# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 1 Instruction  
Lower-limb exoskeletons and prostheses allow stroke patients and lower limb hemiplegia patients much greater convenience, especially in terms of improving their stability for walking on flat ground [1]. With in-depth studies, lower-limb exoskeletons and prostheses have been developed in the direction of smarter man-machine collaborations, which enhance the ability of man-machine coordination, to improve the rehabilitative effect by detecting the wearer's intention of motion [2; 3]. This requires adjustment of the control signal input for the lower-limb exoskeleton and prostheses according toreal-time gait information. Thus, accurate gait recognition is crucial, as it contributes to the realization of real-time control. For the gait phases in gait recognition, the existing division includes five phases [4], seven phases [5], and eight phases in a gait cycle [6]. In our study, we adopted the mainstream five-phase division in a gait cycle, which consists of pre-stance, mid-stance, terminal stance, pre-swing, and terminal swing per cycle. Many researchers have adopted surface electromyogram (sEMG) signals to recognize gait for the control of lower-limb exoskeletons (or prostheses) [7]. Due to their capability for of revealing the inner workings of the human motor nerve system, sEMG signals can directly reflect the subject's intended activity, and the motor function instruction information of the neuromuscular system [8]. In addition to the convenience and non-invasive nature of the signal acquisition, sEMG signals are also widely used as a neural control source for human-machine interaction, such as the control of lower-limb exoskeletons (or prostheses). [PERSON] et al. [5] employed a Bayesian information criterion (BIC) and a linear discriminant analysis (LDA) classification algorithm to deal with lower-limb sEMG signals for gait recognition by determining the boundaries of adjacent gait phases in seven gait phases (loading response, mid-stance, terminal stance, pre-swing, initial swing, mid-swing, and terminal swing). [PERSON] et al. [4] employed support vector machines (SVMs) and lower-limb sEMG to demonstrate that both individual differences and sample sizes have an influence on gait classification accuracy. [PERSON] et al. [9] took sEMGs from a disabled thigh as control signals to drive artificial lower limbs based on support vector machines and achieved conversion from standing to walking. [PERSON] et al. [10] proposed a multiple classification algorithm for lower-limb movement recognition based on a simplified support vector machine, and successfully identified the movements of standing, walking, and going up and down the stairs. The works mentioned above focus mainly on increasing the accuracy of gait phase recognition via sEMG data collection via sensors, effective preprocessing of data, reasonable extraction of feature vectors and algorithm optimization, etc. Although these methods may achieve an ideal accuracy of gait recognition of up to 95% in laboratory conditions, the results are not so encouraging in real-time environments. There are several practical limitations for sEMGs for use in gait recognition for the control of exoskeletons and prostheses. [PERSON] et al. [8] demonstrated that performance in gait recognition is not guaranteed as a result of the influence of the variable environmental factors. The most common variable factors include different lower-limb positions [11; 12], sEMG signal instability [13], displacement of the electrode [14], and so on; moreover, load variation is a problem that cannot be ignored. Some scholars have studied the effects of different loads on pattern recognition based on electromyography. For instance, [PERSON] et al. [15] revealed that the load variation can affect the recognition accuracy (about 60% difference between best and worst accuracy) for the same control system, based on sEMG. [PERSON] et al.