Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models

The recognition of the human lower limb jump phases plays an important role in measuring the degree of rehabilitation and the control of the exoskeleton. However, one of the challenges is that the recognition accuracy using sEMG signal is low. In this paper, we propose two types of long-short term memory network (LSTM) models for offline and online recognition of jump sequences. The recognition accuracies of bidirectional LSTM and convolutional LSTM (ConvLSTM) for sEMG reach 97.84% and 97.44%, respectively. When the offline analysis model is used with sEMG sequence of the jump process, the misclassification only occurs in the adjacent phases. From the Pearson correlation coefficients (PCCs) of sEMG and IMU signals, the complex network of muscles and kinematics is built to analyze the coupling of muscle and motion in the jumping process. Taking the sequence composed of PCC matrix with sensor information confusion as the input, ConvLSTM model can acquire spatiotemporal features and the accuracy of the online model can reach 98.13%. In this paper, the number and length of analysis windows that influence the model performance are studied. The synthesis method of Euler angle signals facilitates the recognition of human movement intention.

Automated summary

The paper presents two types of LSTM models for offline and online recognition of jump sequences, with recognition accuracies of 97.84% and 97.44% respectively. By studying the number and length of analysis windows that influence model performance, as well as the synthesis method of Euler angle signals, facilitated the recognition of human movement intention.