Development of a Fatigue-Tracking System for Monitoring Human Body Movement Haiwei Dong∗ , Izaskun Ugalde† and Abdulmotaleb El Saddik ∗ Multimedia
Communications Research Laboratory, EECS, University of Ottawa, Canada Email: {hdong, elsaddik}@uottawa.ca † Division of Engineering, New York University Abu Dhabi, UAE Email:
[email protected]
Abstract—Monitoring fatigue and furthermore predicting fatigue is quite important in fundamental research and practical applications. In this paper, we developed a wireless wearable system for quantifying and tracking fatigue. The system is based on the scientific electromyography kinesiology study, which shows the mean frequency of the surface electromyogram (sEMG) signal decreases with the increase of fatigue intensity. According to this clue, from the engineering viewpoint, we assume the decrease relation mentioned is a linear relation and use statistical analysis (including 10 male subjects and 7 female subjects) to prove this assumption. Besides, in order to accurately assess fatigue, fatigue level is defined. Based on the simplified linear model mentioned, the fatigue level can be calculated efficiently. Furthermore, by considering the fatigue process as a dynamic process, we track the fatigue level by “forgetting” the sEMG measurement history taken at a relatively long time ago. Finally, the performance of the developed fatigue tracking system is tested and verified by the experiment of holding self weight.
I.
I NTRODUCTION
Fatigue is a common scenario in human movement, which is always called as “tired”. From the viewpoint of biomechanics, it can be defined as a decrease in physical movement performance because of internal and external forces [1], [2]. A serious fatigue can lead to musculoskeletal disorder (MSD) which is always due to overload and cumulative physical fatigue [3]. Whereas, if the fatigue can be controlled in a proper level, it can strength the muscles’ power in exercise. Therefore, monitoring fatigue and furthermore predicting fatigue is quite important both in fundamental research and in practical applications. For instance, the fatigue measurement can be used in promoting muscle performance/growth or preventing training injury in sport scenarios [4]; the fatigue monitoring can also be applied in preventing over training and intolerant exercise in rehabilitation and medical services [5], [6]. Until now, the fatigue assessment has been considered from two viewpoints in the joint level and in the muscle level, respectively. Fatigue assessment in the joint level evaluates the fatigue produced by multi groups of muscles acting on one specific joint. For this specific joint, it has two kinds of external torques acting on it. One is active torque produced by external loads and self body mass; the other is passive torque produced by ligaments and tissues inside the body. When the movement is close to the extreme position, the passive torque produces a negative torque. By considering the active and passive torque in the inverse dynamics, it is applicable to calculate the internal torque generated by muscles. Based on the calculated internal torque, the joint fatigue assessment can
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be modeled as a first-order differential process of the current joint load torque divided by the maximum joint load [7], [8]. The fatigue assessment in the muscle level can be simply referred as muscle fatigue. A scientific definition of muscle fatigue can be stated as the “progressive increase in the effort required to exert a desired force and the eventual progressive inability to maintain this force in sustained or repeated contractions” [9]. Compared with the fatigue assessment in the joint level, it has been intensively addressed as numerous muscle fatigue models have been built according to the Ca2+ crossbridge mechanism [10], [11], force-PH relation [12], [13], elastic element modeling (e.g., Hill’s model) [14], etc. Besides, in practical clinical applications, measurement of electrogyogram (EMG) and mechanomyogram (MMG) can reflect the electrical and mechanical aspects of muscle activity which is also an indication of muscle fatigue. Recently, noninvasive technique (surface electrogyogram, abbreviated as sEMG) has been more focused in fatigue measurement as the subject has no discomfort at all (no needle puncture) [15]. Considering the fatigue assessment methods mentioned, the assessment in the joint level can be run efficiently due to the well developed musculoskeletal simulation [16], [17]. However, it is a rough estimation and hence usually applied in ergonomics design, where only general fatigue evaluation with respect to the repetitiveness, intensity and duration of a physical task is required [18]. To accurately assess the fatigue, we need to go back to the muscle level. Due to individual differences, the fatigue assessment based on noninvasive sEMG measurement would have a better performance. Although the muscle fatigue assessment is a localized assessment regarding a specific task, it can simply extend to an overall fatigue assessment by fusing all the localized muscle fatigue assessments [19]. In this paper, we focus on the development of an fatigue-tracking system by sEMG measurement. Compared with previous research, the following issues are addressed: Finding an effective feature to indicate fatigue: The fatigue feature can be possibly obtained by analyzing the sEMG signal in the time domain or frequency domain. Regarding sEMG changes in the time domain, the amplitude variables (such as root mean square, average rectified value, integrated EMG, etc) can reflect the fatigue level [20]. During maximum voluntary contraction movement, the sEMG amplitude variables decrease continuously [21]. On the other hand, regarding sEMG changes in the frequency domain, the power spectrum variables of the sEMG signals that are classically used include mean frequency, median frequency, mode frequency [22]. These
variables decrease continuously during sustained contraction, whose scenario can be used as an indicator of fatigue intensity. In this research, we choose the mean frequency of the power spectrum as a fatigue feature as it is less sensitive to noise [10]. Defining a fatigue level based on the feature: Basically, the fatigue level can be described by “maximum dwell time of muscle activity” and “frequency change of the sEMG power spectrum” [23]. The former gives an index on the dwell time for a predefined static force [24], which is based on a simple scenario: the longer the muscle works, the more tired it is. The latter provides a dynamic index of a muscle’s working status online. In this paper, we choose the frequency change to propose the fatigue level. Although plenty of previous literatures have shown that the mean frequency of the sEMG signal decreases with fatigue and usually, the mean frequency can be empirically fitted by linear regression. However, to our knowledge, there has been no statistical analysis done proving this linear relation. In this paper, we focus on the statistical analysis of mean frequency change. Furthermore, by comparing with the mean frequency at the initial moment, we define a fatigue level index. Tracking the fatigue level: The fatigue level change is a dynamic process. To track the current fatigue level accurately, the fatigue level in the previous moments needs to be counted in. Here, we create a “forgetting factor”, whose physical meaning is how much previous information should be counted in [25]. Based on the concept of dynamically “forgetting previous information”, a fatigue level tracking method is proposed. In this paper, according to the statistical analysis of the fatigue experiment (including 10 male subjects and 7 female subjects), the mean frequency of the sEMG signal is proved to satisfy linear relation with the working time. Based on this statistical result, we use the mean frequency of the power spectrum of sEMG to define an index of fatigue level. Furthermore, the tracking scheme of the fatigue level is proposed. The overall system is tested by an experiment of holding self weight. This paper is organized as follows. In Section 2, the system architecture is firstly illustrated. Then the detailed methods are explained. 1) The sEMG signal is segmented by recognizing the periodic movements and connected to form a new sEMG signal. 2) A fatigue level is defined based on the mean frequency of sEMG signal. 3) The tracking of fatigue level is proposed. After that, the developed system is tested in Section 3 where the analysis of mean frequency trend and tracked fatigue level are emphasized. Finally, the conclusion is drawn in Section 4. II.
T HE P ROPOSED S YSTEM
A. System Architecture and Scheme The fatigue-tracking system includes a central processing PC, a wireless communication center and a couple of fatigue sensors (Fig.1). The fatigue sensor used is Delsys Trigno wireless sensor (37mm×26mm×15mm, 16-bit resolution, 2000 Hz sampling rate), which consists of a parallel-bar-based EMG measurement device and a triaxial accelerometer. The trixial accelerometer is used to capture dynamic movements and impact simultaneously with the EMG data measurement. As the fatigue sensor is required to be located at the center of the
Fig. 1: System scheme of the fatigue-tracking system. The system consists of a central PC, a wireless communication station and a couple of fatigue sensors.
muscle span, during muscle’s contraction, the body movement can be recorded at the same time. The wireless communication center is used to collect online data from the fatigue sensors (communication distance is 40m) and send the data to the central processing PC in real time. It can communicate with 16 fatigue sensors at the same time (i.e., having 16 EMG channels, 48 accelerometer channels). The central PC computes and displays the tracked fatigue level in real-time. The sEMG processing procedures in the central PC are briefly illustrated as follows. The sEMG signal and corresponding acceleration signal are firstly resampled as the sampling rate of the two signals are usually different. After that, the two signals with the same sampling rate are filtered to remove high frequency noise. The filtered acceleration signal is then used for recognizing periodic movement. If periodic movement is found, the filtered sEMG signal with periodic movement is segmented and connected for mean frequency calculation. The calculated mean frequency of the sEMG signal is used to compute the fatigue level, which is then tracked based on the sEMG measurement history. If there is no periodic movement recognized, the sEMG segmentation procedure is skipped. The details of each procedure is explained in the following subsections. B. Pre-processing sEMG and Recognizing Periodic Movement As the sampling frequency of raw sEMG and acceleration signals are recorded by different sampling rate, we resample the signal with high sampling ratio to make the two signals consistent in data length. The resampled sEMG and acceleration signals are processed to remove noise by Butterworth filter. Specifically, in designing the Butterworth filter, the lowest order of the filter n and normalized cutoff frequency Wn are computed firstly by the designed filter parameters, including passband corner frequency Wp , stopband corner frequency Ws , passband ripple Rp and stopband attenuation Rs . After that, the Butterworth filter is determined by n and Wn .
There are two working patterns in muscle’s movement: sustained contraction (considered as nonperiodic movement) and alternate contraction-recovery (considered as periodic movement). The former is easier for analysis, as it is a continuous consistent movement pattern; the latter is complex, as it consists of contraction and recover phase, corresponding with active sEMG and inactive sEMG, respectively. To assess the muscle fatigue for the alternate contraction-recovery muscle movement, we segment the contraction movement and connect the corresponding active sEMG signals. In detail, we use the cross-covariance to analyze the acceleration signal to verify if the recorded movement is a periodic movement and if so, to further find out the breaking points for segmentation. For the acceleration signal ACC with N samples, we compute the cross-covariance φACC by [26]
φACC ∗ = E (ACC (n + m) − µACC ) (ACC (n) − µACC ) P PN −1 N −|m|−1 1 ACC (n + m) − ACC (i) N i=0 n=0 P = · ACC ∗ − 1 N −1 ACC (i)∗ f or m ≥ 0 (1) i=0 N ∗ (−m) f or m < 0 CovACC where E {·} is the expected value operator, µACC is the mean values of ACC, and ∗ denotes the complex conjugate. The periodic movement is confirmed if M ax (φACC , 1) − M ax (φACC , 2)
