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Wearable and Implantable Body Sensor Networks, International Workshop on (2009)
Berkeley, CA
June 3, 2009 to June 5, 2009
ISBN: 978-0-7695-3644-6
pp: 175-180
This paper investigates an ear worn sensor for the development of a gait analysis framework. Instead of explicitly defining gait features that indicate injury or impairment, an automatic method of feature extraction and selection is proposed. The proposed framework uses multi-resolution wavelet analysis and margin based feature selection. It was validated on three datasets; the first simulating a leg injury, the second simulating abdominal impairment that could result from surgery or injury and the third is a dataset collected from a patient during recovery from leg injury. The method shows a clear distinction of gait between injured and normal walking. It also illustrates the fact that using source separation before pattern classification can significantly improve the proposed gait analysis framework.
wearable sensors, gait, wavelet analysis

O. Aziz, B. Lo, G. Yang and L. Atallah, "Detecting Walking Gait Impairment with an Ear-worn Sensor," 2009 Sixth International Workshop on Wearable & Implantable Body Sensor Networks Conference (BSN 2009)(BSN), Berkeley, CA, 2009, pp. 175-180.
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