Document Detail

Development of gait segmentation methods for wearable foot pressure sensors.
MedLine Citation:
PMID:  23367055     Owner:  NLM     Status:  MEDLINE    
We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.
S Crea; S M M De Rossi; M Donati; P Reberšek; D Novak; N Vitiello; T Lenzi; J Podobnik; M Munih; M C Carrozza
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2012     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-31     Completed Date:  2013-08-01     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  5018-21     Citation Subset:  IM    
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MeSH Terms
Diagnosis, Computer-Assisted / instrumentation,  methods
Equipment Design
Equipment Failure Analysis
Foot / physiology*
Gait / physiology*
Manometry / instrumentation*,  methods
Monitoring, Ambulatory / instrumentation*,  methods*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Transducers, Pressure*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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