Preprint Clinical Feedback and Technology Selection of Game Based Dysphonic Rehabilitation Tool

TL;DR

This paper evaluates a game-based dysphonic rehabilitation tool through clinical feedback, comparing pitch estimation algorithms and generating benchmarks to inform technology choices for effective patient treatment.

Contribution

It introduces a novel assistive training tool using serious games on tablets and provides a comparative analysis of pitch estimation algorithms based on clinical data.

Findings

Clinical feedback identified key improvements for the tool.

Seven pitch estimation algorithms were evaluated and compared.

Benchmarks were generated to guide technology selection.

Abstract

This is the preprint version of our paper on 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth2015). An assistive training tool software for rehabilitation of dysphonic patients is evaluated according to the practical clinical feedback from the treatments. One stroke sufferer and one parkinson sufferer have provided earnest suggestions for the improvement of our tool software. The assistive tool employs a serious game as the attractive logic part, and running on the tablet with normal microphone as input device. Seven pitch estimation algorithms have been evaluated and compared with selected patients voice database. A series of benchmarks have been generated during the evaluation process for technology selection.