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Automatically detecting asymmetric running using time and frequency domain features

Mitchell, Edmond and Ahmadi, Amin and O'Connor, Noel E. and Richter, Chris and Farrell, Evan and Moran, Kieran (2015) Automatically detecting asymmetric running using time and frequency domain features. In: 12th Annual Body Sensor Networks Conference 2015, 9-12 June 2015, MIT Media Lab, Boston.

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Abstract

Human motion analysis technologies have been widely employed to identify injury determining factors and provide objective and quantitative feedback to athletes to help prevent injury. However, most of these technologies are: expensive, restricted to laboratory environments, and can require significant post processing. This reduces their ecological validity, adoption and usefulness. In this paper, we present a novel wearable inertial sensor framework to accurately distinguish between symmetrical and asymmetrical running patterns in an unconstrained environment. The framework can automatically classify symmetry/asymmetry using Short Time Fourier Transform (STFT) and other time domain features in conjunction with a customized Random Forest classifier. The accuracy of the designed framework is up to 94% using 3-D accelerometer and 3-D gyroscope data from a sensor node attached on the upper back of a subject. The upper back inertial sensors data were then down-sampled by a factor of 4 to simulate utilizing low-cost inertial sensors whilst also facilitating a decrease of the computational cost to achieve near real-time application. We conclude that the proposed framework can potentially pave the way for employing low-cost sensors, such as those used in smartphones, attached on the upper back to provide injury related and performance feedback in real-time in unconstrained environments.

Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
Engineering > Signal processing
Engineering > Biomedical engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:20576
Deposited On:19 Jun 2015 09:58 by Edmond Mitchell. Last Modified 08 Dec 2016 10:24

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