Research Repository

HMM-Fuzzy Model for Recognition of Gait Changes due to Trip-Related Falls

Hassan, Rafiul and Begg, Rezaul and Taylor, Simon B and Kumar, Dinesh K (2006) HMM-Fuzzy Model for Recognition of Gait Changes due to Trip-Related Falls. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, EMBS '06. IEEE, Piscataway, New Jersey, pp. 1216-1219.

Full text for this resource is not available from the Research Repository.

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.

Item Type: Book Section
ISBN: 1424400333 (print), 9781424400331 (print), 14244003303 (online)
Uncontrolled Keywords: fuzzy logic, gait analysis, hidden Markov models, pattern recognition, HMM generated log-likelihood values, HMM-fuzzy models, continuous walking, cross-validation protocol, fuzzy rules, gradient descent method, minimum foot clearance, receiver operating characteristics, treadmills, data mining, feature extraction, foot, feet, legged locomotion, optimisation methods, senior citizens, elderly
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
Depositing User: Yimin Zeng
Date Deposited: 11 Dec 2013 23:42
Last Modified: 11 Dec 2013 23:42
URI: http://vuir.vu.edu.au/id/eprint/21419
ePrint Statistics: View download statistics for this item
Citations in Scopus: 2 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar