Tracking of Gymnast’s Limb Movement Trajectory Based on MEMS Inertial Sensor

Li, Peng; Zhou, Jihe

doi:10.1155/2022/5292454

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Li et al [23] proposed an SVM-based model for the recognition of gymnastic movements. Rustam [24] presented a deep stacked multilayered perceptron model for HAR.…”

Section: Related Workmentioning

confidence: 99%

CNN-LSTM-Based Late Sensor Fusion for Human Activity Recognition in Big Data Networks

Baloch

Shaikh

Unar

2022

Wireless Communications and Mobile Computing

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The technological advancement in sensor technology and pervasive computing has brought smart devices into our daily life. Due to the continuous connectivity of the internet with our everyday devices, researchers can deploy IoT sensors to health care and other applications, such as human activity recognition. Most of the state-of-the-art sensor-based human activity recognition systems can detect basic activities (such as standing, sitting, and walking), but they cannot accurately distinguish similar activities (ascending stairs or descending stairs). Such systems are not efficient for critical healthcare applications having complex activity sets. This paper proposes two sensor fusion approaches, i.e., position-based early and late sensor fusion using convolutional neural network (CNN) and convolutional long-short-term memory (CNN-LSTM). The performance of our proposed models is evaluated on two publicly available datasets. We also evaluated the effect of different normalization techniques on recognition accuracy. Our results show that the CNN-LSTM-based late sensor fusion model also improves the recognition accuracy of similar activities.

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“…Li et al [23] proposed an SVM-based model for the recognition of gymnastic movements. Rustam [24] presented a deep stacked multilayered perceptron model for HAR.…”

Section: Related Workmentioning

confidence: 99%

CNN-LSTM-Based Late Sensor Fusion for Human Activity Recognition in Big Data Networks

Baloch

Shaikh

Unar

2022

Wireless Communications and Mobile Computing

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

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confidence: 99%

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confidence: 99%

Retracted: Tracking of Gymnast’s Limb Movement Trajectory Based on MEMS Inertial Sensor

2023

Applied Bionics and Biomechanics

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Multi-sensor fusion based optimized deep convolutional neural network for boxing punch activity recognition

Jayakumar,

Govindarajan

2024

Proceedings of the Institution of Mechanical Engineers, Part P:

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Recent advancements in deep learning have significantly enhanced the recognition of player activities in sports by enabling automatic feature extraction. In our proposed work, we focus on recognizing six distinct punches in the context of boxing. We incorporate the sliding window technique during the pre-processing stage to transform the time-series data obtained from Inertial Measurement Unit (IMU) sensors in a boxing punch activity recognition system. Our approach influences a sensor fusion-based Deep Convolutional Neural Network (DCNN) classification model to identify various boxing punches accurately, achieving an impressive F1 score. The system demonstrates proficiency in distinguishing similar activities, such as jab and hook punches where the existing systems made misclassifications due to subtle variations in arm flexion that differentiate the two. Through experimentation, we identify an optimal window size for boxing punch activity recognition, which falls within the range of 15–20 frames (equivalent to 0.15–0.25 s). This window size selection results in a notable reduction in inference time. To evaluate our proposed model, we conduct comparisons with a standard DCNN and an optimized DCNN model. Our proposed optimized DCNN model demonstrates enhanced recognition accuracy, achieving an impressive 99%, coupled with an improved F1 score of 87%. Furthermore, the model displays a remarkable reduction in inference time, clocking in at less than 1 ms. Overall, our research contributes to the field of sports-related player activity recognition by employing the power of deep learning. By expertly combining these techniques, we achieve remarkable accuracy, precision, and efficiency in recognizing various boxing punches.

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Tracking of Gymnast’s Limb Movement Trajectory Based on MEMS Inertial Sensor

Cited by 4 publications

References 15 publications

CNN-LSTM-Based Late Sensor Fusion for Human Activity Recognition in Big Data Networks

CNN-LSTM-Based Late Sensor Fusion for Human Activity Recognition in Big Data Networks

Retracted: Tracking of Gymnast’s Limb Movement Trajectory Based on MEMS Inertial Sensor

Multi-sensor fusion based optimized deep convolutional neural network for boxing punch activity recognition

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