Step sequence and direction detection of four square step test
Step sequence and direction detection of four square step test
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.
Automation in life sciences: biotechnology, health care management, pharmaceutical and health care, sensor fusion
2194-2200
Kong, Weisheng
808d478a-54c0-4ff4-b1df-6103d7d92703
Wanning, Lauren
7789899e-0763-4767-92f7-b0e00fd125a3
Sessa, Salvatore
dae18bb6-4a40-4581-8c50-f0faa16841df
Zecca, Massimiliano
870c8b27-684b-42b3-baed-40dd996c2800
Magistro, Daniele
ab9296bc-fda6-469e-a3f8-3a574faa1b7e
Takeuchi, Hikaru
5b946b96-b159-4ead-8f17-e078a7ee765b
Kawashima, Ryuta
696ba780-ca26-4227-af1d-3ae821a12d00
Takanishi, Atsuo
856a12a7-dd3f-4f1f-ae54-1fb62989db20
October 2017
Kong, Weisheng
808d478a-54c0-4ff4-b1df-6103d7d92703
Wanning, Lauren
7789899e-0763-4767-92f7-b0e00fd125a3
Sessa, Salvatore
dae18bb6-4a40-4581-8c50-f0faa16841df
Zecca, Massimiliano
870c8b27-684b-42b3-baed-40dd996c2800
Magistro, Daniele
ab9296bc-fda6-469e-a3f8-3a574faa1b7e
Takeuchi, Hikaru
5b946b96-b159-4ead-8f17-e078a7ee765b
Kawashima, Ryuta
696ba780-ca26-4227-af1d-3ae821a12d00
Takanishi, Atsuo
856a12a7-dd3f-4f1f-ae54-1fb62989db20
Kong, Weisheng, Wanning, Lauren, Sessa, Salvatore, Zecca, Massimiliano, Magistro, Daniele, Takeuchi, Hikaru, Kawashima, Ryuta and Takanishi, Atsuo
(2017)
Step sequence and direction detection of four square step test.
IEEE Robotics and Automation Letters, 2 (4), , [7970186].
(doi:10.1109/LRA.2017.2723929).
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.
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e-pub ahead of print date: 6 July 2017
Published date: October 2017
Keywords:
Automation in life sciences: biotechnology, health care management, pharmaceutical and health care, sensor fusion
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Local EPrints ID: 510625
URI: http://eprints.soton.ac.uk/id/eprint/510625
ISSN: 2377-3766
PURE UUID: 8afc9a4e-11e2-4423-940f-ae6338458f43
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Date deposited: 14 Apr 2026 16:45
Last modified: 16 Apr 2026 02:18
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Contributors
Author:
Weisheng Kong
Author:
Lauren Wanning
Author:
Salvatore Sessa
Author:
Massimiliano Zecca
Author:
Daniele Magistro
Author:
Hikaru Takeuchi
Author:
Ryuta Kawashima
Author:
Atsuo Takanishi
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