A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors
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Published:2023-09-21
Issue:18
Volume:13
Page:10529
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Mao Yaxin1, Yan Lamei2, Guo Hongyu1, Hong Yujie1, Huang Xiaocheng13ORCID, Yuan Youwei13
Affiliation:
1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China 2. School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China 3. Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
Abstract
Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment.
Funder
Natural Science Foundation of Zhejiang Province National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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