AI-Driven Arm Movement Estimation for Sustainable Wearable Systems in Industry 4.0

Emanuel Muntean, Monica Leba () and Andreea Cristina Ionica
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Emanuel Muntean: Doctoral School, University of Petroșani, 332006 Petrosani, Romania
Monica Leba: System Control and Computer Engineering Department, University of Petroșani, 332006 Petrosani, Romania
Andreea Cristina Ionica: Management and Industrial Engineering Department, University of Petroșani, 332006 Petrosani, Romania

Sustainability, 2025, vol. 17, issue 14, 1-18

Abstract: In an era defined by rapid technological advancements, the intersection of artificial intelligence and industrial innovation has garnered significant attention from both academic and industry stakeholders. The emergence of Industry 4.0, characterized by the integration of cyber–physical systems, the Internet of Things, and smart manufacturing, demands the evolution of operational methodologies to ensure processes’ sustainability. One area of focus is the development of wearable systems that utilize artificial intelligence for the estimation of arm movements, which can enhance the ergonomics and efficiency of labor-intensive tasks. This study proposes a Random Forest-based regression model to estimate upper arm kinematics using only shoulder orientation data, reducing the need for multiple sensors and thereby lowering hardware complexity and energy demands. The model was trained on biomechanical data collected via a minimal three-IMU wearable configuration and demonstrated high predictive performance across all motion axes, achieving R 2 > 0.99 and low RMSE scores on training (1.14, 0.71, and 0.73), test (3.37, 1.97, and 2.04), and unseen datasets (2.77, 0.78, and 0.63). Statistical analysis confirmed strong biomechanical coupling between shoulder and upper arm motion, justifying the feasibility of a simplified sensor approach. The findings highlight the relevance of our method for sustainable wearable technology design and its potential applications in rehabilitation robotics, industrial exoskeletons, and human–robot collaboration systems.

Keywords: sensor optimization; human–robot collaboration; random forest regression; upper limb kinematics; motion prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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