi-EMG-signal-based graph matching algorithm and graph matching technique to improve detection accuracy. Finally, the proposed method can be extended to different tasks such as human motion recognition and human and robot system activity analysis using EMG signals.

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ACKNOWLEDGEMENTS This work was supported by the Industrial Technology innovation Program funded by the Ministry of Trade, industry & Energy (MI, Korea) [10073154, Development of humanfriendly human-robot interaction technologies using human internal emotional states] and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(20100020163) and in part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2015R1D1A1A01061112).

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algorithm reduced false-alarm detection performance by minimizing the recording of muscle activity that produced unclear EMG signals. Next, we evaluated the accuracy of the proposed and existing methods with respect to gait subphase. Fig. 12 shows the accuracy of the proposed and existing methods in terms of gait. The results show that the accuracy of the proposed method was higher than those of the existing methods in all cases. The average accuracy of the proposed method was 89%, while the accuracy of existing methods using LDA, QDA, kNN were 76%, 75%, and 76%, respectively. The oneway analysis of variance results indicated that the performance of the proposed method over four gait subphases was significant (F = 23.42, P < 0.05). Therefore, we confirmed that the ESGM algorithm achieved higher performance compared to existing methods because the proposed method reflected the gait characteristics, muscle activity, and physiological boundaries of an individual subject. Therefore, we verified that the proposed method can be used to accurately detect gait subphase in a real environment.

CONCLUSION

In this study, we proposed a method of gait subphase detection using an EMG with an ESGM algorithm. The contributions of the proposed method are as follows. It does not apply pattern recognition using traditional feature extraction and classification, but instead, uses the amplitude graph of the EMG signals and cure matching. Therefore, it can better reflect timing characteristics of EMG signals compared

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