Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70836
Title: Comparison of algorithms and classifiers for stride detection using wearables
Authors: Steinmetzer, Tobias
Bönninger, Ingrid
Reckhardt, Markus
Reinhardt, Fritjof
Erk, Dorela
Travieso González, Carlos Manuel 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Stride detection
Gait analysis
Inertial sensors
Parkinson’s disease
Validation
Dynamic time warping
Time up and go test
Convolutional neural networks
Issue Date: 2020
Journal: Neural Computing and Applications 
Abstract: Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min–max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson’s disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown.
URI: http://hdl.handle.net/10553/70836
ISSN: 0941-0643
DOI: 10.1007/s00521-019-04384-6
Source: Neural Computing and Applications[ISSN 0941-0643], n. 32, p. 17857-17868
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