A Myoelectric Computer Interface for Reducing Abnormal Muscle Activations after Spinal Cord Injury

Rizzoglio, Fabio; Sciandra, Francesca; Galofaro, Elisa; Losio, L.; Quinland, E.; Leoncini, Clara; Massone, Antonino; Mussa-Ivaldi, Ferdinando A.; Casadio, Maura

doi:10.1109/icorr.2019.8779493

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Myoelectric interfaces have been widely studied [13], especially in the context of prosthetics [14], [15], but also for rehabilitative purposes [16], both in SCI [17] and stroke [18]. However, EMG signals are inherently noisier than motion signals.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Noninvasive myoelectric sensing has been explored in human-device interaction studies, including teleoperation [20], user understanding [43], VR/AR training [55], and upper-limb [18] and lower-limb prostheses [21], and rehabilitation [33, 47, 50, 56]. From an HCI perspective, these studies demonstrated an accessible alternative to traditional manual devices like mice and keyboards for users with upper-limb motor impairments [70].…”

Section: Related Workmentioning

confidence: 99%

Using Eye Gaze to Train an Adaptive Myoelectric Interface

Chou,

Madduri,

et al. 2024

Preprint

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Myoelectric interfaces hold promise in consumer and health applications, but they are currently limited by variable performance across users and poor generalizability across tasks. To address these limitations, we consider interfaces that continually adapt during operation. Although current adaptive interfaces can reduce inter-subject variability, they still generalize poorly between tasks because they make use of task-specific data during training. To address this limitation, we propose a new paradigm to adapt myoelectric interfaces using natural eye gaze as training data. We recruited 11 subjects to test our proposed method on a 2D computer cursor control task using high-density surface EMG signals measured from forearm muscles. We find comparable task performance between our gaze-trained paradigm and the current task-dependent method. This result demonstrates the feasibility of using eye gaze to replace task-specific training data in adaptive myoelectric interfaces, holding promise for generalization across diverse computer tasks.

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A Myoelectric Computer Interface for Reducing Abnormal Muscle Activations after Spinal Cord Injury

Cited by 2 publications

References 14 publications

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

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

Using Eye Gaze to Train an Adaptive Myoelectric Interface

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