Application of Mean and Median Frequency Methods for Identification of Human Joint Angles Using EMG Signal Sirinee Thongpanja1,*, Angkoon Phinyomark2, Chusak Limsakul1, and Pornchai Phukpattaranont1 1
Department of Electrical Engineering, Prince of Songkla University, Thailand
[email protected],
[email protected],
[email protected] 2
Faculty of Kinesiology, University of Calgary, AB, Canada
[email protected]
Abstract. The analysis of surface electromyography (EMG) signals is generally based on three major issues, i.e., the detection of muscle force, muscle geometry, and muscle fatigue. Recently, there are no any techniques that can analyse all the issues. Mean frequency (MNF) and median frequency (MDF) have been successfully applied to be used as muscle force and fatigue indices in previous studies. However, there is the lack of consensus upon the effect of muscle geometry on the basis of varying joint angles. In this paper, the modification of MNF and MDF using a min-max normalization technique was proposed to provide a consistent relationship between feature value and joint angle across subjects. The results show that MNF and MDF extracted from normalized EMG showed a stronger linear relationship with elbow joint angle compared to traditional MNF and MDF methods. Modified MNF and MDF features increased with increasing elbow angle during isometric flexion. As a result of the proposed technique, modified MNF and MDF features could be used as a universal index to determine all the issues involving muscle fatigue, muscle force, and also muscle geometry. Keywords: Feature extraction · Frequency analysis · Muscle fatigue · Spectral analysis · Surface electromyography signal
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Introduction
Surface electromyography (EMG) signal is one of the useful electrophysiological signals, which is applied in many medical and engineering applications. The analysis of EMG signals is generally based on three issues, i.e., the detection of muscle force, muscle geometry, and muscle fatigue [1]. Many classical and advanced feature extraction techniques based on time and/or frequency domain have been used, such as root mean square, fractal dimension, wavelet transform as well as mean and median frequencies (MNF and MDF) [2]. Unfortunately, there are no any techniques that can be used to analyse all the issues as a universal index. Moreover, when one of the three issues was analysed, other issues could affect the results of the analysis, for example, the assessment of muscle fatigue during dynamic contraction [1].
Springer-Verlag Berlin Heidelberg 2015 K.J. Kim (ed.), Information Science and Applications, Lecture Notes in Electrical Engineering 339, DOI 10.1007/978-3-662-46578-3_81
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Generally, MNF and MDF are held as the gold standard for the assessment of muscle fatigue [3]. During the fatigue of a muscle, the EMG power spectrum is shifted toward lower frequencies, so MNF/MDF decreases when muscle fatigues. However, daily activities involve dynamic contraction where the changes of muscle force and/or geometry produce non-stationary EMG signal. The effects of muscle force and geometry are also still not clear [1], [4]. More details about contradictory findings of muscle force and geometry effects on MNF and MDF can be found in [5]. To develop a technique that can detect muscle force and muscle fatigue during dynamic contraction, in our previous studies [6,7,8] the modification of MNF and MDF is proposed based on the basis of consecutive fast Fourier transforms (FFTs). As a result of this proposed technique, the modified MNF and MDF features can be used to identify both muscle fatigue and muscle force. Muscle fatigue can be detected by a decrease of MNF/MDF during one dynamic contraction [3], and by a slope of the regression line that fits maximum MNF/MDF during several cyclic dynamic contractions [9]. For the muscle force detection, a mean value of the effective range of the consecutive MNFs/MDFs can provide a strong linear relationship between feature value and muscle contraction level for upper-limb muscles, such as biceps brachii and flexor pollicis longus [8,9,10]. When muscle contraction levels increased, the modified MNF and MDF features decrease [6]. This relationship is consistent across all the studied subjects but it is not consistent across the subjects using traditional MNF and MDF methods. Generally, the effect of muscle geometry including electrode configuration, fibre diameter, and subcutaneous tissue thickness, has been evaluated by the resulting from changes in joint angles [1], [11]. Two relationships have been found for the joint angle effect on MNF/MDF [5]. First, MNF and MDF are unaffected by changes in joint angle [12]. Gerdle et al. [13] reported that no significant change in the EMG power spectrum measured from biceps brachii under constant load while joint angle varied. Second, MNF/MDF increases as a joint angle increased (or a muscle length decreased) [1], [14,15]. This relationship has been found in most of the previous studies. Although the second case has been found frequently in literature compared to the first case, in our previous study [5] both cases are found (i.e., subject-dependent). In this paper, to develop the universal MNF and MDF indices which can analyse all the issues this problem was revisited by proposing the modification of MNF and MDF using a min-max normalization technique for the characterization of EMG signals at different angles of elbow joint.
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Materials and Methods
2.1
EMG data collection
Surface EMG signals were acquired at five different isometric contraction levels: 1, 2, 3, 4, and 5 kg, and five different elbow joint angles: 30º, 60º, 90º, 120º, and 150º of flexion. Nineteen normal subjects, Subj:#1-#19 (10 males and 9 females, 21.10±0.81 years) participated in this study. The subject was asked to lift the required load at the
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specific elbow joint angle. EMG data were recorded for 5 s in each trial after the subject’s elbow joint angle was stable. Five trials of each of the twenty-five (5 loads × 5 angles) possible combinations were performed per day for four separate days. A sequence of the 25 combinations was randomized. EMG data were collected from the biceps brachii muscle using bipolar Ag/AgCl electrodes (H124SG, Kendall ARBO) with a common ground reference on wrist. The signals were amplified at a gain of 19x with a bandwidth of 20-500 Hz and were sampled at a rate of 1024 Hz with an analog-to-digital resolution of 24 bits using an EMG measuring system (Mobi-6b, TMS International BV, Netherlands). 2.2
Normalization Technique
There are many techniques that can use to normalize EMG signals, such as the maximal voluntary isometric contraction method, the peak and mean activation-levels methods, the peak-to-peak amplitude of the maximum M-wave method, and the zscore normalization method [16]. Usually, these techniques have been used to reduce the variation of EMG signals between subjects, between days within a subject, and/or within a day in a subject if the electrode positions are shifted [16]. However, in this study the normalization technique is used based on the observation of asymmetric and symmetric between the maximum positive and the minimum negative EMG amplitudes at different angles of elbow joint in time domain. This can be confirmed by a skewness value, which is a measure of the asymmetry of the probability distribution of data. The min-max normalization technique is used which performs a linear transformation on raw EMG data by setting the highest (positive) value to 1 and the lowest (negative) value to -1. The interval maximum and minimum of raw EMG data x is transformed into a new interval, which can be expressed as
y
( x xmin )( ymax ymin ) ymin , xmax xmin
(1)
where xmax and xmin represent the maximum and the minimum values of x respectively, ymax and ymin are the new range in which the normalized data will fall, and y is the normalized EMG data within the interval ymin to ymax. In this paper, ymin and ymax are -1 and 1, respectively. 2.3
Mean Frequency (MNF) and Median Frequency (MDF)
MNF is an average frequency which is defined as a sum of the product of EMG power spectrum and frequency divided by a total sum of power spectrum. It can be expressed as M
MNF
M
¦ f P ¦P , j
j 1
j
j
j 1
(2)
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where Pj is the EMG power spectrum at a frequency bin j, fj is the frequency of the spectrum at a frequency bin j, and M is the total number of frequency bins. MDF is a frequency at which the EMG power spectrum is divided into two regions with an equal integrated power. It can be expressed as M
¦P
j
j 1
M
¦
j MDF
Pj
1 M ¦ Pj . 2j1
(3)
The proposed MNF and MDF features are extracted from the normalized EMG data y instead of the raw EMG data x. After the modified MNF and MDF values were calculated, the relationship between MNF/MDF and elbow joint angle was evaluated for all the different loads. Correlation analysis was also used to measure the strength of linear association between feature value and elbow joint angle.
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Experimental Results and Discussion
First, the relationship between traditional MNF/MDF feature and elbow joint angle was re-investigated. Three different relationships were found in our experiments for the effect of elbow joint angle on MNF and MDF: (1) MNF/MDF is unaffected by changes in elbow joint angle, as shown in Figs. 1(a) and 1(b). This relationship was found for 8 subjects. (2) MNF/MDF increases as elbow flexion angle increases or muscle length decreases, as shown in Figs. 1(c) and 1(d). This relationship was found for 10 subjects. (3) MNF/MDF decreases as elbow flexion angle increases or muscle length decreases, as shown in Figs. 1(e) and 1(f), for a subject. There are several possible reasons for the lack of consensus upon the effect of muscle geometry found in the previous and current studies. Differences in the experimental conditions between studies may be the main reasons for the conflicting results. For instance, Doheny et al. [19] suggested that the difference of muscle type and electrode location over the muscle is one of the reasons based on the experiments on three muscles: biceps brachii, triceps brachii, and brachioradialis. However, the three conflicting cases were found in the present investigation on the same muscle, i.e., the biceps brachii. Cechetto et al. [1] suggested that an inter-electrode distance is another possible reason for the conflicting results. Several inter-electrode distances have been used in the literature such as 10, 30, and 40 mm. In the present investigation, an interelectrode distance of 20 mm was used and the three conflicting cases were still found. Further, it is important to note that due to a fixed load used in the experiments in literature at the different angles, the change of MNF and MDF is not due to the change of only muscle length but also due to the change of muscle contraction levels. Second, to reduce the variation of intrinsic and extrinsic factors, raw EMG data was normalized in a comparison of activity. Figures 2(a) and 2(b) show the raw EMG data in time domain at a constant load with two different elbow angles, i.e., 30º and 150º of flexion. The results show that at a small elbow flexion angle, the distribution of the positive and negative EMG amplitudes was asymmetry. On the other hand, the distribution of the positive and negative EMG amplitude was symmetry at a large elbow flexion angle. Their power spectrums are shown in Figs. 2(c) and 2(d).
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Fig. 1. (a, c, e) MNF and (b, d, f) MDF of raw EMG data at a constant load (4 kg) as a function of elbow angles (30º-150º of flexion): (a-b) the first case from Subj: #11, (c-d) the second case from Subj: #13, (e-f) the third case from Subj: #18. The error bars shown are given by the standard deviation of the mean value over 20 trials.
Fig. 2. Raw EMG data in time domain at (a) 30º and (b) 150º of elbow flexion and their power spectrum at (c) 30º and (d) 150º of elbow flexion at a constant load of 3 kg from Subj: #11. The maximum peak (positive value) is marked with a circle and the minimum peak (negative value) is marked with a cross.
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If raw EMG amplitude was normalized by converting the maximum peak value (ż) to 1 and the minimum peak value (×) to -1 for the asymmetric signal, the EMG baseline (dash line) was shifted away from the true zero line (solid line), as shown in Fig. 3(a). On the other hand, if raw EMG amplitude had a symmetry property, the EMG baseline was still been at the true zero line, as shown in Fig. 3(b). Therefore, the power spectrum of normalized EMG data measured at lower elbow flexion angles increased at low frequencies (0-15 Hz) compared to the power spectrum of normalized EMG data measured at greater elbow flexion angles and also raw EMG data, as shown in Figs. 3(c) and 3(d). The EMG power spectrums at low frequencies of normalized EMG data at 30º and 150º of flexion are zoomed and shown in Fig. 4(a). This finding can be confirmed by skewness values of raw EMG data at different elbow flexion angles, as shown in Fig. 4(b). The skewness value was high (an asymmetric distribution) at lower elbow flexion angles and decreased toward zero (a symmetric distribution) at higher elbow flexion angles. This result also suggested that the skewness value can identify different joint angles using EMG signal. Subsequently, MNF/MDF extracted from normalized EMG was shifted toward lower frequencies.
Fig. 3. Normalized EMG data in time domain at (a) 30º and (b) 150º of elbow flexion and their power spectrum at (c) 30º and (d) 150º of elbow flexion from Subj: #11. The horizontal solid line is a true zero line and the horizontal dash line is a mean EMG amplitude value.
Fig. 4. (a) EMG power spectrum at low frequencies (0-32 Hz) of normalized EMG data at (dash line) 30º and (solid line) 150º of elbow flexion. (b) Skewness values of raw EMG data at a constant load (4 kg) as a function of elbow angles (30º-150º of flexion) from Subj: #18.
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A certain relationship was found across all the studied subjects and loads (i.e., subject- and muscle force-independent), as shown in Fig. 5. Specifically, modified MNF and MDF features increased with increasing elbow flexion angle or decreasing muscle length (i.e., the second relationship case for traditional MNF and MDF features). To measure the increasing strength of linear relationship between modified MNF (and MDF) feature and elbow joint angle, the correlation analysis was performed. On average, normalized EMG data exhibited greater correlation coefficients compared to raw EMG data for both MNF (0.89>0.78) and MDF (0.86>0.80). This result confirmed a stronger linear relationship of the modified MNF/MDF feature and elbow joint angle. In conclusion, a concept of using the normalization technique to provide the MNF and MDF features to determine the muscle geometry based on changing elbow joint angle was proposed in this paper. As a result of the proposed normalization technique, the modified MNF and MDF features can be used to analyse all three issues consisting of muscle force, muscle geometry, and muscle fatigue. Additional normalization procedure does not require any additional hardware. Acknowledgements. This work is jointly funded by Prince of Songkla University and Thailand Research Fund through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/0155/2554). It is partially supported by NECTEC-PSU Center of Excellence for Rehabilitation Engineering, Faculty of Engineering, Prince of Songkla University.
Fig. 5. (a, c, e) MNF and (b, d, f) MDF of normalized EMG data at a constant load (4 kg) as a function of elbow angles (30º-150º): (a-b) Subj: #11, (c-d) Subj: #13, and (e-f) Subj: #18.
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