View source for Publications:Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis

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{{PublicationSetupTemplate|Author=Siddhartha Khandelwal, Nicholas Wickström
|PID=688909
|Name=Khandelwal, Siddhartha [sidkha] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650]);Wickström, Nicholas (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650])
|Title=Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
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|HostPublication=7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014
|Conference=BIOSIGNALS 2013, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 
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|Year=2014
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|NBN=urn:nbn:se:hh:diva-24396
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|Keywords=Gait Event Detection;Gait Event Identification;Wavelet Analysis;Accelerometers;Outdoor Walking;Continuous Wavelet Transform;Inertial Sensors;Expert Knowledge
|Categories=Annan medicinteknik (20699)
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|Abstract=<p>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 indoor, 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.</p>
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|CreatedDate=2014-01-18
|PublicationDate=2014-01-18
|LastUpdated=2014-01-21
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:688909}}
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