AI-based task classification with pressure insoles for occupational safety
Institute of Electrical and Electronics Engineers (IEEE)
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 over 82%, using ten-fold cross-validation, for a time window of 5 seconds, showing the potential for task classification in edge-AI applications in smart manufacturing environments. A re-analysis focused on the five most influential features obtained 83% accuracy. The combination of random forest and in-depth feature analysis (SHAP) provided insights into the importance of features and the impact of their value on each task class. Such an understanding can aid in reducing misclassifications for health and safety purposes and can aid in the design of pressure insoles that are optimized for impactful features. The accuracy achieved is comparable to similar task classification studies but with the benefit of added explainability, which increases transparency and, thereby, trust in the classifier decisions.
Human Activity Recognition , Machine learning , Wearable sensors
O’Sullivan, P., Menolotto, M., Visentin, A., O’Flynn, B. and Komaris, D.-S. (2024) 'AI-based task classification with pressure insoles for occupational safety', IEEE Access, doi: https://doi.org/10.1109/ACCESS.2024.3361754