Estimation of Fine-Grained Foot Strike Patterns with Wearable Smartwatch Devices

Hyeyeoun Joo, Hyejoo Kim, Jeh-Kwang Ryu, Semin Ryu, Kyoung-Min Lee and Seung-Chan Kim
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Hyeyeoun Joo: Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea
Hyejoo Kim: Machine Learning Systems Laboratory, Department of Sports Science, Sungkyunkwan University, Suwon 16419, Korea
Jeh-Kwang Ryu: Department of Physical Education, College of Education, Dongguk University, Seoul 04620, Korea
Semin Ryu: Intelligent Robotics Laboratory, School of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Korea
Kyoung-Min Lee: Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea
Seung-Chan Kim: Machine Learning Systems Laboratory, Department of Sports Science, Sungkyunkwan University, Suwon 16419, Korea

IJERPH, 2022, vol. 19, issue 3, 1-18

Abstract: People who exercise may benefit or be injured depending on their foot striking (FS) style. In this study, we propose an intelligent system that can recognize subtle differences in FS patterns while walking and running using measurements from a wearable smartwatch device. Although such patterns could be directly measured utilizing pressure distribution of feet while striking on the ground, we instead focused on analyzing hand movements by assuming that striking patterns consequently affect temporal movements of the whole body. The advantage of the proposed approach is that FS patterns can be estimated in a portable and less invasive manner. To this end, first, we developed a wearable system for measuring inertial movements of hands and then conducted an experiment where participants were asked to walk and run while wearing a smartwatch. Second, we trained and tested the captured multivariate time series signals in supervised learning settings. The experimental results obtained demonstrated high and robust classification performances (weighted-average F1 score > 90%) when recent deep neural network models, such as 1D-CNN and GRUs, were employed. We conclude this study with a discussion of potential future work and applications that increase benefits while walking and running properly using the proposed approach.

Keywords: healthcare wearables; deep sequence learning; fine-grained motion classification; activity monitoring; human activity recognition (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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