Details
| Original language | English |
|---|---|
| Pages (from-to) | 715-718 |
| Number of pages | 4 |
| Journal | Current Directions in Biomedical Engineering |
| Volume | 2 |
| Issue number | 1 |
| Publication status | Published - 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.
Keywords
- Automatic calibration, Inertial sensor networks, Realtime gait analysis, Sensor-to-segment pairing, Wireless inertial measurement units
ASJC Scopus subject areas
- Engineering(all)
- Biomedical Engineering
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In: Current Directions in Biomedical Engineering, Vol. 2, No. 1, 09.2016, p. 715-718.
Research output: Contribution to journal › Article › Research › 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 -