Robust stride segmentation of inertial signals based on local cyclicity estimation

Šprager, Sebastijan (author), Jurič, Matjaž B. (author)

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 Parkinson’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.

• Language: English
• Keywords: inertial sensors, stride segmentation, gait assessment, inertial signals, biomedical signal processing
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2018
• Number of pages: 24 str.
• Numbering: Vol. 18, iss. 4, art. 1091
• PID: 20.500.12556/RUL-131859
• UDC: 004.93:621.391
• ISSN on article: 1424-8220
• DOI: 10.3390/s18041091
• COBISS.SI-ID: 1537764547
• Publication date in RUL: 05.10.2021

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 04.04.2018

Secondary language

• Language: Slovenian
• Keywords: inercialni senzorji, segmentacija korakov, ocenjevanje hoje, inercijski signali, obdelava biomedicinskih signalov