Research on Human Motion Recognition System Based on MEMS Sensor Network

Ma, Hao; Liu, Hao

doi:10.1109/iaeac47372.2019.8997727

Cited by 5 publications

(2 citation statements)

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“…By recording features such as pitch angle and rotation angle, they analyzed the movement details of swimmers and developed a swimming monitoring system for training guidance. Ma et al [11] designed a wireless network module based on MEMS inertial sensors [12] and Zigbee [13] used the SVM classification algorithm [14] for human motion recognition. This work showed desirable performance in detecting abnormal behaviors such as falls.…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion

Wang,

Su,

Liu

et al. 2023

Electronics

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Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-MobileNet employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. Furthermore, we introduce a novel approach for action recognition that combines skeleton-based information and multiple feature fusion. By extracting spatial geometric and temporal characteristics from actions, we employ a sliding window algorithm to integrate these features. Experimental results show the effectiveness of our approach, demonstrating its ability to accurately recognize and classify various human actions. Additionally, we address the evaluation of traditional fitness exercises, specifically focusing on the BaDunJin movements. We propose a multimodal information-based assessment method that combines pose detection and keypoint analysis. Label sequences are obtained through a pose detector and each frame’s keypoint coordinates are represented as pose vectors. Leveraging multimodal information, including label sequences and pose vectors, we explore action similarity and perform quantitative evaluations to help exercisers assess the quality of their exercise performance.

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Section: Related Workmentioning

confidence: 99%

Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion

Wang,

Su,

Liu

et al. 2023

Electronics

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“…[ 10 , 11 , 12 , 16 ], extract features from time series of sensing data. Others extract motion features from the frequency domain of raw data [ 17 , 18 ]. As for classifiers, machine learning algorithms are widely used, such as K-nearest neighbor, support vector machine, naïve Bayes, neural networks, Markov models, and convolutional neural networks [ 9 , 11 , 13 , 19 , 20 ].…”

Section: Related Workmentioning

confidence: 99%

Human Lower Limb Motion Capture and Recognition Based on Smartphones

Duan

Lawo

Wang

et al. 2022

Sensors

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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.

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