Publication: Assessing gait impairments based on auto-encoded patterns of mahalanobis distances from consecutive steps
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ISSN: 0926-9630 (Print)
ISSN: 1879-8365 (Online)
ISBN: 978-1-61499-797-9 (Print)
ISBN: 978-1-61499-798-6 (Online)
UXXI: CC/0000027441
Publication date
2017
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IOS Press
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Abstract
Insole pressure sensors capture the force distribution patterns during the stance phase while walking. By comparing patterns obtained from healthy individuals to patients suffering different medical conditions based on a given similarity measure, automatic impairment indexes can be computed in order to help in applications such as rehabilitation. This paper uses the data sensed from insole pressure sensors for a group of healthy controls to train an auto-encoder using patterns of stochastic distances in series of consecutive steps while walking at normal speeds. Two experiment groups are compared to the healthy control group: a group of patients suffering knee pain and a group of post-stroke survivors. The Mahalanobis distance is computed for every single step by each participant compared to the entire dataset sensed from healthy controls. The computed distances for consecutive steps are fed into the previously trained autoencoder and the average error is used to assess how close the walking segment is to the autogenerated model from healthy controls. The results show that automatic distortion indexes can be used to assess each participant as compared to normal patterns computed from healthy controls. The stochastic distances observed for the group of stroke survivors are bigger than those for the people with knee pain.
Description
Proceedings of: 14th conference of the Association for the Advancement of Assistive Technology in Europe (AAATE 2017), Sheffield (UK), 12-15th September 2017.
Keywords
Gait impairment, Pattern analysis, Modelling, Mahalanobis distance
Bibliographic citation
Peter Cudd, Luc de Witte (eds.) (2014). Harnessing the power of technology to improve lives. (Studies in Health Technology and Informatics, 242). Amsterdam: IOS Press, 2017. Pp. 733-740.