Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 715-718 |
| Seitenumfang | 4 |
| Fachzeitschrift | Current Directions in Biomedical Engineering |
| Jahrgang | 2 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - Sept. 2016 |
Abstract
Inertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Biomedizintechnik
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in: Current Directions in Biomedical Engineering, Jahrgang 2, Nr. 1, 09.2016, S. 715-718.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Automatic pairing of inertial sensors to lower limb segments--a plug-and-play approach
AU - Graurock, David
AU - Schauer, Thomas
AU - Seel, Thomas
N1 - Publisher Copyright: © 2016 Thomas Seel et al., licensee De Gruyter.
PY - 2016/9
Y1 - 2016/9
N2 - Inertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.
AB - Inertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.
KW - Automatic calibration
KW - Inertial sensor networks
KW - Realtime gait analysis
KW - Sensor-to-segment pairing
KW - Wireless inertial measurement units
UR - http://www.scopus.com/inward/record.url?scp=85034003244&partnerID=8YFLogxK
U2 - 10.1515/cdbme-2016-0155
DO - 10.1515/cdbme-2016-0155
M3 - Article
VL - 2
SP - 715
EP - 718
JO - Current Directions in Biomedical Engineering
JF - Current Directions in Biomedical Engineering
IS - 1
ER -