A Survey: EMG Signal-Based Controller for Human–Computer Interaction

Subba, Tanuja; Chingtham, Tejbanta Singh

doi:10.1007/978-981-10-8911-4_13

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…Signals produced from an individual's body are bio-signals and can potentially be used as the interface of human-computer devices [4]. Among the bioelectric signals, EMG is tested to be the basis of a natural means of HMI, i.e., an alternate input mechanism.…”

Section: Introductionmentioning

confidence: 99%

The Development of an Interface Instrument for Collecting Electromyography Data and Controlling a Continuous Passive Motion Machine

Jastania,

Wang,

Alqahtani

et al. 2023

Applied Sciences

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There is a lack of research in using electromyography (EMG) signals to control a continuous passive motion (CPM) machine. This study aimed to develop an interface instrument for digitalising EMG signals and controlling a CPM machine. Methods: The proposed device was designed with the following: (1) a signal processing unit which converted the EMGs from analogue to digital for the controller; (2) a personal computer which stored and displayed the EMG signals; (3) an LCD device to display the running angle of the CPM; and (4) a microcontroller unit to control the input/output signals and process the algorithm, driving the CPM. To validate the reliability of the proposed system, a total of 600 EMG trials were collected from 10 healthy subjects by using the proposed device via the Delsys® TringoTM EMG system and simultaneously using the Vicon® motion capture system. Result: This proposed device was able to digitalise and process EMG signals from eight channels of muscles, and the signals were able to drive a CPM. The validated results showed that the digitalised EMG signals by the proposed device were statistically similar to and correlated with the signals by the Vicon system with a median correlation coefficient of 0.81, with the 25% and 75% range being 0.56–0.92 with all pairs (300 pairs of EMG trials) (p < 0.001). Conclusions: This study confirmed that the developed device can digitalise EMG signals and drive a CPM as an applicable prototype that can work as an interface between EMG and CPM devices with high reliability.

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Section: Introductionmentioning

confidence: 99%