Work

Force estimation for human–robot interaction using electromyogram signals from varied arm postures

Year: 2024

Type: article

Abstract: In this paper, a system for force estimation based on surface electromyography signals measured from the eight channels of the Myo armband is presented. We evaluated nineteen regression models to continuously estimate force in three scenario cases to cover the natural movement in two degrees of freedom planer rehabilitation mobile robots. The best estimation model that could overcome the challenge in a variety of different scenarios was determined. Based on the experimental results, the Gaussian process regression model performed best, giving a root mean square error in the overall range of 1.18–1.77 N. Additionally, the results showed that the exponential algorithm outperformed other solutions, significantly reducing the force estimation error.

Source: EURASIP Journal on Advances in Signal Processing

DOI: 10.1186/s13634-024-01183-7

Authors: Thantip Sittiruk, Kiattisak Sengchuai, Apidet Booranawong, Paramin Neranon, Pornchai Phukpattaranont

Institution: Prince of Songkla University

Language: English

Topic: Muscle activation and electromyography studies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,790