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Open AccessArticle

Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping

1
Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376 Stuttgart, Germany
2
Department of Neuroscience, NTNU, 7491 Trondheim, Norway
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Department of Electrical, Electronic, and Information Engineering, University of Bologna, 40136 Bologna, Italy
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Department of General Practice and Primary Health Care, University of Auckland, Auckland 1023, New Zealand
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Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081 Ulm, Germany
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Study Center Stuttgart, IB University for Health and Social Sciences, Paulinenstr. 45, 70178 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Mehmet Rasit Yuce
Sensors 2021, 21(8), 2601; https://doi.org/10.3390/s21082601
Received: 17 February 2021 / Revised: 16 March 2021 / Accepted: 29 March 2021 / Published: 7 April 2021
(This article belongs to the Section Intelligent Sensors)
Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2–5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities. View Full-Text
Keywords: physical activity recognition; locomotion; wearable sensors; dynamic time warping physical activity recognition; locomotion; wearable sensors; dynamic time warping
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MDPI and ACS Style

Sczuka, K.S.; Schneider, M.; Bourke, A.K.; Mellone, S.; Kerse, N.; Helbostad, J.L.; Becker, C.; Klenk, J. Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping. Sensors 2021, 21, 2601. https://doi.org/10.3390/s21082601

AMA Style

Sczuka KS, Schneider M, Bourke AK, Mellone S, Kerse N, Helbostad JL, Becker C, Klenk J. Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping. Sensors. 2021; 21(8):2601. https://doi.org/10.3390/s21082601

Chicago/Turabian Style

Sczuka, Kim S.; Schneider, Marc; Bourke, Alan K.; Mellone, Sabato; Kerse, Ngaire; Helbostad, Jorunn L.; Becker, Clemens; Klenk, Jochen. 2021. "Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping" Sensors 21, no. 8: 2601. https://doi.org/10.3390/s21082601

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