# Qualitative Assessment of Recurrent Human Motion

## TL;DR

This paper introduces a method for qualitative assessment of recurrent human motion using sensor data, enabling detection of malpositions and injury prevention with high accuracy in exercise classification.

## Contribution

It presents a novel approach combining segmentation and expressive features for qualitative motion assessment, validated on athletes performing various exercises.

## Key findings

- Achieved 100% accuracy in classifying six exercise types.
- Attained an average qualitative assessment success rate of 99.3%.
- Demonstrated effectiveness in real-world athletic scenarios.

## Abstract

Smartphone applications designed to track human motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. Commonly, they provide quantitative services, such as personalized training instructions or the counting of distances. But qualitative monitoring and assessment is still missing, e.g., to detect malpositions, to prevent injuries, or to optimize training success. We address this issue by presenting a concept for qualitative as well as generic assessment of recurrent human motion by processing multi-dimensional, continuous time series tracked with motion sensors. Therefore, our segmentation procedure extracts individual events of specific length and we propose expressive features to accomplish a qualitative motion assessment by supervised classification. We verified our approach within a comprehensive study encompassing 27 athletes undertaking different body weight exercises. We are able to recognize six different exercise types with a success rate of 100% and to assess them qualitatively with an average success rate of 99.3%.