Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality

TL;DR

This paper introduces a novel machine learning approach for automatic error detection in human motor performance during virtual reality coaching, enabling detailed and rapid feedback for sports and rehabilitation exercises.

Contribution

It presents a new method combining Dynamic Time Warping, Random Forests, and Support Vector Machines for classifying subtle movement errors more accurately and efficiently than existing techniques.

Findings

Outperforms 1-Nearest Neighbor with DTW in accuracy

Reduces computational cost significantly

Provides detailed error classification for virtual coaching

Abstract

In the context of fitness coaching or for rehabilitation purposes, the motor actions of a human participant must be observed and analyzed for errors in order to provide effective feedback. This task is normally carried out by human coaches, and it needs to be solved automatically in technical applications that are to provide automatic coaching (e.g. training environments in VR). However, most coaching systems only provide coarse information on movement quality, such as a scalar value per body part that describes the overall deviation from the correct movement. Further, they are often limited to static body postures or rather simple movements of single body parts. While there are many approaches to distinguish between different types of movements (e.g., between walking and jumping), the detection of more subtle errors in a motor performance is less investigated. We propose a novel approach to classify errors in sports or rehabilitation exercises such that feedback can be delivered in a rapid and detailed manner: Homogeneous sub-sequences of exercises are first temporally aligned via Dynamic Time Warping. Next, we extract a feature vector from the aligned sequences, which serves as a basis for feature selection using Random Forests. The selected features are used as input for Support Vector Machines, which finally classify the movement errors. We compare our algorithm to a well established state-of-the-art approach in time series classification, 1-Nearest Neighbor combined with Dynamic Time Warping, and show our algorithm's superiority regarding classification quality as well as computational cost.