Development and validation of an insole based wearable system for gait and activity monitoring

dc.contributorMelanson, Edward
dc.contributorBishop, Philip A.
dc.contributorHu, Fei
dc.contributorFonseca, Daniel J.
dc.contributor.advisorSazonov, Edward
dc.contributor.authorHegde, Nagaraj
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-07-28T14:11:58Z
dc.date.available2017-07-28T14:11:58Z
dc.date.issued2017
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractFootwear based wearable sensors are used in applications such as activity monitoring, gait analysis, post-stroke rehabilitation, body weight measurements, and energy expenditure estimation. Such wearable sensors typically required the modification or alteration of the shoe, which is not typically feasible for large populations without the direct involvement of shoe manufacturers. This dissertation presents an insole-based wearable sensor (SmartStep) that has its electronics fully embedded into a generic insole, which can be used with a large variety of shoes and, thus, resolves the need for shoe modification. The SmartStep is an always-on electronic device that comprises of inertial sensors and resistive pressure sensors implemented around a system on chip with Bluetooth low energy (BLE) connectivity. The dissertation explains a novel Android application methodology to interface multiple BLE sensors. As a part of the research, SmartStep’s predecessor, SmartShoe, was tested in an application scenario of gait monitoring of children with cerebral palsy. Novel signal processing algorithms were developed to deal with the effect of orthotics on pressure sensors. Machine learning models were used to recognize 3 major activities (sitting, standing and walking) with greater than 95% accuracy. The gait parameter estimation resulted in average error less than 6% across a range of temporal gait parameters. The SmartStep device was utilized in a study for recognizing activities of daily living (ADL) in adults. An upper-body, wrist-worn sensor was also utilized in the same study to compare the effectiveness of upper and lower body-worn sensors in recognizing ADL. The indirect calorimetry data from a portable metabolic system (Cosmed K4b), collected in the same study, were used to develop energy expenditure (EE) estimation models. Two different sets of steady state activity branched models were developed, along with models for transition activities. The EE models were validated on the data from a room calorimeter. SmartStep was 90% accurate in recognizing a broad set of nine ADLs, including household activities; while having 6% validation error in estimating total energy expenditure. These results suggest that SmartStep is accurate in recognizing multiple activities of daily living and in energy expenditure estimation.en_US
dc.format.extent187 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0002582
dc.identifier.otherHegde_alatus_0004D_12989
dc.identifier.urihttp://ir.ua.edu/handle/123456789/3179
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectElectrical engineering
dc.subjectBiomedical engineering
dc.subjectComputer engineering
dc.titleDevelopment and validation of an insole based wearable system for gait and activity monitoringen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Electrical and Computer Engineering
etdms.degree.disciplineElectrical and Computer Engineering
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.
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