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Autoregressive-moving-average hidden Markov model for vision-based fall prediction—An application for walker robot.

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the ac...

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Published in:Assistive Technology Vol. 29; no. 1; pp. 19 - 28
Main Authors: Taghvaei, Sajjad, Jahanandish, Mohammad Hasan, Kosuge, Kazuhiro
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Taylor & Francis Ltd Spring2017
Online Access:View this record in EBSCOhost
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      jtl: Assistive Technology
      issn: 10400435
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      dt: Spring2017
      vid: 29
      iid: 1
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/10400435.2016.1174178
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        atl: Autoregressive-moving-average hidden Markov model for vision-based fall prediction—An application for walker robot.
      aug:
        au:
          Taghvaei, Sajjad
          Jahanandish, Mohammad Hasan
          Kosuge, Kazuhiro
        affil: School of Mechanical Engineering, Shiraz University, Shiraz, Iran
      sug:
        subj:
          Accidental Falls Evaluation
          Robotics
          Vision
          Hidden Markov Models
          Aged
          Aging
          Algorithms
          Human
          Walking
          Patient Safety
          Assistive Technology
          Accidental Falls Prevention and Control
          Models, Statistical
          Data Analysis Software
          Male
          Female
          Aged: 65+ years
          Male
          Female
      ab: Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid “system identification-machine learning” approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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