A parallel and efficient transformer deep learning network for continuous estimation of hand kinematics from electromyographic signals

Surface electromyography (EMG) provides a non-invasive human-machine interaction interface that can promote the coherence of human-machine interaction operations. Decomposing surface electromyographic signals into hand joint angles in real time can be applied to prosthetic control, rehabilitation engineering and other fields. However, existing methods of using surface electromyography signals suffer from high end-to-end latency, high memory consumption, and high power consumption, which hinder their dissemination in clinical edge devices and public wearable devices. After a thorough analysis of the state-of-the-art surface EMG based architecture, we observed that the time complexity of the attention mechanism in using Transformer for continuous motion estimation results in longer inference time. The attention mechanism requires a large number of parameters to achieve good results, leading to higher model power consumption. This will reduce its performance in continuous motion statistics. To tackle the existing Surface EMGs challenges, PET, a lightweight parallel efficient transformer model, is proposed. We elaborately develop a thorough bottom-up architecture of PET, from model structure and power mechanism. The PET's parallel and lightweight architecture can decompose the surface electromyography in real time and output the hand joint angles while compacting memory consumption and affordable power expenditure without sacrificing the accuracy of extracting motion statistics. Compared to the state-of-the-art surface EMG architectures, the experimental results demonstrate that PET outperforms SVR, TCN, LSTM, GRU, LE-LSTM, LE-ConvMN, Transformer, Bert, MAFN and Conformer by Correlation Coefficient, RMSE, NRMSE, AME, End-to-end latency in variety of challenging Surface EMG programs, including Ninapro DB2, Ninapro DB7, FMHD, and SEEDS. The PET correlation coefficient for all 60 subjects in the Ninapro dataset was 0.85 ± 0.01, the root mean square error was 7.26 ± 0.32, the normalized RMSE was 0.11 ± 0.01, and the AME was 6.183. The PET correlation coefficient in the test of the Finger Movement HD was 0.81 ± 0.01, and the root mean square error was 10.15 ± 0.52 with a normalized RMSE of 0.11 ± 0.01. The PET correlation coefficient in the test of the SEEDS was 0.82 ± 0.01, and the root mean square error was 10.09 ± 0.01 with a normalized RMSE of 0.10 ± 0.01. Our method achieved state-of-the-art performance in the above tests. The results of the above tests were based on the same subjects.