We investigate the use of high-density surface EMG (HDsEMG) recordings of intrinsic hand muscles, along with those from extrinsic muscles, on finger and wrist kinematic prediction performance. We incorporate these HDsEMG signals using a framework based
on a custom hybrid convolutional-recurrent deep learning
model. Methods: Five healthy subjects performed a wide
variety of motion tasks activating multiple degrees of freedom of the wrist and fingers. During the tasks, HDsEMG signals were recorded from extrinsic and intrinsic muscles of the hand while motion capture technology tracked the hand/wrist kinematics. A convolutional-recurrent model architecture was designed and trained on the recorded dataset, incorporating both residual connections as well as inception convolutional structures. Results: The proposed model led to greater regression accuracy over the simultaneous prediction of 12 joint angles (CC, MAE and RMSE of 0.850, 4.84 degrees and 11.2 degrees respectively) than previously proposed mapping models, when incorporating both intrinsic and extrinsic muscle signals. The inclusion of
both sets of hand muscles also led to statistically greater
performance than the same model trained on only extrinsic muscle data. Conclusion: We show accurate predictions of hand/wrist kinematics from combined extrinsic and intrinsic hand muscle myoelectric activity, using a convolutionalrecurrent hybrid deep learning model. This greater performance is replicated over several subjects and across multidegree of freedom motion tasks. Significance: Our developed system (electrode setup and deep neural networks) can be translated into a compact wearable interface in the
future for medical as well as consumer applications.