Mouse HCI Through Combined EMG and IMU

Forbes, Timothy

doi:10.23860/thesis-forbes-timothy-2013

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…IMU and EMG signals have been used together as a mean to improve classifiers for gesture [19] and American Sign Language [20] recognition, as well as capturing the motion of the arm [21]. When muscle activities were used for control, the design of a control map was based on supervised methods, as in the case of a hybrid-based classifier for the control of a computer cursor [22,23], a trans-humeral prosthesis [24], or a powered wheelchair [25]. This approach, however, limits the user's ability to operate the device in a continuous manner, as the actions to be performed are predefined and not fully tailored to the user's available motor abilities.…”

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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Sensor fusion in upper limb area networks: A survey

Duivenvoorden

Lee

Raison

et al. 2017

2017 Global Information Infrastructure and Networking Symposium (GIIS)

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SEMG signal acquisition system for muscle fatigue detection

Correa-Figueroa

Morales-Sánchez

Huerta-Ruelas

et al.

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This paper presents the development of a system for acquiring and processing of surface myoelectric signals or SEMG. The proposed system acquires signals SEMG skin surface using AgCl surface electrodes. The system has an amplification step and hardware filtering to streamline the processing time. Developed software for processing the Fourier transform SEMG amplified and filtered signal. Unlike other systems for acquisition of biological signals, which are developed for therapy or rehabilitation, this system is intended to be used for the control of robotic arms, so the software performs the measurement of fatigue using parameters like bleed average frequency and instantaneous power spectral density of the signal SEMG.

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Mouse HCI Through Combined EMG and IMU

Cited by 3 publications

References 16 publications

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

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

Sensor fusion in upper limb area networks: A survey

SEMG signal acquisition system for muscle fatigue detection

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