A multimedia tennis instruction system: tracking and classifying swing motions

Takano, Kosuke; Li, Kin Fun

doi:10.1504/ijssc.2013.056406

Cited by 13 publications

(3 citation statements)

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“…Different methods and models, such as Neural Networks, Hidden Markov models, and Support Vector Machines, have proven to be appropriate for this purpose (see Activity classification for further examples). This approach is essential for feedback systems aiming to recognise and evaluate the quality of specific techniques in the field [ 301 ], and eventually during technical–tactical sessions [ 306 ]. Machine learning can also automatically provide feedback on how to continue exercising and/or adjust smart sports equipment, depending on environmental conditions [ 367 ].…”

Section: Discussionmentioning

confidence: 99%

“…synthesis of athlete motion in competitive game-play by finding related action sequences in a database of reference motions using a sparse set of measurements from the performance (exemplified for tennis shots [ 177 , 301 ])…”

Section: Trendsmentioning

confidence: 99%

“…Activity classification has been performed by measuring body segments in relative or absolute orientation, to detect sprinting, jogging, walking [ 52 , 195 , 232 , 326 ], jumping, cutting, kicking [ 52 ], dribbling, ball strike, tackling [ 232 ], swimming style [ 164 , 304 ], trampoline routines [ 161 ], climbing phases [ 114 ], rugby collisions [ 176 ], volleyball actions [ 306 ], tennis strokes [ 301 ], golf putting [ 171 ], and aerial manoeuvres in half-pipe snowboarding [ 159 ] and skateboarding [ 154 ].…”

Section: Trendsmentioning

confidence: 99%

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Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Camomilla

Bergamini

Fantozzi

et al. 2018

Sensors

380

302

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Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

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Section: Discussionmentioning

confidence: 99%

Section: Trendsmentioning

confidence: 99%