Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation
Javier Conte Alcaraz, Sanam Moghaddamnia, J\"urgen Peissig

TL;DR
This paper introduces an Android-based system using smartphone sensors and machine learning to automatically assess gait rehabilitation progress with high accuracy, supporting digital healthcare and physiotherapy.
Contribution
It presents a novel autonomous gait quality metric leveraging smartphone sensors and real-time classification algorithms for rehabilitation assessment.
Findings
Achieved up to 100% classification accuracy between patients and controls.
Real-time data processing enables immediate feedback to users.
System supports data sharing with physicians for treatment monitoring.
Abstract
This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal…
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