Recognition of hearing needs from body and eye movements to improve hearing instruments
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Dogsperate escape: a demonstration of real-time BSN-based game control with e-AR sensor
KICSS'10 Proceedings of the 5th international conference on Knowledge, information, and creativity support systems
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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.