Last data update: Apr 04, 2025. (Total: 49030 publications since 2009)
Filtered By: |
Order By: | Records 1-1 (of 1 Records) |
Query Trace: Werren DM [original query] |
---|
A single-camera method for estimating lift asymmetry angles using deep learning computer vision algorithms
Lou Z , Zhan Z , Xu H , Li Y , Hu YH , Lu ML , Werren DM , Radwin RG . IEEE Trans Human Mach Syst 2025 A computer vision (CV) method to automatically measure the revised NIOSH lifting equation asymmetry angle (A) from a single camera is described and tested. A laboratory study involving ten participants performing various lifts was used to estimate A in comparison to ground truth joint coordinates obtained using 3-D motion capture (MoCap). To address challenges, such as obstructed views and limitations in camera placement in real-world scenarios, the CV method utilized video-derived coordinates from a selected set of landmarks. A 2-D pose estimator (HR-Net) detected landmark coordinates in each video frame, and a 3-D algorithm (VideoPose3D) estimated the depth of each 2-D landmark by analyzing its trajectories. The mean absolute precision error for the CV method, compared to MoCap measurements using the same subset of landmarks for estimating A, was 6.25° (SD = 10.19°, N = 360). The mean absolute accuracy error of the CV method, compared against conventional MoCap landmark markers was 9.45° (SD = 14.01°, N = 360). © 2013 IEEE. |
- Page last reviewed:Feb 1, 2024
- Page last updated:Apr 04, 2025
- Content source:
- Powered by CDC PHGKB Infrastructure