Automatically detecting asymmetric running using time and frequency domain features

—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 ...

• Type: Journal
• Year: 2015
• Where: BSN
• Authors: Edmond Mitchell, Amin Ahmadi, Noel E. O'Connor, Chris Richter, Evan Farrell, Jennifer Kavanagh, Kieran Moran
• Keywords: BSN 2015, Sensor Networks