Abstract
Background: This paper focuses on the characteristics of lower limb EMG signals for common movements. Methods: We obtained length data for lower limb muscles during gait motion using software named OpenSim; statistical product and service solutions (SPSS) were utilized to study the correlation between each muscle, based on gait data. Low-correlation muscles in different regions were selected; inertial measurement unit (IMU) and EMG sensors were used to measure the lower limb angles and EMG signals when on seven kinds of slope, in five kinds of gait (walking on flat ground, uphill, downhill, up-step and down-step) and four kinds of movement (squat, lunge, raised leg and standing up). Results: After data denoising and feature extraction, we designed a double hidden-layer BP neural network to recognize the above motions according to EMG signals. Results show that EMG signals of selected muscles have a certain periodicity in the process of movement that can be used to identify lower limb movements. Conclusions: It can be seen, after the recognition of different proportions of training and testing sets that the average recognition rate of the BP neural network is 86.49% for seven gradients, 93.76% for five kinds of gait and 86.07% for four kinds of movements.
Highlights
IntroductionHuman motion intention recognition technology offers a way to use physical methods, through all kinds of sensor systems, to identify human motion modes and gait divisions
The results indicate that the muscles with a low correlation in the lower limbs can be distinguished by statistical principles
Four muscles in the lower limb with a low correlation through gait were selected by the OpenSim and statistical product and service solutions (SPSS) correlation analysis module
Summary
Human motion intention recognition technology offers a way to use physical methods, through all kinds of sensor systems, to identify human motion modes and gait divisions. It has important applications in many fields, which has brought widespread interest around the world [1]. It is essential to research more accurate human motion-sensing technology [3]. The methods used to recognize human motion information mainly include bio-force information (such as joint angle, plantar pressure, joint torque, etc.), video imaging and bioelectrical signals (such as in an electrocardiogram (ECG), electrooculogram (EOG), electromyography (EMG), etc.) [4,5,6,7]
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