Robust Stride Segmentation of Inertial Signals Based on Local Cyclicity Estimation
| Autor: | Matjaž B. Jurič, Sebastijan Sprager |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Inertial frame of reference
Computer science biomedical signal processing obdelava biomedicinskih signalov STRIDE 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Article inertial signals Analytical Chemistry udc:004.93:621.391 Gait (human) Inertial measurement unit Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Segmentation lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Ground truth business.industry 010401 analytical chemistry Pattern recognition inertial sensors stride segmentation gait assessment Gait Atomic and Molecular Physics and Optics 0104 chemical sciences segmentacija korakov ocenjevanje hoje Gait analysis inercijski signali 020201 artificial intelligence & image processing Artificial intelligence inercialni senzorji business |
| Zdroj: | Sensors; Volume 18; Issue 4; Pages: 1091 Sensors, vol. 18, no. 4, 1091, 2018. Sensors, Vol 18, Iss 4, p 1091 (2018) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s18041091 |
| Popis: | A novel approach for stride segmentation, gait sequence extraction, and gait event detection for inertial signals is presented. The approach operates by combining different local cyclicity estimators and sensor channels, and can additionally employ a priori knowledge on the fiducial points of gait events. The approach is universal as it can work on signals acquired by different inertial measurement unit (IMU) sensor types, is template-free, and operates unsupervised. A thorough evaluation was performed with two datasets: our own collected FRIgait dataset available for open use, containing long-term inertial measurements collected from 57 subjects using smartphones within the span of more than one year, and an FAU eGait dataset containing inertial data from shoe-mounted sensors collected from three cohorts of subjects: healthy, geriatric, and Parkinsonrsquo;s disease patients. The evaluation was performed in controlled and uncontrolled conditions. When compared to the ground truth of the labelled FRIgait and eGait datasets, the results of our evaluation revealed the high robustness, efficiency (F-measure of about 98%), and accuracy (mean absolute error MAE in about the range of one sample) of the proposed approach. Based on these results, we conclude that the proposed approach shows great potential for its applicability in procedures and algorithms for movement analysis. |
| Databáze: | OpenAIRE |
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