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

2025-02-13

AI Summary

To address the challenge of real-time safety monitoring for human arm operations in human–robot collaborative manufacturing, this paper proposes a low-latency dynamic risk assessment method based on wrist-worn inertial measurement units (IMUs). The method integrates IMU sensing, impedance-based modeling, frequency-domain analysis, and probabilistic safety evaluation, with optimized embedded real-time inference. Key contributions include: (i) the first adaptation of a physics-informed spring–damper–mass model to wrist kinematics; (ii) an impedance-driven probabilistic safety decision mechanism; and (iii) frequency-domain calibration of quantifiable safety thresholds. Evaluated across three representative tasks—tool operation, visual inspection, and pick-and-place—the approach achieves a false positive rate below 3.2% and end-to-end inference latency under 8 ms, enabling efficient edge deployment.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Real-time safety monitoring in human-robot collaboration
• Optimized wrist motion safety model
• Impedance-based probabilistic safety assessment

Innovation

• IMU sensors for safety monitoring
• Spring-damper-mass predictive model
• Impedance-based safety assessment