Next Article in Journal
Sensor Systems for FRP Lightweight Structures: Automotive Features Based on Serial Sensor Products
Next Article in Special Issue
Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson’s Disease
Previous Article in Journal
Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems
Article

Comparing Gait Trials with Greedy Template Matching

1
COGNAC-G (UMR 8257), CNRS Service de Santé des Armées University Paris Descartes, 75006 Paris, France
2
L2TI, University Paris 13, 93430 Villetaneuse, France
3
CMLA (UMR 8536), CNRS ENS Paris-Saclay, 94235 Cachan, France
4
ORPEA Group, 92813 Puteaux, France
5
Hangzhou Dianzi University, 310005 Hangzhou, China
6
Service de Neurologie, Hôpital d’Instruction des Armées Percy, Service de Santé des Armées, 92190 Clamart, France
7
Ecole du Val-de-Grâce, Ecole de Santé des Armées, 75005 Paris, France
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3089; https://doi.org/10.3390/s19143089
Received: 9 May 2019 / Revised: 9 July 2019 / Accepted: 11 July 2019 / Published: 12 July 2019
Gait assessment and quantification have received an increased interest in recent years. Embedded technologies and low-cost sensors can be used for the longitudinal follow-up of various populations (neurological diseases, elderly, etc.). However, the comparison of two gait trials remains a tricky question as standard gait features may prove to be insufficient in some cases. This article describes a new algorithm for comparing two gait trials recorded with inertial measurement units (IMUs). This algorithm uses a library of step templates extracted from one trial and attempts to detect similar steps in the second trial through a greedy template matching approach. The output of our method is a similarity index (SId) comprised between 0 and 1 that reflects the similarity between the patterns observed in both trials. Results on healthy and multiple sclerosis subjects show that this new comparison tool can be used for both inter-individual comparison and longitudinal follow-up. View Full-Text
Keywords: inertial measurement units; gait analysis; biomedical signal processing; pattern recognition; step detection; physiological signals inertial measurement units; gait analysis; biomedical signal processing; pattern recognition; step detection; physiological signals
Show Figures

Figure 1

MDPI and ACS Style

Vienne-Jumeau, A.; Oudre, L.; Moreau, A.; Quijoux, F.; Vidal, P.-P.; Ricard, D. Comparing Gait Trials with Greedy Template Matching. Sensors 2019, 19, 3089. https://doi.org/10.3390/s19143089

AMA Style

Vienne-Jumeau A, Oudre L, Moreau A, Quijoux F, Vidal P-P, Ricard D. Comparing Gait Trials with Greedy Template Matching. Sensors. 2019; 19(14):3089. https://doi.org/10.3390/s19143089

Chicago/Turabian Style

Vienne-Jumeau, Aliénor, Laurent Oudre, Albane Moreau, Flavien Quijoux, Pierre-Paul Vidal, and Damien Ricard. 2019. "Comparing Gait Trials with Greedy Template Matching" Sensors 19, no. 14: 3089. https://doi.org/10.3390/s19143089

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop