RT Conference Proceedings
T1 Assessing gait impairments based on auto-encoded patterns of mahalanobis distances from consecutive steps
A1 Muñoz Organero, Mario
A1 Davies, Richard
A1 Mawson, Sue
AB 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.
PB IOS Press
SN 978-1-61499-797-9 (Print)
SN 978-1-61499-798-6 (Online)
SN 0926-9630 (Print)
SN 1879-8365 (Online)
YR 2017
FD 2017
LK https://hdl.handle.net/10016/26515
UL https://hdl.handle.net/10016/26515
LA eng
NO Proceedings of: 14th conference of the Association for the Advancement of Assistive Technology in Europe (AAATE 2017), Sheffield (UK), 12-15th September 2017.
NO The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF).
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RD 17 jul. 2024