The Feasibility of Quantifying Frequency and Duration of Hand Exertions Using Surface Electromyography
Mentor 1
Dr. Jay Kapellusch
Location
Union Wisconsin Room
Start Date
24-4-2015 2:30 PM
End Date
24-4-2015 3:45 PM
Description
Surface electromyography (sEMG) has shown promise in estimating grip forces while people are performing tasks, but has not been used to predict frequency and duration of those forceful exertions. Multi video task analysis (MVTA) is a video analysis tool that can be used to reliably quantify work activities. However, video analysis is typically very time consuming. The objective of this experiment is to determine the feasibility of using sEMG to quantify frequency and duration of exertions as an alternative to video analyses. Five different hand tasks were simulated and sEMG signals and video were collected simultaneously. Hand tasks required varying forces, frequencies, and durations of hand efforts. Videos were analyzed using MVTA, and sEMG signals were analyzed using an algorithm to quantify patterns of efforts and durations of efforts. Patterns from the two techniques were then compared graphically. Preliminary results suggest that sEMG can reliably quantify force, frequency, and duration of hand efforts.
The Feasibility of Quantifying Frequency and Duration of Hand Exertions Using Surface Electromyography
Union Wisconsin Room
Surface electromyography (sEMG) has shown promise in estimating grip forces while people are performing tasks, but has not been used to predict frequency and duration of those forceful exertions. Multi video task analysis (MVTA) is a video analysis tool that can be used to reliably quantify work activities. However, video analysis is typically very time consuming. The objective of this experiment is to determine the feasibility of using sEMG to quantify frequency and duration of exertions as an alternative to video analyses. Five different hand tasks were simulated and sEMG signals and video were collected simultaneously. Hand tasks required varying forces, frequencies, and durations of hand efforts. Videos were analyzed using MVTA, and sEMG signals were analyzed using an algorithm to quantify patterns of efforts and durations of efforts. Patterns from the two techniques were then compared graphically. Preliminary results suggest that sEMG can reliably quantify force, frequency, and duration of hand efforts.
