A hybrid Body-Machine Interface integrating signals from muscles and motions

Abstract

Objective. 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. Approach. 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. Main results. 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. Significance. 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.

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

“…However, sEMG only contains the signal of muscle activity directly related to gesture, not enough to reflect all the information of the gesture. In order to solve these dilemmas, some research workers have come up with many ideas, such as HD-sEMG system or multi-mode sensor system including accelerometer (Jiang et al, 2018;Rizzoglio et al, 2020), nearinfrared spectroscopy (Nissler et al, 2016;Scano et al, 2019), and electroencephalogram (Li et al, 2017). Accelerometer can reflect the dynamic characteristics of muscle rotation and translation.…”

Targeted Muscle Training with a Hybrid Body-Machine Interface

“…We propose a novel approach for engaging targeted muscles into skilled activities while operating assistive interfaces based on wearable sensors. A recent work explored the advantages of integrating EMG and kinematics for controlling body-machine interfaces [6]. The authors suggest that the parameters of a non-linear regression could be tuned to modulate muscle contributions to the control.…”

“…Accordingly, only the overall effect of coordinated muscle activations can be controlled and observed. Myoelectric interfaces, on the other side, directly map the activity of a selected group of muscles into control inputs for the device, but suffer from recording instabilities, are sensitive to electrode placement and inject more uncertainty in the control due to the lower signal-to noise ratio [6].…”

Vibrotactile Perception for Sensorimotor Augmentation: Perceptual Discrimination of Vibrotactile Stimuli Induced by Low-Cost Eccentric Rotating Mass Motors at Different Body Locations in Young, Middle-Aged, and Older Adults

“…Non-invasive body-machine interfaces are being developed to mitigate a number of sensorimotor impairments due to disease and injury [c.f., (11)(12)(13)(14)(15)(16)]. To advance our long-term goal, we specifically seek to develop and use low-cost technology to enhance or augment closed-loop feedback control of limb movements in patients with proprioceptive deficits, but who still retain the ability to generate movement.…”