H H Savelberg1, A L de Lange. 1. Faculty of Health Sciences, Department of Movement Sciences, Maastricht University, P.O. Box 616, 6200, Maastricht, The Netherlands. hans.savelberg@bw.unimaas.nl
Abstract
OBJECTIVE: In this study it was investigated whether an artificial neural network can be used to determine the horizontal, fore-aft component of the ground reaction force from insole pressure patterns. DESIGN: An artificial neural network was applied to map insole pressures and ground reaction forces. METHOD: To train an artificial neural network insole pressure patterns and ground reaction force data were simultaneously determined for a wide range of different speeds (0.9-2.3 m s(-1)) for five subjects. Both intrasubject and intersubject generalizability were evaluated. RESULTS: At the intrasubject level generalizability was good when the speed for which the force was to be predicted was within the range of speeds from which data were used to train the network. Besides in some cases, generalizability to a condition outside the range of training conditions could be demonstrated. At the intersubject level the quality of generalization differed widely over subjects, from poor to good. CONCLUSIONS: It was found that an artificial neural network is able to map the relationship between insole pressure patterns and the fore-aft component of the ground reaction force. RELEVANCE: Good intrasubject generalization of 'knowledge' obtained by an artificial neural network will allow the assessment of the fore-aft component of ground reaction force in condition that cannot be evaluated with force plates, e.g. activities of daily living or real sport situations. Additionally, intersubject generalization will allow shear-force recordings in subjects that are not able to complete a great number of runs to acquire enough force-plate hits.
OBJECTIVE: In this study it was investigated whether an artificial neural network can be used to determine the horizontal, fore-aft component of the ground reaction force from insole pressure patterns. DESIGN: An artificial neural network was applied to map insole pressures and ground reaction forces. METHOD: To train an artificial neural network insole pressure patterns and ground reaction force data were simultaneously determined for a wide range of different speeds (0.9-2.3 m s(-1)) for five subjects. Both intrasubject and intersubject generalizability were evaluated. RESULTS: At the intrasubject level generalizability was good when the speed for which the force was to be predicted was within the range of speeds from which data were used to train the network. Besides in some cases, generalizability to a condition outside the range of training conditions could be demonstrated. At the intersubject level the quality of generalization differed widely over subjects, from poor to good. CONCLUSIONS: It was found that an artificial neural network is able to map the relationship between insole pressure patterns and the fore-aft component of the ground reaction force. RELEVANCE: Good intrasubject generalization of 'knowledge' obtained by an artificial neural network will allow the assessment of the fore-aft component of ground reaction force in condition that cannot be evaluated with force plates, e.g. activities of daily living or real sport situations. Additionally, intersubject generalization will allow shear-force recordings in subjects that are not able to complete a great number of runs to acquire enough force-plate hits.
Authors: M Iosa; G Morone; A Fusco; M Bragoni; P Coiro; M Multari; V Venturiero; D De Angelis; L Pratesi; S Paolucci Journal: Stroke Res Treat Date: 2012-12-13