Abstract
One Leg Stance (OLS), a test assessing postural stability, is popularly conducted both in clinic and community settings because it is inexpensive and time-efficient. However, the evaluation based on visual observation and manual time measurement with a stop-watch cannot provide quantitative and detailed parameters for longitudinal or cross-sectional studies. In recent years, to overcome these limitations, the use of Inertial Measurement Unit (IMU) as objective measurement analysis tools is becoming more and more popular. However, the greatest issue is that IMU data segmentation is still time-consuming and prone to errors, as the OLS segmentation is being done manually, off-line, on recorded data. In this paper we proposed a novel algorithm for the automatic segmentation of IMU data of the OLS test. The result showed that the correct rate of detection was over 90% which was close to the correct rate in manual segmentation. Compared to manual segmentation with video, besides being less time-consuming, the proposed algorithm closes the loop making the data acquisition and analysis completely automatic, thus can be integrated in self-assessment smart phone applications, allowing the continuous tracking of postural stability also outside clinics and health-care facilities.
| Original language | English |
|---|---|
| Title of host publication | SII 2016 - 2016 IEEE/SICE International Symposium on System Integration |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 307-312 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509033294 |
| DOIs | |
| Publication status | Published - 2017 Feb 6 |
| Event | 2016 IEEE/SICE International Symposium on System Integration, SII 2016 - Sapporo, Japan Duration: 2016 Dec 13 → 2016 Dec 15 |
Other
| Other | 2016 IEEE/SICE International Symposium on System Integration, SII 2016 |
|---|---|
| Country | Japan |
| City | Sapporo |
| Period | 16/12/13 → 16/12/15 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Control and Systems Engineering
- Mechanical Engineering
- Artificial Intelligence
- Hardware and Architecture
- Control and Optimization
Cite this
Automatic segmentation for one leg stance test with inertial measurement unit. / Kong, W.; Kodama, T.; Sessa, S.; Cosentino, Sarah; Magistro, D.; Kawashima, R.; Takanishi, Atsuo.
SII 2016 - 2016 IEEE/SICE International Symposium on System Integration. Institute of Electrical and Electronics Engineers Inc., 2017. p. 307-312 7844016.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic segmentation for one leg stance test with inertial measurement unit
AU - Kong, W.
AU - Kodama, T.
AU - Sessa, S.
AU - Cosentino, Sarah
AU - Magistro, D.
AU - Kawashima, R.
AU - Takanishi, Atsuo
PY - 2017/2/6
Y1 - 2017/2/6
N2 - One Leg Stance (OLS), a test assessing postural stability, is popularly conducted both in clinic and community settings because it is inexpensive and time-efficient. However, the evaluation based on visual observation and manual time measurement with a stop-watch cannot provide quantitative and detailed parameters for longitudinal or cross-sectional studies. In recent years, to overcome these limitations, the use of Inertial Measurement Unit (IMU) as objective measurement analysis tools is becoming more and more popular. However, the greatest issue is that IMU data segmentation is still time-consuming and prone to errors, as the OLS segmentation is being done manually, off-line, on recorded data. In this paper we proposed a novel algorithm for the automatic segmentation of IMU data of the OLS test. The result showed that the correct rate of detection was over 90% which was close to the correct rate in manual segmentation. Compared to manual segmentation with video, besides being less time-consuming, the proposed algorithm closes the loop making the data acquisition and analysis completely automatic, thus can be integrated in self-assessment smart phone applications, allowing the continuous tracking of postural stability also outside clinics and health-care facilities.
AB - One Leg Stance (OLS), a test assessing postural stability, is popularly conducted both in clinic and community settings because it is inexpensive and time-efficient. However, the evaluation based on visual observation and manual time measurement with a stop-watch cannot provide quantitative and detailed parameters for longitudinal or cross-sectional studies. In recent years, to overcome these limitations, the use of Inertial Measurement Unit (IMU) as objective measurement analysis tools is becoming more and more popular. However, the greatest issue is that IMU data segmentation is still time-consuming and prone to errors, as the OLS segmentation is being done manually, off-line, on recorded data. In this paper we proposed a novel algorithm for the automatic segmentation of IMU data of the OLS test. The result showed that the correct rate of detection was over 90% which was close to the correct rate in manual segmentation. Compared to manual segmentation with video, besides being less time-consuming, the proposed algorithm closes the loop making the data acquisition and analysis completely automatic, thus can be integrated in self-assessment smart phone applications, allowing the continuous tracking of postural stability also outside clinics and health-care facilities.
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U2 - 10.1109/SII.2016.7844016
DO - 10.1109/SII.2016.7844016
M3 - Conference contribution
AN - SCOPUS:85015361664
SP - 307
EP - 312
BT - SII 2016 - 2016 IEEE/SICE International Symposium on System Integration
PB - Institute of Electrical and Electronics Engineers Inc.
ER -