A Wearable Device to Detect in Real-Time Bimanual Gestures of Basketball Players During Training Sessions

Mangiarotti, Marco; Ferrise, Francesco; Graziosi, Serena; Tamburrino, Francesco; Bordegoni, Monica

doi:10.1115/1.4041704

Cited by 18 publications

(33 citation statements)

References 32 publications

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“…The motion editing algorithm is based on the human motion tracking technology to generate a new action sequence that is realistic, vivid and in line with user requirements. We not only require the synthesized human movement to be natural and realistic, but also hope to provide the user with a programming interface for human-computer interaction and adjust the corresponding actions according to the user's requirements [21,22]. The GRF value of ADAMS simulation simulation jumping action and the measured GRF curve of the force plate are shown in Table 1 and Figure 1.…”

Section: A Impact Of Vr Technology On Basketball Trainingmentioning

confidence: 99%

Feasibility Analysis of VR Technology in Basketball Training

Wang

Liu

2024

IEEE Access

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With the rapid development of virtual reality technology in multimedia technology, sports simulation teaching has attracted more and more attention. VR technology provides advanced and scientific training methods for teaching workers, and basketball train ing simulation also promotes the development of computer technology. This article mainly studies the feasibility analysis of VR technology in basketball training. First, a human basketball training model is established, and then the image is preprocessed. The denoised background grayscale image and the target frame grayscale image are used as experimental objects, and the difference operation of the two images is performed using the background difference principle described earlier. Finally, the bwlabel function is used to mark the coherent area in the differential binary image, and the area of the area can be calculated. Experimental data shows that when using the kinematic equation model to simulate human motion, the maximum iEMG is 15.5 and the maximum RMS is 19. The results show that VR technology makes the basketball training process more efficient, and athletes can interact with the virtual environment in a natural way.

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Section: A Impact Of Vr Technology On Basketball Trainingmentioning

confidence: 99%

Feasibility Analysis of VR Technology in Basketball Training

Wang

Liu

2024

IEEE Access

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“…To verify the accuracy and effectiveness of the proposed system, it was compared with references [18,19,38]. Reference [18] established a real-time wearable assist system for upper extremity throwing action based on accelerometers, which used the longest common subsequence (LCS) algorithm to recognize the six phases of baseball throwing 13 Journal of Sensors posture.…”

Section: Foot-acc Xmentioning

confidence: 99%

“…In [19], an activity assessment chain for evaluating human activity was established using machine learning (ML) to classify six types of indoor rowing stroke postures (one correct and five incorrect). Reference [38] used a wearable and wireless system based on SVM to recognize overhead passes, chest passes, and shooting in basketball. As shown in Table 7, similar to the three systems above, the system proposed in this paper uses a small number of sensors to recognize a number of postures.…”

Section: Foot-acc Xmentioning

confidence: 99%

Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

et al. 2021

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In recent years, with the development of wearable sensor devices, research on sports monitoring using inertial measurement units has received increasing attention; however, a specific system for identifying various basketball shooting postures does not exist thus far. In this study, we designed a sensor fusion basketball shooting posture recognition system based on convolutional neural networks. The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition. We collected 12,177 sensor fusion basketball shooting posture data entries of 13 Chinese adult male subjects aged 18–40 years and with at least 2 years of basketball experience without professional training. We then trained and tested the shooting posture data using the classic visual geometry group network 16 deep learning model. The intratest achieved a 98.6% average recall rate, 98.6% average precision rate, and 98.6% accuracy rate. The intertest achieved an average recall rate of 89.8%, an average precision rate of 91.1%, and an accuracy rate of 89.9%.

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“…Additionally, only papers referring to the direct scanning of the subject’s anatomical region were considered, discarding all the papers that exploit the acquisition of plaster casts to obtain 3D digital models. Hand pose estimation and tracking, which also represents a wide research area [ 14 , 15 , 16 ], was not considered in this work. It must be noted that this review aims to cover the devices and architectures that have been used at least once to create a virtual model of the patient’s upper limb anatomy, rather than addressing all the existing papers which use a 3D scanning process for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review

Paoli

Neri

Razionale

et al. 2020

Sensors

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3D digital models of the upper limb anatomy represent the starting point for the design process of bespoke devices, such as orthoses and prostheses, which can be modeled on the actual patient’s anatomy by using CAD (Computer Aided Design) tools. The ongoing research on optical scanning methodologies has allowed the development of technologies that allow the surface reconstruction of the upper limb anatomy through procedures characterized by minimum discomfort for the patient. However, the 3D optical scanning of upper limbs is a complex task that requires solving problematic aspects, such as the difficulty of keeping the hand in a stable position and the presence of artefacts due to involuntary movements. Scientific literature, indeed, investigated different approaches in this regard by either integrating commercial devices, to create customized sensor architectures, or by developing innovative 3D acquisition techniques. The present work is aimed at presenting an overview of the state of the art of optical technologies and sensor architectures for the surface acquisition of upper limb anatomies. The review analyzes the working principles at the basis of existing devices and proposes a categorization of the approaches based on handling, pre/post-processing effort, and potentialities in real-time scanning. An in-depth analysis of strengths and weaknesses of the approaches proposed by the research community is also provided to give valuable support in selecting the most appropriate solution for the specific application to be addressed.

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A Wearable Device to Detect in Real-Time Bimanual Gestures of Basketball Players During Training Sessions

Cited by 18 publications

References 32 publications

Feasibility Analysis of VR Technology in Basketball Training

Feasibility Analysis of VR Technology in Basketball Training

Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review

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