Minimization of number of gait trials for tripping probability tests using artificial neural networks

Minimum toe clearance ( M T C ) data has been used to quantify the probability of tripping (PT) during gait (Best, Begg and James, 1999). MTC data collection is very time consuming and there has been no research conducted to devise a methodology that has the potential to predict long-term histogram characteristics of MTC data (e.g. mean, standard deviation, skewness and kurtosis), based on the characteristics of MTC data collected from fewer gait trials. The aim of this study is to apply a novel technology, artificial neural network (ANN), to predict stabilized MTC characteristics (mean, M; standard deviation, SD; skewness, S; kurtosis, K) from relatively fewer gait trials. data of 24 subjects (age range: 19-79 years) were collected during normal walking on a treadmill for 30 minutes. Thirty-one back-propagation neural networks (BPNs) were developed using various combinations of input variables to predict 30-minute MTC characteristics. The network performance was evaluated using the percentage of error (POE) of the test results (i.e. difference between desired and predicted results divided by the desired result).

Name dc:type.qualificationname

other

Level dc:type.qualificationlevel

rmaster

Grantor dc:publisher.institution

Victoria University of Technology

Year dc:date.issued

2001

Author dc:creator

• Cai, Jing

• 0801 Artificial Intelligence and Image Processing
• 1106 Human Movement and Sports Science
• School of Sport and Exercise Science

Language dc:language

en