AI-Based Task Classification With Pressure Insoles for Occupational Safety

Abstract

Pressure insoles allow for the collection of real time pressure data inside and outside a laboratory setting as they are non-intrusive and can be simply integrated into industrial environments for occupational health and safety monitoring purposes. Activity detection is important for the safety and wellbeing of workers, and the present study aims to employ pressure insoles to detect the type of industry-related task an individual is performing by using random forest, an artificial intelligence-based classification technique. Twenty subjects wore loadsol® pressure insoles and performed five specific tasks associated with a typical workflow: standing, walking, pick and place, assembly, and manual handling. For each activity, statistical and morphological features were extracted to create a training dataset. The classifier performed with an accuracy of 82%, and a re-analysis focusing on the five most influential features resulted in 83% accuracy. These accuracies are comparable to similar task classification studies but with the benefit of added explainability, which increases transparency and, thereby, trust in the classifier decisions. The combination of random forest and in-depth feature analysis (SHAP) provided insights into the importance of certain features and the impact of their value on the classification of each task. The insights obtained from these methods can aid in the design of pressure insoles that are optimized for the extraction of impactful features and the prevention of work-related musculoskeletal disorders in Industry 4.0 operators.

Keywords

• Human activity recognition
• pressure insoles
• explainable AI
• industry 4.0