Smart shoes with adaptive sampling for outpatient daily health monitoring

Julie Vuong, Zhi Qiao, Wenlong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes an adaptive sampling algorithm for a pair of smart shoes for patients to use as a daily health monitoring device. The main hardware of the smart shoes features four pneumatic pressure sensors that measure ground contact forces (GCFs) and a global positioning system (GPS) to track the location of the user. The sampling rate of the pressure sensors and the GPS are changed based on the activity, either walking or sitting, detected from the user’s GCFs. An outdoor test was conducted to validate the adaptive sampling algorithm. The result was a 95% reduction in data size compared to sampling with the highest settings from all components. Collected GPS information from a subject’s morning activities was displayed onto a map to demonstrate how it could be used as contextual data for daily monitoring.

Original languageEnglish (US)
Title of host publicationFrontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791841037
DOIs
StatePublished - 2019
Event2019 Design of Medical Devices Conference, DMD 2019 - Minneapolis, United States
Duration: Apr 15 2019Apr 18 2019

Publication series

NameFrontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019

Conference

Conference2019 Design of Medical Devices Conference, DMD 2019
CountryUnited States
CityMinneapolis
Period4/15/194/18/19

Keywords

  • Adaptive sampling algorithm
  • Contextual data
  • Gait
  • Patient monitoring device
  • Wearable sensors

ASJC Scopus subject areas

  • Biomedical Engineering

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