Physical activity discrimination improvement using accelerometers and wireless sensor network localization

Research output: Contribution to journalArticle

Abstract

Automating documentation of physical activity data (e.g., duration and speed of walking or propelling a wheelchair) into the electronic medical record (EMR) offers promise for improving efficiency of documentation and understanding of best practices in the rehabilitation and home health settings. Commercially available devices which could be used to automate documentation of physical activities are either cumbersome to wear or lack the specificity required to differentiate activities. We have designed a novel system to differentiate and quantify physical activities, using inexpensive accelerometer-based biomechanical data technology and wireless sensor networks, a technology combination that has not been used in a rehabilitation setting to date. As a first step, a feasibility study was performed where 14 healthy young adults (mean age = 22.6 ± 2.5 years, mean height = 173 ± 10.0 cm, mean mass = 70.7 ± 11.3 kg) carried out eight different activities while wearing a biaxial accelerometer sensor. Activities were performed at each participant's self-selected pace during a single testing session in a controlled environment. Linear discriminant analysis was performed by extracting spectral parameters from the subjects' accelerometer patterns. It is shown that physical activity classification alone results in an average accuracy of 49.5%, but when combined with rule-based constraints using a wireless sensor network with localization capabilities in an in silico simulated room, accuracy improves to 99.3%. When fully implemented, our technology package is expected to improve goal setting, treatment interventions and patient outcomes by enhancing clinicians' understanding of patients' physical performance within a day and across the rehabilitation program.

Original languageEnglish (US)
Pages (from-to)243-250
Number of pages8
JournalBiomedical Sciences Instrumentation
Volume49
StatePublished - Jan 1 2013

Fingerprint

Documentation
Exercise
Rehabilitation
Wireless Technology
Technology
Controlled Environment
Wheelchairs
Electronic Health Records
Feasibility Studies
Discriminant Analysis
Practice Guidelines
Computer Simulation
Young Adult
Equipment and Supplies
Health
Therapeutics
Walking Speed

Keywords

  • Physical activity classification
  • Rehabilitation
  • Spectral analysis
  • Wireless sensor networks

ASJC Scopus subject areas

  • Biophysics
  • Medical Laboratory Technology

Cite this

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abstract = "Automating documentation of physical activity data (e.g., duration and speed of walking or propelling a wheelchair) into the electronic medical record (EMR) offers promise for improving efficiency of documentation and understanding of best practices in the rehabilitation and home health settings. Commercially available devices which could be used to automate documentation of physical activities are either cumbersome to wear or lack the specificity required to differentiate activities. We have designed a novel system to differentiate and quantify physical activities, using inexpensive accelerometer-based biomechanical data technology and wireless sensor networks, a technology combination that has not been used in a rehabilitation setting to date. As a first step, a feasibility study was performed where 14 healthy young adults (mean age = 22.6 ± 2.5 years, mean height = 173 ± 10.0 cm, mean mass = 70.7 ± 11.3 kg) carried out eight different activities while wearing a biaxial accelerometer sensor. Activities were performed at each participant's self-selected pace during a single testing session in a controlled environment. Linear discriminant analysis was performed by extracting spectral parameters from the subjects' accelerometer patterns. It is shown that physical activity classification alone results in an average accuracy of 49.5{\%}, but when combined with rule-based constraints using a wireless sensor network with localization capabilities in an in silico simulated room, accuracy improves to 99.3{\%}. When fully implemented, our technology package is expected to improve goal setting, treatment interventions and patient outcomes by enhancing clinicians' understanding of patients' physical performance within a day and across the rehabilitation program.",
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