Assessing motor performance with PCA

Hammerla, Nils Y; Pl�tz, Thomas; Andras, Peter; Olivier, Patrick

Abstract

Information about the motor performance, i.e. how well an activity is performed, is valuable information for a variety of novel applications in Activity Recognition (AR). Its assessment represents a significant challenge, as requirements depend on the specific application. We develop an approach to quantify one aspect that many domains share – the efficiency of motion – that has implications for signals from body-worn or pervasive sensors, as it influences the inherent complexity of the recorded multi-variate time-series. Based on the energy distribution in PCA we infer a single, normalised metric that is intimately linked to signal complexity and allows comparison of (subject-specific) time-series. We evaluate the approach on artificially distorted signals and apply it to a simple kitchen task to show its applicability to real-life data streams.

Citation

Hammerla, N. Y., Plötz, T., Andras, P., & Olivier, P. (2011, June). Assessing motor performance with PCA. Presented at International Workshop on Frontiers in Activity Recognition using Pervasive Sensing (in conjunction with Pervasive), Newcastle

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International Workshop on Frontiers in Activity Recognition using Pervasive Sensing (in conjunction with Pervasive)
• Start Date: Jun 12, 2011
• Publication Date: 2011
• Deposit Date: Nov 17, 2021
• Pages: 18-23
• Book Title: Proceedings of the International Workshop on Frontiers in Activity Recognition using Pervasive Sensing
• Public URL: http://researchrepository.napier.ac.uk/Output/2809152