Details

Autor(en) / Beteiligte

• Panchal, Marcus
• Tanzarella, Simone
• Jung, Moon Ki
• Farina, Dario

Titel

Mapping Intrinsic and Extrinsic Muscle Myoelectric Activity During Natural Dynamic Movements Into Finger and Wrist Kinematics Using Deep Learning Prediction Models

Ist Teil von

• IEEE transactions on human-machine systems, 2023-10, Vol.53 (5), p.1-11

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEL

Beschreibungen/Notizen

• Objective: We investigate the use of high-density surface electromyographic (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 (correlation coefficient, mean absolute error, and root mean squared error of 0.850, 4.84°, and 11.2°, 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 convolutional-recurrent 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.