Paper Info
Title
Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds.
Abstract
The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects' step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm.
Year
DOI
Venue
2019
10.3390/s19081869
SENSORS
Keywords
Field
DocType
initial contact,final contact,inertial sensor,wavelet,gait parameters
Computer vision,Gait,Accelerometer,Waist,Mean squared error,Electronic engineering,Acceleration,Artificial intelligence,Engineering,Motion analysis,Wavelet,Preferred walking speed
Journal
Volume
Issue
ISSN
19
8
1424-8220
Citations 
PageRank 
References 
1
0.41
0
Authors
3
Name
Order
Citations
PageRank
Carlotta Caramia151.22
Cristiano De Marchis2125.89
Maurizio Schmid38916.32