# Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study

## TL;DR

This study develops a machine learning-based system that uses a single inertial sensor to automatically classify knee rehabilitation exercises, enabling effective remote monitoring and feedback for patients post-surgery.

## Contribution

It introduces a novel two-phase approach combining signal segmentation and classification for analyzing inertial sensor data in knee exercises.

## Key findings

- High accuracy in classifying exercises from clinical and healthy subjects
- Effective segmentation of exercise repetitions from continuous signals
- Promising results for real-world home rehabilitation monitoring

## Abstract

Inertial measurement units have the ability to accurately record the acceleration and angular velocity of human limb segments during discrete joint movements. These movements are commonly used in exercise rehabilitation programmes following orthopaedic surgery such as total knee replacement. This provides the potential for a biofeedback system with data mining technique for patients undertaking exercises at home without physician supervision. We propose to use machine learning techniques to automatically analyse inertial measurement unit data collected during these exercises, and then assess whether each repetition of the exercise was executed correctly or not. Our approach consists of two main phases: signal segmentation, and segment classification. Accurate pre-processing and feature extraction are paramount topics in order for the technique to work. In this paper, we present a classification method for unsupervised rehabilitation exercises, based on a segmentation process that extracts repetitions from a longer signal activity. The results obtained from experimental datasets of both clinical and healthy subjects, for a set of 4 knee exercises commonly used in rehabilitation, are very promising.