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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25962

Title: Step sequence and direction detection of four square step test
Authors: Kong, Weisheng
Waaning, Lauren
Sessa, Salvatore
Zecca, Massimiliano
Magistro, Daniele
Takeuchi, Hikaru
Kawashima, Ryuta
Takanishi, Atsuo
Keywords: Automation in life sciences: biotechnology
Health care management
Pharmaceutical and health care
Sensor fusion
Issue Date: 2017
Publisher: © IEEE
Citation: KONG, W. ... et al, 2017. Step sequence and direction detection of four square step test. IEEE Robotics and Automation Letters, 2 (4), pp. 2194-2200.
Abstract: Poor balance control and falls are big issues for older adults that due to aging decline have a lower postural balance and directional control in balance performance than younger age groups. The four square step test (FSST) was developed to evaluate rapid stepping that is often required when changing direction and avoiding obstacles while walking. However, previous researchers used only the total time as the assessment in the test. The aim of this letter is to objectively quantify the sequence and direction of the steps in FSST, by using two inertial sensors placed on both feet. An algorithm was developed to automatically segment the steps performed during the test, and calculate the stepping direction from the linear velocity of the foot. Experiments were conducted with 100 Japanese healthy older adults, where sensor data and video of 20 subjects were randomly subtracted for algorithm verification. The results showed that the algorithm succeeded for 71.7% trials in recognizing both the step sequence and step direction in FSST, while 90.2% of the detection failure could be excluded with an auto verification method.
Description: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work was supported in part by the JSPS Grant-in-Aid for Young Scientists (Wakate B) [25750259] and [15K21437], in part by the Program for Leading Graduate Schools, “Graduate Program for Embodiment Informatics” of the Ministry of Education, Culture, Sports, Science and Technology, and in part by the UK-HEFCE Catalyst grant and by the LU-EESE startup grant.
Version: Accepted for publication
DOI: 10.1109/LRA.2017.2723929
URI: https://dspace.lboro.ac.uk/2134/25962
Publisher Link: http://dx.doi.org/10.1109/LRA.2017.2723929
ISSN: 2377-3766
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)
Published Articles (Sport, Exercise and Health Sciences)

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