Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

Philip Heller

Third Advisor

Fabio Di Troia

Keywords

Gamified physiotherapy, parallel processing, accelerometry data, anomaly detection, dynamic feedback, real-time motion analysis.

Abstract

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the bounds of low-latency inference and real-time parallel processing. This sensor-based augmented reality system has significant potential as a scalable foundation for individualized rehabilitation in clinical and home environments.

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Available for download on Monday, May 25, 2026

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