Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer

Pau-Choo Chung, Yu Liang Hsu, Chun Yao Wang, Chien Wen Lin, Jeen-Shing Wang, Ming-Chyi Pai

Research output: Contribution to conferencePaper

17 Citations (Scopus)

Abstract

This paper presents an inertial-sensor-based wearable device and its associated stride detection algorithm to analyze gait information for patients with Alzheimer's disease (AD). The wearable gait analysis device is composed of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. To validate the effectiveness of the proposed device and algorithm, nine AD patients and three healthy controls were recruited to participate a gait analysis experiment. They were asked to mount the device on their foot and walk along a straight line of 40 meters at normal speed. The stride detection algorithm, consisting of procedures of data collection, signal preprocessing, and stride detection, has been developed for acquiring gait feature information from acceleration signals. The advantages of this wearable gait analysis device include the following: 1) It can be used anywhere without any external device, and 2) the stride detection algorithm can acquire gait feature information from acceleration signals automatically and effectively. Experimental results show that the AD patients exhibited a significantly shorter mean stride length and slower mean gait speed than those of the healthy controls. No significant differences in mean stride frequency and mean cadence were observed in the two groups. The variability in the percentage of the stance phase of the AD patients was slightly greater than that of the healthy controls. Based on the above results and discussions with physicians, we conclude that the proposed wearable gait analysis device is a promising tool for automatically analyzing gait information which can serve as indicators for early diagnosis of AD.

Original languageEnglish
Pages1323-1326
Number of pages4
DOIs
Publication statusPublished - 2012 Sep 28
Event2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012 - Seoul, Korea, Republic of
Duration: 2012 May 202012 May 23

Other

Other2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012
CountryKorea, Republic of
CitySeoul
Period12-05-2012-05-23

Fingerprint

Gait analysis
Accelerometers
Microcontrollers
Sensors
Experiments

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Chung, P-C., Hsu, Y. L., Wang, C. Y., Lin, C. W., Wang, J-S., & Pai, M-C. (2012). Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer. 1323-1326. Paper presented at 2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012, Seoul, Korea, Republic of. https://doi.org/10.1109/ISCAS.2012.6271484
Chung, Pau-Choo ; Hsu, Yu Liang ; Wang, Chun Yao ; Lin, Chien Wen ; Wang, Jeen-Shing ; Pai, Ming-Chyi. / Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer. Paper presented at 2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012, Seoul, Korea, Republic of.4 p.
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abstract = "This paper presents an inertial-sensor-based wearable device and its associated stride detection algorithm to analyze gait information for patients with Alzheimer's disease (AD). The wearable gait analysis device is composed of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. To validate the effectiveness of the proposed device and algorithm, nine AD patients and three healthy controls were recruited to participate a gait analysis experiment. They were asked to mount the device on their foot and walk along a straight line of 40 meters at normal speed. The stride detection algorithm, consisting of procedures of data collection, signal preprocessing, and stride detection, has been developed for acquiring gait feature information from acceleration signals. The advantages of this wearable gait analysis device include the following: 1) It can be used anywhere without any external device, and 2) the stride detection algorithm can acquire gait feature information from acceleration signals automatically and effectively. Experimental results show that the AD patients exhibited a significantly shorter mean stride length and slower mean gait speed than those of the healthy controls. No significant differences in mean stride frequency and mean cadence were observed in the two groups. The variability in the percentage of the stance phase of the AD patients was slightly greater than that of the healthy controls. Based on the above results and discussions with physicians, we conclude that the proposed wearable gait analysis device is a promising tool for automatically analyzing gait information which can serve as indicators for early diagnosis of AD.",
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Chung, P-C, Hsu, YL, Wang, CY, Lin, CW, Wang, J-S & Pai, M-C 2012, 'Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer' Paper presented at 2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012, Seoul, Korea, Republic of, 12-05-20 - 12-05-23, pp. 1323-1326. https://doi.org/10.1109/ISCAS.2012.6271484

Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer. / Chung, Pau-Choo; Hsu, Yu Liang; Wang, Chun Yao; Lin, Chien Wen; Wang, Jeen-Shing; Pai, Ming-Chyi.

2012. 1323-1326 Paper presented at 2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012, Seoul, Korea, Republic of.

Research output: Contribution to conferencePaper

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AU - Wang, Jeen-Shing

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N2 - This paper presents an inertial-sensor-based wearable device and its associated stride detection algorithm to analyze gait information for patients with Alzheimer's disease (AD). The wearable gait analysis device is composed of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. To validate the effectiveness of the proposed device and algorithm, nine AD patients and three healthy controls were recruited to participate a gait analysis experiment. They were asked to mount the device on their foot and walk along a straight line of 40 meters at normal speed. The stride detection algorithm, consisting of procedures of data collection, signal preprocessing, and stride detection, has been developed for acquiring gait feature information from acceleration signals. The advantages of this wearable gait analysis device include the following: 1) It can be used anywhere without any external device, and 2) the stride detection algorithm can acquire gait feature information from acceleration signals automatically and effectively. Experimental results show that the AD patients exhibited a significantly shorter mean stride length and slower mean gait speed than those of the healthy controls. No significant differences in mean stride frequency and mean cadence were observed in the two groups. The variability in the percentage of the stance phase of the AD patients was slightly greater than that of the healthy controls. Based on the above results and discussions with physicians, we conclude that the proposed wearable gait analysis device is a promising tool for automatically analyzing gait information which can serve as indicators for early diagnosis of AD.

AB - This paper presents an inertial-sensor-based wearable device and its associated stride detection algorithm to analyze gait information for patients with Alzheimer's disease (AD). The wearable gait analysis device is composed of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. To validate the effectiveness of the proposed device and algorithm, nine AD patients and three healthy controls were recruited to participate a gait analysis experiment. They were asked to mount the device on their foot and walk along a straight line of 40 meters at normal speed. The stride detection algorithm, consisting of procedures of data collection, signal preprocessing, and stride detection, has been developed for acquiring gait feature information from acceleration signals. The advantages of this wearable gait analysis device include the following: 1) It can be used anywhere without any external device, and 2) the stride detection algorithm can acquire gait feature information from acceleration signals automatically and effectively. Experimental results show that the AD patients exhibited a significantly shorter mean stride length and slower mean gait speed than those of the healthy controls. No significant differences in mean stride frequency and mean cadence were observed in the two groups. The variability in the percentage of the stance phase of the AD patients was slightly greater than that of the healthy controls. Based on the above results and discussions with physicians, we conclude that the proposed wearable gait analysis device is a promising tool for automatically analyzing gait information which can serve as indicators for early diagnosis of AD.

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Chung P-C, Hsu YL, Wang CY, Lin CW, Wang J-S, Pai M-C. Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer. 2012. Paper presented at 2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012, Seoul, Korea, Republic of. https://doi.org/10.1109/ISCAS.2012.6271484