TY - JOUR
T1 - Autoregressive-moving-average hidden Markov model for vision-based fall prediction—An application for walker robot
AU - Taghvaei, Sajjad
AU - Jahanandish, Mohammad Hasan
AU - Kosuge, Kazuhiro
N1 - Publisher Copyright:
© 2017 RESNA.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/1/2
Y1 - 2017/1/2
N2 - 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%.
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%.
KW - autoregressive-moving-average (ARMA) model
KW - hidden Markov model
KW - human fall prediction
KW - walking assistive robot
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U2 - 10.1080/10400435.2016.1174178
DO - 10.1080/10400435.2016.1174178
M3 - Article
C2 - 27450279
AN - SCOPUS:84991255663
VL - 29
SP - 19
EP - 27
JO - Assistive Technology
JF - Assistive Technology
SN - 1040-0435
IS - 1
ER -