Augmented Reality Prosthesis Training Setup for Motor Skill Enhancement

TL;DR

This paper introduces HoloPHAM, an augmented reality system for prosthesis training that enables self-paced practice, demonstrating significant improvements in motor skills and potential to replace traditional physical training methods.

Contribution

The study presents a novel AR-based prosthesis training tool, HoloPHAM, validated through a 10-day study showing its effectiveness in improving motor skills in amputees.

Findings

Significant improvements in motor skills after AR training.

AR training with HoloPHAM is comparable to physical therapy outcomes.

Participants showed increased confidence and motor learning in AR environment.

Abstract

Adjusting to amputation can often time be difficult for the body. Post-surgery, amputees have to wait for up to several months before receiving a properly fitted prosthesis. In recent years, there has been a trend toward quantitative outcome measures. In this paper, we developed the augmented reality (AR) version of one such measure, the Prosthetic Hand Assessment Measure (PHAM). The AR version of the PHAM - HoloPHAM, offers amputees the advantage to train with pattern recognition, at their own time and convenience, pre- and post-prosthesis fitting. We provide a rigorous analysis of our system, focusing on its ability to simulate reach, grasp, and touch in AR. Similarity of motion joint dynamics for reach in physical and AR space were compared, with experiments conducted to illustrate how depth in AR is perceived. To show the effectiveness and validity of our system for prosthesis training, we conducted a 10-day study with able-bodied subjects (N = 3) to see the effect that training on the HoloPHAM had on other established functional outcome measures. A washout phase of 5 days was incorporated to observe the effect without training. Comparisons were made with standardized outcome metrics, along with the progression of kinematic variability over time. Statistically significant (p<0.05) improvements were observed between pre- and post-training stages. Our results show that AR can be an effective tool for prosthesis training with pattern recognition systems, fostering motor learning for reaching movement tasks, and paving the possibility of replacing physical training.