NDLI: HMM-Fuzzy Model for Recognition of Gait Changes due to Trip-related Falls

Content Provider	IEEE Xplore Digital Library
Author	Hassan, M.R. Begg, R. Taylor, S. Kumar, D.K.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci. & Software Eng., Melbourne Univ., Carlton, Vic. (Hassan, M.R.)
Abstract	This paper reports the use of HMM-based fuzzy rules generation for identifying the differences in gait between people with tendencies to fall and healthy people. This work is built on the work reported earlier by the authors where fuzzy rules were successfully applied in gait pattern recognition. This paper reports the hybridization of HMM with fuzzy logic for improving the recognition accuracy. Gait features were extracted from minimum foot clearance (MFC) data that was collected during continuous walking on a treadmill from 20 elderly subjects, 10 healthy and 10 with reported balance problem and history of falls. The input feature space was divided into a number of groups based on HMM generated log-likelihood values, and consequently each group was applied to construct a new fuzzy rule. Gradient descent method was used to optimize the parameters of the generated rules. These were then applied to recognize differences in the gait in subjects with trip-related falls history. The model's performance was evaluated using a cross- validation protocol applied on the training and testing data. The HMM-Fuzzy model outperformed the Fuzzy-based gait recognition as reflected both in the receiver operating characteristics (ROC) results as well as absolute percentage accuracy
Sponsorship	IEEE EMB
Starting Page	1216
Ending Page	1219
File Size	280982
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.259804
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models History Pattern recognition Fuzzy logic Feature extraction Data mining Foot Legged locomotion Senior citizens Optimization methods
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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