Personalized Electrical Muscle Stimulation for Precise Weight Perception in Virtual Reality: A Machine Learning Approach

Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, pp. 7414–7421

Abstract

Electrical muscle stimulation (EMS) can create compelling weight sensations in virtual reality by activating antagonist muscles, prompting users to exert effort against induced contractions. This experiment investigates personalizing EMS amplitudes to precisely make a 2 kg object feel 1 kg heavier. This task is challenging due to high individual variability in sensory, motor, and pain thresholds, as well as physical characteristics. We applied nine personalized EMS intensity levels to the triceps and extensor carpi ulnaris muscles of 75 participants, who then compared the perceived weight of the stimulated arm against a 3 kg reference. A machine learning model was trained on the collected data to predict the perceived weight difference from user-specific features. The best-performing XGBoost model achieved a classification accuracy of 77%. Feature analysis revealed that the applied EMS amplitude was the most influential predictor, supplemented by individual motor and pain thresholds, body mass index, and forearm length. This ML-based personalization is a significant advancement for creating adaptive haptic feedback, with applications in immersive VR, physical rehabilitation, and physiotherapy.

Authors 5

1. Apostolos Vrontos Aachen Institute of Industrial Engineering and Ergonomics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Industrial Engineering and Ergonomics,Aachen,Germany,52062

2. Reza Zolnouri Aachen Institute of Industrial Engineering and Ergonomics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Industrial Engineering and Ergonomics,Aachen,Germany,52062

3. Verena Nitsch Aachen Institute of Industrial Engineering and Ergonomics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Industrial Engineering and Ergonomics,Aachen,Germany,52062

4. A. Mertens Aachen Institute of Industrial Engineering and Ergonomics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Industrial Engineering and Ergonomics,Aachen,Germany,52062

5. Christopher Brandl Aachen Institute of Industrial Engineering and Ergonomics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Industrial Engineering and Ergonomics,Aachen,Germany,52062