A hybrid body-machine interface integrating signals from muscles and motions

Body-Machine Interfaces (BoMIs) establish a way to operate a variety of devices, allowing their users to extend the limits of their motor abilities by exploiting the redundancy of muscles and motions that remain available after spinal cord injury or stroke. Here, we considered the integration of two types of signals, motion signals derived from inertial measurement units (IMUs) and muscle activities recorded with electromyography (EMG), both contributing to the operation of the BoMI. A direct combination of IMU and EMG signals might result in inefficient control due to the differences in their nature. Accordingly, we used a nonlinear-regression-based approach to predict IMU from EMG signals, after which the predicted and actual IMU signals were combined into a hybrid control signal. The goal of this approach was to provide users with the possibility to switch seamlessly between movement and EMG control, using the BoMI as a tool for promoting the engagement of selected muscles. We tested the interface in three control modalities, EMG-only, IMU-only and hybrid, in a cohort of 15 unimpaired participants. Participants practiced reaching movements by guiding a computer cursor over a set of targets. We found that the proposed hybrid control led to comparable performance to IMU-based control and significantly outperformed the EMG-only control. Results also indicated that hybrid cursor control was predominantly influenced by EMG signals. We concluded that combining EMG with IMU signals could be an efficient way to target muscle activations while overcoming the limitations of an EMG-only control.This work was supported by the Marie Curie Integration Grant (FP7- PEOPLE-2012-CIG-334201), the Ministry of Science and Technology, Israel (Joint Israel-Italy lab in Biorobotics Artificial somatosensorial for humans and humanoids), the National Science Foundation Grant 1632259, the NIDILRR Grant 90REGE0005-01, the NICHHD Grant 5R01HD072080 and the European Unions Horizon 2020. research and innovation program under the Marie Sklodowska-Curie, project REBoT, G.A. No750464