U-WeAr: User Recognition on Wearable Devices through Arm Gesture

Bianco, Simone; Napoletano, Paolo; Raimondi, Alberto; Rima, Mirko

doi:10.1109/thms.2022.3170829

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…Bianco et al implemented arm posture recognition, user identification, and identity verification based on a custom wireless wristband inertial sensor combined with a recursive neural network. The U-WeAr study found that numerical normalization preprocessing can enhance the anti-interference ability of gesture data with different amplitudes and speeds [26]. Kang et al achieved accurate gesture recognition during walking dynamics using a wristband-type inertial sensor, by combining empirical mode decomposition with a distribution-adaptive transfer learning method [27].…”

Section: Related Workmentioning

confidence: 99%

TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

Liu¹,

Wu²,

Liu³

2022

Journal on Internet of Things

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This study proposed a lightweight but high-performance convolution network for accurately classifying five upper limb movements of arm, involving forearm flexion and rotation, arm extension, lumbar touch and no reaction state, aiming to monitoring patient's rehabilitation process and assist the therapist in elevating patient compliance with treatment. To achieve this goal, a lightweight convolution neural network TMCA-Net (Time Multiscale Channel Attention Convolutional Neural Network) is designed, which combines attention mechanism, uses multi-branched convolution structure to automatically extract feature information at different scales from sensor data, and filters feature information based on attention mechanism. In particular, channel separation convolution is used to replace traditional convolution. This reduces the computational complexity of the model, decouples the convolution operation of the time dimension and the cross-channel feature interaction, which is helpful to the target optimization of feature extraction. TMCA-Net shows excellent performance in the upper limb rehabilitation gesture data, achieves 99.11% accuracy and 99.16% F1-score for the classification and recognition of five gestures. Compared with CNN and LSTM network, it achieves 65.62% and 89.98% accuracy in the same task. In addition, on the UCI smartphone public dataset, with the network parameters of one tenth of the current advanced model, the recognition accuracy rate of 95.21% has been achieved, which further proves the light weight and performance characteristics of the model. The clinical significance of this study is to accurately monitor patients' upper limb rehabilitation gesture by an affordable intelligent model as an auxiliary support for therapists' decision-making.

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Section: Related Workmentioning

confidence: 99%

TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

Liu¹,

Wu²,

Liu³

2022

Journal on Internet of Things

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“…For offline training, the study used the same loss function as SiamRPN, with crossentropy loss for the classification branch and smooth L 1 loss for the regression branch. Assuming that A x , A y , A W , A h represents the coordinates of the anchor point, y A represents the probability of the anchor point predicting the presence of the target, T x , T y , T w , T h represents the coordinates of the calibration frame, and y T represents the probability of the presence of the true target, the regularized distance d[0], d [1], d [2], d [3] is as shown in Equation (11).…”

Section: A Singletarget Long-time Tracking Framework Based On Improve...mentioning

confidence: 99%

“…In a high-quality sports event, real-time acquisition of athletes' competition information plays a decisive role in coaches' personnel arrangement and tactical arrangement. However, nowadays, commonly used wearable GPS devices are very complex to operate, difficult to maintain, and it is difficult to ensure accuracy in long-term tracking [2]. With the development of science and technology, tracking technology has also been significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Target Tracking Algorithm Model for Athlete Training Trajectory

Wang

2022

Processes

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The main function of the athlete tracking system is to collect the real-time competition data of the athletes. Deep learning is a research hotspot in the field of image and video. With the rapid development of science and technology, it has not only made a breakthrough in theory, but also achieved excellent results in practical application. SiamRPN (Siamese Region Proposal Network) is a single target tracking network model based on deep learning, which has high accuracy and fast operation speed. However, in long-term tracking, if the target is completely obscured and out of the sight of SiamRPN, the tracking of the network will be invalid. Considering the difficulty of long-term tracking, the algorithm is improved and tested by introducing channel attention mechanism and local global search strategy into SiamRPN. Experimental results show that this algorithm has higher accuracy and prediction average overlap rate than the original SiamRPN algorithm when performing tracking tasks on long-term tracking sequences. At the same time, the improved algorithm can still achieve good results in the case of target disappearance and other challenging factors. This study provides an important reference for the coaches of deep learning to realize long-term tracking of athletes.

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“…With the progress of science and technology, various types of computer equipment play an increasingly prominent role in human life. All kinds of intelligent devices are indispensable to human life [1,2]. The Internet of Things (IoT) and virtual reality (VR), as the key technologies leading the future development of science and technology, are increasingly used in different intelligent devices.…”

Section: Introductionmentioning

confidence: 99%

The Application of the Gesture Analysis Method Based on Hybrid RF and CNN Algorithms in an IoT–VR Human–Computer Interaction System

2023

Processes

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With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human–machine interaction module for IoT–VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used in this study to build an intelligent gesture recognition model. The experiments were conducted to test the application performance of the design model. The test results show that the qualification rate of the analytical model designed in this study is significantly higher than that of the comparative model. When the threshold is determined to be 43.26 mm, the analytical qualification rates of the RF-CNN (the method of combining RF with CNN algorithms), faster regions with CNN features (Faster-RCNN), and RF models are 82.41%, 76.10%, and 59.10%, respectively. The calculation time of the RF–CNN model is between the two comparative models. From the test data, it can be observed that the research results have certain significance for improving the accuracy of gesture machine recognition technology in China’s VR Internet of Things (IoT) system.

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U-WeAr: User Recognition on Wearable Devices through Arm Gesture

Cited by 11 publications

References 38 publications

TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

Deep Learning Based Target Tracking Algorithm Model for Athlete Training Trajectory

The Application of the Gesture Analysis Method Based on Hybrid RF and CNN Algorithms in an IoT–VR Human–Computer Interaction System

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