Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

Hoelzemann, Alexander; Romero, Julia; Bock, Marius; Laerhoven, Kristof Van; Lv, Qin

doi:10.3390/s23135879

Cited by 13 publications

(4 citation statements)

References 86 publications

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“…Diverse methodologies have been investigated by researchers in order to develop wearable devices for basketball dribbling gesture detection. Hoelzemann et al developed a wearable sensor-based system that tracks hand movements during dribbling and provides real-time player tactics feedback [1]. Trigueiros et al expanded on this notion by incorporating machine learning algorithms for gesture detection, gaining more accuracy in distinguishing different dribbling patterns [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Wearable Basketball Dribbling Gesture Detection Method

2023

FSST

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This utility model reveals a wearable gesture-detection system for basketball dribbling that consists of a motion-sensing glove and a gesture-detection terminal. The gesture detection terminal comprises a Bluetooth signal receiver, the main Arduino controller, buttons, a power source, and a display screen. This technique provides a practical and valuable supplemental tool for basketball training and performance improvement by precisely capturing gesture movements during dribbling. Athletes can track and evaluate their dribbling motions in real time using the motion-sensing glove and terminal, leading to more accurate technique correction and progress. Basketball players will have improved training and performance opportunities thanks to this wearable, which will actively advance the sport.

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Section: Literature Reviewmentioning

confidence: 99%

Wearable Basketball Dribbling Gesture Detection Method

2023

FSST

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“…By integrating drill selection, real-time feedback, and performance analysis, the system empowers coaches and players to maximize their potential and achieve competitive success on the court. A Basketball Tactical Drill System is a sophisticated platform designed to optimize player skill development and team performance [11]. It offers a diverse range of customized drills tailored to address specific aspects of the game, providing players with targeted training opportunities to enhance their skills and tactical understanding.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm

Yan

2024

jes

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A basketball tactical drill system based on motion trajectory analysis algorithms employs advanced computational methods to enhance training effectiveness and strategy development. By utilizing these algorithms to analyze player movements and interactions during drills, coaches can gain valuable insights into team dynamics, spatial awareness, and decision-making processes. This analysis enables coaches to tailor drills to specific tactical objectives, such as improving defensive rotations, offensive spacing, or fast break execution. Additionally, the system can provide real-time feedback to players, helping them understand their positioning and movement patterns to refine their skills and decision-making abilities. This paper presents the design and implementation of a Basketball Tactical Drill System leveraging advanced object detection and motion trajectory analysis algorithms. The system aims to enhance basketball training by providing coaches and players with real-time insights into tactical drills and player performance. Two key algorithms, Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT), are utilized to accurately identify and track basketballs, players, and other relevant objects during training sessions. Experimental results demonstrate the effectiveness of the proposed system, showcasing high detection accuracy, minimal false positives, and competitive processing times across various scenarios and tactical drills. The system leverages Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT) algorithms to achieve exceptional detection accuracy, with an average of 93% across various scenarios and tactical drills. False positives are minimized, with an average of 12 per scenario. Processing times remain competitive, averaging 27 milliseconds per scenario. Integration of motion trajectory analysis provides coaches with invaluable real-time insights into player movements and interactions, facilitating data-driven coaching strategies. The system's versatility is demonstrated through successful application in a range of drills, resulting in performance improvements averaging 20%.

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“…HAR is an active field of research in pervasive computing that aims to detect human physical activities through machine learning models. HAR has various applications in healthcare [ 1 , 2 ], sports [ 3 , 4 ], industry [ 5 , 6 , 7 ], and other fields. Commonly, HAR models utilize activity signals recorded by wearable or visual sensors.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition

Azadi,

Haslgrübler,

Anzengruber-Tanase

et al. 2024

Sensors

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Learning underlying patterns from sensory data is crucial in the Human Activity Recognition (HAR) task to avoid poor generalization when coping with unseen data. A key solution to such an issue is representation learning, which becomes essential when input signals contain activities with similar patterns or when patterns generated by different subjects for the same activity vary. To address these issues, we seek a solution to increase generalization by learning the underlying factors of each sensor signal. We develop a novel multi-channel asymmetric auto-encoder to recreate input signals precisely and extract indicative unsupervised futures. Further, we investigate the role of various activation functions in signal reconstruction to ensure the model preserves the patterns of each activity in the output. Our main contribution is that we propose a multi-task learning model to enhance representation learning through shared layers between signal reconstruction and the HAR task to improve the robustness of the model in coping with users not included in the training phase. The proposed model learns shared features between different tasks that are indeed the underlying factors of each input signal. We validate our multi-task learning model using several publicly available HAR datasets, UCI-HAR, MHealth, PAMAP2, and USC-HAD, and an in-house alpine skiing dataset collected in the wild, where our model achieved 99%, 99%, 95%, 88%, and 92% accuracy. Our proposed method shows consistent performance and good generalization on all the datasets compared to the state of the art.

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Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

Cited by 13 publications

References 86 publications

Wearable Basketball Dribbling Gesture Detection Method

Wearable Basketball Dribbling Gesture Detection Method

Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm

Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition

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