Robust Automated Step Extraction From Time-Series Contact Force Data Using the PDShoe

Kyle N. Winfree, Ingrid Pretzer-Aboff, Sunil K. Agrawal

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper presents a method of stride identification, extraction, and analysis of data sets of time-series contact force data for ambulating subjects both with and without Parkinson's disease (PD). This method has been made robust with the use of seeded K-Means clustering, fast Fourier transformation (FFT) spectral analysis, and minimum window size rejection. These methods combine to produce well selected strides of active walking data. We are able to calculate quality of walking measures of stride duration, stance duration (as percent of gait cycle - %GC), swing duration (%GC), time to maximum heel force (%GC), time to maximum toe force (%GC), time spent in heel contact (%GC), and time spent in toe contact (%GC).

Original languageEnglish (US)
Article number6990763
Pages (from-to)1012-1019
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number6
StatePublished - Nov 2015
Externally publishedYes


  • Foot
  • Footwear
  • Force
  • Force measurement
  • Resistors
  • Sensors
  • Time measurement

ASJC Scopus subject areas

  • Rehabilitation
  • General Neuroscience
  • Internal Medicine
  • Biomedical Engineering


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