TY - JOUR
T1 - sEMG-Based Hand Movement Regression by Prediction of Joint Angles With Recurrent Neural Networks
AU - Koch, Philipp
AU - Mohammad-Zadeh, Kamran
AU - Maass, Marco
AU - Dreier, Mark
AU - Thomsen, Ole
AU - Parbs, Tim J.
AU - Phan, Huy
AU - Mertins, Alfred
PY - 2021/11/1
Y1 - 2021/11/1
N2 - This work takes a step towards a better biosignal based hand gesture recognition by investigating the strategies for a reliable prediction of hand joint angles. Those strategies are especially important for medical applications in order to achieve e.g. good acceptance of hand prostheses among amputees. A recurrent neural network with a small footprint is deployed to estimate the joint positions from surface electromyography data measured at the forearm. As the predictions are expected to be not smooth, different post processing methods and a regularisation term for the objective function of the network are proposed. The experiments were conducted on publicly available databases. The results reveal that both post processing strategies and regularisation have a positive impact on the results with a maximal relative improvement of 6.13 %. On the one hand post processing strategies introduce an additional delay, consequently, the improvement is analysed in context of the caused delay. On the other hand the regularisation strategy does not cause a delay and can be adjusted easily to cope with different ground truths or compensate for certain problems in the hand tracking.
AB - This work takes a step towards a better biosignal based hand gesture recognition by investigating the strategies for a reliable prediction of hand joint angles. Those strategies are especially important for medical applications in order to achieve e.g. good acceptance of hand prostheses among amputees. A recurrent neural network with a small footprint is deployed to estimate the joint positions from surface electromyography data measured at the forearm. As the predictions are expected to be not smooth, different post processing methods and a regularisation term for the objective function of the network are proposed. The experiments were conducted on publicly available databases. The results reveal that both post processing strategies and regularisation have a positive impact on the results with a maximal relative improvement of 6.13 %. On the one hand post processing strategies introduce an additional delay, consequently, the improvement is analysed in context of the caused delay. On the other hand the regularisation strategy does not cause a delay and can be adjusted easily to cope with different ground truths or compensate for certain problems in the hand tracking.
UR - http://www.scopus.com/inward/record.url?scp=85122538781&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9630042
DO - 10.1109/EMBC46164.2021.9630042
M3 - Journal articles
C2 - 34892603
AN - SCOPUS:85122538781
SN - 2694-0604
VL - 2021
SP - 6519
EP - 6523
JO - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
JF - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
IS - 43
ER -