Abstract
The use of Inertial Measurement Unit (IMU) for gait analysis is gaining popularity because of its advantages of low cost and non-limited workspace. In this context, researchers are focusing on methods for automated data analysis. For example, many algorithms for stride length estimation have been developed. These algorithms rely on event detection to compute gait parameters during walking and on orientation estimation for a more precise double integration of acceleration. However, at the present, there is not comparison between existing algorithms, and the applicability of each algorithm for different walking patterns is not clear. In this paper, we studied the effect on the stride length estimation using three different techniques of event detection and two techniques of orientation estimation, by using an IMU on the lateral side of shank above the ankle. In total 6 patterns of stride estimation algorithms were compared on different walking patterns of normal and brisk walking. We evaluated the techniques in terms of precision, accuracy, and shape of the histogram of the stride estimation error.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 671-676 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509067572 |
| DOIs | |
| Publication status | Published - 2017 Aug 23 |
| Event | 14th IEEE International Conference on Mechatronics and Automation, ICMA 2017 - Takamatsu, Japan Duration: 2017 Aug 6 → 2017 Aug 9 |
Other
| Other | 14th IEEE International Conference on Mechatronics and Automation, ICMA 2017 |
|---|---|
| Country | Japan |
| City | Takamatsu |
| Period | 17/8/6 → 17/8/9 |
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Keywords
- Double integration
- Gait analysis
- IMU
- R-adaptive
- Stride length estimation
ASJC Scopus subject areas
- Control and Optimization
- Instrumentation
- Artificial Intelligence
- Industrial and Manufacturing Engineering
- Mechanical Engineering
Cite this
Reliability of stride length estimation in self-pace and brisk walking with an inertial measurement unit on shank. / Kasai, R.; Kodama, T.; Gu, Z.; Zhang, Di; Kong, W.; Cosentino, Sarah; Sessa, S.; Kawakami, Yasuo; Takanishi, Atsuo.
2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 671-676 8015896.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Reliability of stride length estimation in self-pace and brisk walking with an inertial measurement unit on shank
AU - Kasai, R.
AU - Kodama, T.
AU - Gu, Z.
AU - Zhang, Di
AU - Kong, W.
AU - Cosentino, Sarah
AU - Sessa, S.
AU - Kawakami, Yasuo
AU - Takanishi, Atsuo
PY - 2017/8/23
Y1 - 2017/8/23
N2 - The use of Inertial Measurement Unit (IMU) for gait analysis is gaining popularity because of its advantages of low cost and non-limited workspace. In this context, researchers are focusing on methods for automated data analysis. For example, many algorithms for stride length estimation have been developed. These algorithms rely on event detection to compute gait parameters during walking and on orientation estimation for a more precise double integration of acceleration. However, at the present, there is not comparison between existing algorithms, and the applicability of each algorithm for different walking patterns is not clear. In this paper, we studied the effect on the stride length estimation using three different techniques of event detection and two techniques of orientation estimation, by using an IMU on the lateral side of shank above the ankle. In total 6 patterns of stride estimation algorithms were compared on different walking patterns of normal and brisk walking. We evaluated the techniques in terms of precision, accuracy, and shape of the histogram of the stride estimation error.
AB - The use of Inertial Measurement Unit (IMU) for gait analysis is gaining popularity because of its advantages of low cost and non-limited workspace. In this context, researchers are focusing on methods for automated data analysis. For example, many algorithms for stride length estimation have been developed. These algorithms rely on event detection to compute gait parameters during walking and on orientation estimation for a more precise double integration of acceleration. However, at the present, there is not comparison between existing algorithms, and the applicability of each algorithm for different walking patterns is not clear. In this paper, we studied the effect on the stride length estimation using three different techniques of event detection and two techniques of orientation estimation, by using an IMU on the lateral side of shank above the ankle. In total 6 patterns of stride estimation algorithms were compared on different walking patterns of normal and brisk walking. We evaluated the techniques in terms of precision, accuracy, and shape of the histogram of the stride estimation error.
KW - Double integration
KW - Gait analysis
KW - IMU
KW - R-adaptive
KW - Stride length estimation
UR - http://www.scopus.com/inward/record.url?scp=85030308225&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030308225&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2017.8015896
DO - 10.1109/ICMA.2017.8015896
M3 - Conference contribution
SP - 671
EP - 676
BT - 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -