Statistical and Practical Significance of Articles at Sports Biomechanics Conferences

Hasan, Uday

doi:10.52547/aassjournal.947

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…However, the commonly used recognition algorithms cannot effectively identify the human motion intention due to the weakness and low signal-to-noise ratio of the EMG signal [10]. The research of human motion recognition based on the EMG signal is still in the primary stage, which also makes researchers work on designing different classifiers to improve the recognition efficiency [11,12]. Kundu B takes the EMG signal as input and motion classification, noise tolerance, and computational complexity as criteria to recognize movements through genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Classification and Regression of Muscle Neural Signals on Human Lower Extremities via BP_AdaBoost

Wang

Dai

2022

Applied Sciences

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Electromyography (EMG) signals are widely applied in the classification of human motion and intention recognition as having the characteristic of earlier than actual limb motion. In this article, to improve its accuracy of classification and prediction, we firstly analyze the relationship between muscle length and joint movement and select rectus femoris and biceps femoris as the experimental muscles to collect neural signals by means of musculoskeletal analysis software. EMG sensors are used to measure those muscles’ EMG signals of five kinds of knee movements, including thigh-raising, calf-raising, squatting, knee bending on chair, and walking. We designed a BP_AdaBoost algorithm with the BP neural network as a weak classifier and weak regressor, and a muscle neural activation is used as the input for recognition. It is a negative correlation between the length of the rectus femoris and the biceps femoris during gait. Their muscle neural signals are used as the input of the recognition algorithm. The experiment results show that the proposed algorithm improves the rate of BP neural network from 78.82% to 93.52%. The thigh EMG signal successfully maps the knee joint angle by utilizing BP_AdaBoost; its error in identifying five kinds of motion modes is lowest compared with other regression algorithms.

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Section: Introductionmentioning

confidence: 99%