Proportional and Simultaneous Real-Time Control of the Full Human Hand From High-Density Electromyography

Surface electromyography (sEMG) is a non-invasive technique that measures the electrical activity generated by the muscles using sensors placed on the skin. It has been widely used in the field of prosthetics and other assistive systems because of the physiological connection between muscle electrical activity and movement dynamics. However, most existing sEMG-based decoding algorithms show a limited number of detectable degrees of freedom that can be proportionally and simultaneously controlled in real-time, which limits the use of EMG in a wide range of applications, including prosthetics and other consumer-level applications (e.g., human/machine interfacing). In this work, we propose a new deep learning method that can decode and map the electrophysiological activity of the forearm muscles into proportional and simultaneous control of > 20 degrees of freedom of the human hand with real-time resolution and with latency within the neuromuscular delays (< 50 ms). We recorded the kinematics of the human hand during grasping, pinching, individual digit movements and three gestures at slow (0.5 Hz) and fast (0.75 Hz) movement speeds in healthy participants. We demonstrate that our neural network can predict the kinematics of the hand in real-time at a constant 32 predictions per second. To achieve this, we employed transfer learning and created a prediction smoothing algorithm for the output of the neural network that reconstructed the full geometry of the hand in three-dimensional Cartesian space in real-time. Our results demonstrate that high-density EMG signals from the forearm muscles contain almost all the information that is needed to predict the kinematics of the human hand. The proposed method has the capability of predicting the full kinematics of the human hand with real-time resolution with immediate translational impact in subjects with motor impairments.