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

Musab Zubair Inamdar, Seyed Amir Tafrishi

TL;DR

The paper tackles real-time safety monitoring in human–robot collaboration during wrist movements by extending a Predictive Safety Model to wrist motion using a wrist-mounted IMU and a spring-damper-mass framework. Safety is quantified with an impedance-based error combining angular position and velocity deviations, plus a probabilistic safety map and frequency-domain analysis in a defined range to identify unsafe signatures. Key contributions include adapting the PSM to the wrist, developing a probabilistic motion distribution, introducing dual-priority safety criteria, and validating on tool fastening, visual inspection, and pick-and-place tasks with robust real-time performance. The approach offers computational efficiency suitable for real-time deployment and lays groundwork for adaptive risk assessment in Industry 4.0/5.0 human–robot collaboration.

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.

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

TL;DR

The paper tackles real-time safety monitoring in human–robot collaboration during wrist movements by extending a Predictive Safety Model to wrist motion using a wrist-mounted IMU and a spring-damper-mass framework. Safety is quantified with an impedance-based error combining angular position and velocity deviations, plus a probabilistic safety map and frequency-domain analysis in a defined range to identify unsafe signatures. Key contributions include adapting the PSM to the wrist, developing a probabilistic motion distribution, introducing dual-priority safety criteria, and validating on tool fastening, visual inspection, and pick-and-place tasks with robust real-time performance. The approach offers computational efficiency suitable for real-time deployment and lays groundwork for adaptive risk assessment in Industry 4.0/5.0 human–robot collaboration.

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.

Paper Structure

This paper contains 6 sections, 3 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Predictive Safety Model (PSM) Design For Arm
  3. Data Collection and Probability Distribution Design
  4. Safety Assessment Criteria
  5. Results and Discussion
  6. Conclusion

Figures (8)

  • Figure 1: The designed wireless IMU sensor attached on the wrist of the participant
  • Figure 2: The Predictive Safety Model (PSM) block diagram tafrishi2022psm.
  • Figure 3: Selected experiments run by the participant
  • Figure 4: The dataset of probability distribution documenting the participant’s motions
  • Figure 5: Fastening using tool experiment with normal motions of participant
  • ...and 3 more figures