A real-time tennis level evaluation and strokes classification system based on the Internet of Things

Wu, Mingyue; Fan, Mingxia; Hu, Yang; Wang, Ran; Yufan, Wang; Li, Yanchun; Wu, Shengyuan; Xia, Guowen

doi:10.1016/j.iot.2021.100494

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…This would further support the notion of the magnitude and timing of trunk rotation as key features of interest, whilst explaining the difficulty of the wearable sensor's position on the spine to accurately classifying volleys, given the negligible trunk movement in this stroke [36,37]. Similar degradations in volley detection accuracy also exist from wrist-worn sensors in samples of elite and sub-elite players, with precision rates of ≈70-80% [3,38]. The impact on tennis practitioners remain unclear though given volleys contribute <2% of strokes per match [2], yet are commonly featured in training drill prescription [25].…”

Section: Discussionmentioning

confidence: 53%

Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions

Perri

Reid

Murphy

et al. 2022

Sensors

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This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement drills. Prototype algorithms classified stroke (i.e., forehand, backhand, serve) and movement (i.e., “Alert”, “Dynamic”, “Running”, “Low Intensity”) events. Manual coding evaluated stroke actions in three classes (i.e., forehand, backhand and serve), with additional descriptors of spin (e.g., slice). Movement data was classified according to the specific locomotion performed (e.g., lateral shuffling). The algorithm output for strokes were analysed against manual coding via absolute (n) and relative (%) error rates. Coded movements were grouped according to their frequency within the algorithm’s four movement classifications. Highest stroke accuracy was evident for serves (98%), followed by groundstrokes (94%). Backhand slice events showed 74% accuracy, while volleys remained mostly undetected (41–44%). Tennis-specific footwork patterns were predominantly grouped as “Dynamic” (63% of total events), alongside successful linear “Running” classifications (74% of running events). Concurrent stroke and movement data from wearable sensors allows detailed and long-term monitoring of tennis training for coaches and players. Improvements in movement classification sensitivity using tennis-specific language appear warranted.

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Section: Discussionmentioning

confidence: 53%

Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions

Perri

Reid

Murphy

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…To avoid altering the racket's weight or feel with attached sensors, we chose motion tracking technology worn on the hand (Perception Neuron studio) to measure racket movements. This decision was informed by studies in racket sports [69][70][71][72][73] , where IMU sensors on the hand provided stroke classification performance comparable to sensors on the racket 74 , and in some cases, even superior correlation with player performance-related measures 72 . This approach allows us to gather essential swing information via IMU sensor-based data from the hand, maintaining the racket's natural feel and balance during play and offering proxy measures for racket dynamics.…”

Section: Question 3 How Do You Give Feedback To Trainees During Train...mentioning

confidence: 99%

MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton Performance

Seong,

Kim,

Yeo

et al. 2024

Sci Data

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The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

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“…Nowadays, there are more and more instruments and equipment on the market that support network operation, and remote monitoring can be easily realized through network operation. The use of network measurement and control technology, supported by hardware and software, can control the parameters of the lithium battery testing process in real time, and can also achieve remote monitoring for some dangerous tests at a distance 5 . During the test, the operator can be far away from the test site and the data transmission and network control functions can be realized with the following tools.…”

Section: Iot Measurement and Control Technologymentioning

confidence: 99%

Lithium battery transport test analysis and equipment optimization exploration

Li¹,

Wang²,

Fu³

et al. 2023

Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023)

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China is one of the world's largest producers of lithium batteries, and with the widespread use of batteries, the transportation of lithium battery cargoes has attracted great attention. The UN38.3 standard, as a safety test basis for the transportation of lithium batteries widely used worldwide, aims to clarify the requirements for the transportation of lithium battery cargo and to ensure its safe transportation by air and land cargo. This paper will introduce the importance of lithium battery testing and various testing items, and discuss the practicalities of using the UN38.3 standard as a basis for analysing the practice of testing in the manufacturing industry and discussing how to use automated and intelligent technology to carry out testing and testing processes for reference in the manufacturing industry.

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A real-time tennis level evaluation and strokes classification system based on the Internet of Things

Cited by 9 publications

References 28 publications

Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions

Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions

MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton Performance

Lithium battery transport test analysis and equipment optimization exploration

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