Safety Evaluation of Human Arm Operations Using IMU Sensors with a Spring-Damper-Mass Predictive Model

TL;DR

This paper introduces a real-time safety monitoring system for human-robot collaboration using wrist-mounted IMU sensors and a spring-damper-mass predictive model, validated across various manufacturing tasks.

Contribution

It adapts a spring-damper-mass model for wrist motion to enhance probabilistic safety assessment in collaborative manufacturing environments.

Findings

Robust safety performance across diverse tasks

Efficient computation with optimized parameters

Quantitative safety thresholds established

Abstract

This paper presents a novel approach to real-time safety monitoring in human-robot collaborative manufacturing environments through a wrist-mounted Inertial Measurement Unit (IMU) system integrated with a Predictive Safety Model (PSM). The proposed system extends previous PSM implementations through the adaptation of a spring-damper-mass model specifically optimized for wrist motions, employing probabilistic safety assessment through impedance-based computations. We analyze our proposed impedance-based safety approach with frequency domain methods, establishing quantitative safety thresholds through comprehensive comparative analysis. Experimental validation across three manufacturing tasks - tool manipulation, visual inspection, and pick-and-place operations. Results show robust performance across diverse manufacturing scenarios while maintaining computational efficiency through optimized parameter selection. This work establishes a foundation for future developments in adaptive risk assessment in real-time for human-robot collaborative manufacturing environments.