A Pre-Fitting Mixed Reality System for Myosignals Evaluation and Optimal Control Training for Hand Prostheses

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Abstract
Nowadays, upper limb prosthesis acceptance remains low due to ineffective control techniques and the relative training methods. However, an engaging training method applied during early prosthetic fitting seems to have a positive impact on acceptance. To this aim, in this paper we present a Mixed Reality (MR) pre-fitting training system based on Microsoft HoloLens2, designed for users of the Hannes prosthetic hand. The system allows individuals with varying stump morphologies to control a holographic Hannes hand through a low-latency interface, replicating the physical architecture and motion of the real device in an immersive, portable environment. This pilot study presents the preliminary evaluation of the MR framework conducted with two limb difference participants (one naïve user and one experienced user in myoelectric prosthesis control) performing a novel bimanual Target Achievement Control (TAC) test. Their performance in controlling the real Hannes device was subsequently compared to a reference group of eight transradial limb difference individuals. Both MR-trained participants showed faster learning and better adaptation to the real prosthesis than the reference group. These preliminary findings suggest that the proposed MR framework, combined with an MR bimanual TAC test, may improve learning and user experience, ultimately contributing to lower prosthesis abandonment rates.
Funding Information
  • INAIL-IIT (PR19-PAS-P1)
  • INAIL-IIT (PR23-PAS-P1)

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