Auto-calibrated Wearable System for Load Vertical Location Estimation During Manual Lifting
November 25, 2024 Β· Declared Dead Β· π IEEE Internet of Things Journal
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Authors
Diliang Chen, Nozhan Ghoreishi, John LaCourse, Sajay Arthanat, Dain LaRoche
arXiv ID
2411.16097
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
IEEE Internet of Things Journal
Last Checked
5 months ago
Abstract
Lifting during manual material handling is a major cause of low-back pain (LBP). As an important risk factor that directly influences the risk of LBP, the Load vertical location (LVL) during lifting needs to be measured and controlled. However, existing solutions for LVL measurement are inefficient, inaccurate, and impractical for real-world workplace environments. To address these problems, an unobtrusive wearable system, including smart insoles and smart wristbands, was proposed to measure LVL accurately in workplace environments. Different from traditional methods which rely on Inertial Measurement Unit (IMU) and suffer from integral drifting errors over time, a novel barometer-based LVL measurement method was proposed in this study. To correct the environment-induced LVL measurement errors in the barometer-based method, a novel Known Vertical Location Update (KVLU) method was proposed. This method calibrates the measured LVL using a known wrist vertical location at known postures during frequently used daily activities such as standing and walking. The proposed wearable system achieved a mean absolute error (MAE) of 5.71 cm in LVL measurement. This result indicates that the proposed system has the potential to reliably measure LVL and assess the risk of LBP in manual lifting tasks.
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