Authors:
Siddhartha Khandelwal
and
Nicholas Wickström
Affiliation:
Halmstad University, Sweden
Keyword(s):
Gait Event Detection, Wavelet Analysis, Accelerometers, Outdoor Walking, Continuous Wavelet Transform.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Detection and Identification
;
Devices
;
Health Information Systems
;
Human-Computer Interaction
;
Physiological Computing Systems
;
Wavelet Transform
;
Wearable Sensors and Systems
Abstract:
Many gait analysis applications involve long-term or continuous monitoring which require gait measurements
to be taken outdoors. Wearable inertial sensors like accelerometers have become popular for such applications
as they are miniature, low-powered and inexpensive but with the drawback that they are prone to noise and require
robust algorithms for precise identification of gait events. However, most gait event detection algorithms
have been developed by simulating physical world environments inside controlled laboratories. In this paper,
we propose a novel algorithm that robustly and efficiently identifies gait events from accelerometer signals
collected during both, indoor and outdoor walking of healthy subjects. The proposed method makes adept
use of prior knowledge of walking gait characteristics, referred to as expert knowledge, in conjunction with
continuous wavelet transform analysis to detect gait events of heel strike and toe off. It was observed that in
comparison to indo
or, the outdoor walking acceleration signals were of poorer quality and highly corrupted
with noise. The proposed algorithm presents an automated way to effectively analyze such noisy signals in
order to identify gait events.
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