Treffer: Evaluating the Feasibility of EMG-Based Human-Machine Interfaces for Driving.

Title:
Evaluating the Feasibility of EMG-Based Human-Machine Interfaces for Driving.
Authors:
Basnet N; Texas A&M University, USA., Allahvirdi S; Texas A&M University, USA., Nadri C; Texas A&M University, USA., Park J; University of Calgary, Canada., Zahabi M; Texas A&M University, USA.
Source:
Human factors [Hum Factors] 2026 Jan; Vol. 68 (1), pp. 123-141. Date of Electronic Publication: 2025 Aug 12.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Human Factors and Ergonomics Society Country of Publication: United States NLM ID: 0374660 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1547-8181 (Electronic) Linking ISSN: 00187208 NLM ISO Abbreviation: Hum Factors Subsets: MEDLINE
Imprint Name(s):
Publication: Santa Monica, Ca : Human Factors and Ergonomics Society
Original Publication: New York, N.Y. : Pergamon Press, 1958-4
Contributed Indexing:
Keywords: EMG-based HMI; assistive technology; driving simulation; upper-limb prosthetic
Entry Date(s):
Date Created: 20250813 Date Completed: 20260101 Latest Revision: 20260101
Update Code:
20260102
PubMed Central ID:
PMC12698871
DOI:
10.1177/00187208251367179
PMID:
40797297
Database:
MEDLINE

Weitere Informationen

ObjectiveTo evaluate the feasibility of electromyography (EMG)-based human-machine interfaces (HMIs) for high-demand activities such as driving based on performance, cognitive workload, usability, and safety measures.BackgroundUpper-limb amputees face challenges in performing everyday tasks, including driving. EMG-based HMIs offer potential solutions, particularly for wrist disarticulated and trans-radial amputee, but their effectiveness in complex tasks like driving requires further investigation.MethodNineteen able-bodied participants completed a driving simulation study using an EMG-based HMI, dominant hand, and both hands. Participants performed various driving maneuvers including straight lane driving, overtaking, and 90-degree turns at intersections. Driver performance, cognitive workload (measured by blink rate and subjective measures), usability (USE questionnaire), and safety were assessed.ResultsUsing the EMG-based HMI led to higher lane offset and steering angle compared to conventional methods, but demonstrated lower steering entropy in some situations. Cognitive workload was higher for EMG-based HMI, while usability scores were lower. Safety measures were mixed, with EMG-based HMI showing better performance at intersections but lower lane offset and steering angle safety scores overall.ConclusionThe study highlights both limitations and opportunities presented by EMG-based HMIs in high-demand tasks such as driving. While the system exhibited lower performance in some conditions, it demonstrated potential for controlled driving, particularly during specific maneuvers. The higher cognitive workload and lower usability scores indicate areas for improvement.ApplicationThe findings provide valuable insights for the development of more effective EMG-based HMIs, supporting future research and clinical trials aimed at enhancing mobility and independence for individuals with upper-limb amputations.

Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.