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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Human motion recognition based on wearable devices plays a vital role in pervasive computing. Smartphones have built-in motion sensors that measure the motion of the device with high precision. In this paper, we propose a human lower limb motion capture and recognition approach based on a Smartphone. We design a motion logger to record five categories of limb activities (standing up, sitting down, walking, going upstairs, and going downstairs) using two motion sensors (tri-axial accelerometer, tri-axial gyroscope). We extract the motion features and select a subset of features as a feature vector from the frequency domain of the sensing data using Fast Fourier Transform (FFT). We classify and predict human lower limb motion using three supervised learning algorithms: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANNs). We use 670 lower limb motion samples to train and verify these classifiers using the 10-folder cross-validation technique. Finally, we design and implement a live detection system to validate our motion detection approach. The experimental results show that our low-cost approach can recognize human lower limb activities with acceptable accuracy. On average, the recognition rate of NB, KNN, and ANNs are 97.01%, 96.12%, and 98.21%, respectively.

Details

Title
Human Lower Limb Motion Capture and Recognition Based on Smartphones
Author
Lin-Tao, Duan 1   VIAFID ORCID Logo  ; Lawo, Michael 2 ; Zhi-Guo, Wang 3 ; Hai-Ying, Wang 4 

 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; [email protected]; School of Computer Science, Chengdu University, Chengdu 610106, China; [email protected] 
 International Graduate School for Dynamics in Logistics, Bremen University, 28359 Bremen, Germany; [email protected] 
 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; [email protected] 
 School of Computer Science, Chengdu University, Chengdu 610106, China; [email protected] 
First page
5273
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2694063146
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.