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Adjusting to amputation can often time be difficult for the body.Post-surgery, amputees have to wait for up to several months before receiving aproperly fitted prosthesis. In recent years, there has been a trend towardquantitative outcome measures. In this paper, we developed the augmentedreality (AR) version of one such measure, the Prosthetic Hand AssessmentMeasure (PHAM). The AR version of the PHAM - HoloPHAM, offers amputees theadvantage 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. Similarityof motion joint dynamics for reach in physical and AR space were compared, withexperiments conducted to illustrate how depth in AR is perceived. To show theeffectiveness and validity of our system for prosthesis training, we conducteda 10-day study with able-bodied subjects (N = 3) to see the effect thattraining on the HoloPHAM had on other established functional outcome measures.A washout phase of 5 days was incorporated to observe the effect withouttraining. Comparisons were made with standardized outcome metrics, along withthe progression of kinematic variability over time. Statistically significant(p<0.05) improvements were observed between pre- and post-training stages. Ourresults show that AR can be an effective tool for prosthesis training withpattern recognition systems, fostering motor learning for reaching movementtasks, and paving the possibility of replacing physical training.

Augmented Reality Prosthesis Training Setup for Motor Skill Enhancement