Detection of Gait Phases Using Orient Specks for Mobile Clinical Gait Analysis

R.L. Evans, D.K. Arvind

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

Abstract / Description of output

This paper presents a hybrid method based on a feed-forward neural network (FNN) embedded in a hidden Markov model (HMM), for detecting phases in a gait cycle, based on data from inertial sensors attached to the lower body. The method was validated against the ground truth obtained concurrently from a Vicon optical motion capture system for five volunteers. The method was characterised using metrics such as sensitivity and specificity for sensor placements, and gait analysis. The results demonstrate that the proposed method is accurate within 23 milliseconds with the added advantages of mobility afforded by wireless sensors and the flexibility of the classification method.
Original languageEnglish
Title of host publicationWearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages149-154
Number of pages6
ISBN (Print)978-1-4799-4932-8
DOIs
Publication statusPublished - Jun 2014

Keywords / Materials (for Non-textual outputs)

  • biomedical telemetry
  • body sensor networks
  • feedforward neural nets
  • gait analysis
  • hidden Markov models
  • medical signal processing
  • portable instruments
  • signal classification
  • telemedicine
  • FNN embedding
  • HMM
  • Orient specks
  • Vicon optical motion capture system
  • classification method flexibility
  • feedforward neural network
  • gait cycle
  • gait phase detection
  • hidden Markov model
  • hybrid method
  • lower body inertial sensor
  • mobile clinical gait analysis
  • mobility
  • sensitivity metrics
  • sensor placements
  • specificity metrics
  • wireless sensors
  • Hidden Markov models
  • Neural networks
  • Phase detection
  • Sensitivity
  • Sensitivity and specificity
  • Sensors
  • Training
  • feed-forward neural network
  • inertial sensors
  • mobile gait analysis

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