An IMU-based mobile system for golf putt analysis

Jensen, Ulf; Schmidt, Marcus; Hennig, Markus; Dassler, Frank A.; Jaitner, Thomas; Eskofier, Bjoern M.

doi:10.1007/s12283-015-0171-9

Cited by 21 publications

(14 citation statements)

References 18 publications

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“…IMU-based studies (Adelsberger & Tröster, 2013;Anand, Sharma, Srivastava, Kaligounder, & 333 Prakash, 2017;Buckley et al, 2017; 334 Groh et al, 2016;Groh, Fleckenstein, Kautz, & Eskofier, 2017;Groh, Kautz, & Schuldhaus, 2015;Jensen et al, 2016Jensen et al, , 2015Jiao, Wu, Bie, Umek, & Kos, 2018;Kautz et al, 2017;Kobsar et al, 2014;336 M. A. O'Reilly et al, 2017a;Ó Conaire et al, 2010;Pernek, Kurillo, Stiglic, 337 & Bajcsy, 2015;Qaisar et al, 2013;Salman et al, 2017;Schuldhaus et al, 2015). Methods included, 338 calibration of data (Groh et al, 2016(Groh et al, , 2017Jensen et al, 2015;Qaisar et al, 2013), a one-second 339 window centred around identified activity peaks in the signal (Adelsberger & Tröster, 2013; 340 Schuldhaus et al, 2015), temporal alignment (Pernek et al, 2015), normalisation (Ó Conaire et al, 341 2010), outlier adjustment (Kobsar et al, 2014) or removal (Salman et al, 2017), and sliding windows 342 ranging from one to 3.5 seconds across the data (Jensen et al, 2016). The three studies that 343 investigated trick classification in skateboarding (Groh et al, 2017(Groh et al, , 2015…”

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confidence: 99%

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Cust

Sweeting

Ball

et al. 2018

Journal of Sports Sciences

183

126

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Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sportspecific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data preprocessing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental setup , data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).

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confidence: 99%

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Cust

Sweeting

Ball

et al. 2018

Journal of Sports Sciences

183

126

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“…For non-cyclic tasks, temporal parameters were identified to measure critical temporal events (blade–puck contact time in ice hockey [ 206 , 309 ], cricket bowling [ 227 ] and, more specifically, ball release [ 288 ]), or detect task phases and critical events (in ski jumping [ 90 , 91 ], half-pipe snowboard [ 159 ], bowling [ 180 ], baseball swing [ 146 , 179 ], instep kick [ 228 ], karate front kick [ 273 ], diving trampoline jumps [ 161 ], artistic gymnastics springboard jumps [ 187 ], golf [ 171 ], javelin throw [ 270 ], soccer turning manoeuvres [ 241 ], swimming tumble turn [ 192 , 197 , 285 ] and start [ 193 ] and cricket bowling [ 264 ]).…”

Section: Trendsmentioning

confidence: 99%

“…The estimation of these parameters entails coping with the typical sensor errors but does not entail soft tissue wobbling. Orientation and kinematics have been estimated for several activities: alpine skiing (ski inclination in a lateral direction with respect to the Earth’s fixed coordinate system, describing the skis’ steering and edging manoeuvres [ 189 , 231 ]) baseball (bat position, orientation, linear velocity during the swing [ 179 ]; ball’s velocity at release [ 221 ]) bowling (ball velocity [ 180 ]) combat sports (acceleration of the boxing bag at the time of the punch [ 79 , 239 , 284 ]) cricket bowling (outward acceleration at ball release [ 288 ]) cycling (roll angle [ 330 ], crank angle [ 95 ]) ice hockey (puck velocity [ 309 ]) fencing (foil speed during lounge and touche [ 329 ]) golf (clubface orientation, head velocity, and position [ 181 ]; clubface orientation, head velocity and acceleration [ 171 ]) gymnastics vaulting (springboard kinematics during jump on and take-off [ 187 ]) javelin throw (javelin kinematics during the throw [ 270 ]) kayaking (instantaneous boat velocity [ 169 ]) rowing (boat acceleration [ 81 , 205 , 260 , 275 , 287 ], orientation [ 287 ], velocity [ 217 , 287 ], forward movement and balancing, propulsion, and distance travelled [ 217 ], as well as boat–oar stroke angle [ 205 , 287 ], oar acceleration [ 205 …”

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

338

271

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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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“…Results show that the instrument can be a promising tool for serving as a training assistant tool for golfers. Authors in Jensen [14] presented a device for golf put analysis. The device uses a removable sensor, and it is built solely from off-the-shelf components.…”

Section: Introductionmentioning

confidence: 99%

Smart Wearables for Tennis Game Performance Analysis

Kos¹,

Kramberger²

2020

Sports Science and Human Health - Different Approaches

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For monitoring the progress of athletes in various sports and disciplines, several different approaches are nowadays available. Recently, miniature wearables have gained popularity for this task due to being lightweight and typically cheaper than other approaches. They can be positioned on the athlete's body, or in some cases, the devices are incorporated into sports requisites, like tennis racquet handles, balls, baseball bats, gloves, etc. Their purpose is to monitor the performance of an athlete by gathering essential information during match or training. In this chapter, the focus will be on the different possibilities of tennis game monitoring analysis. A miniature wearable device, which is worn on a player's wrist during the activity, is going to be presented and described. The smart wearable device monitors athletes' arm movements with sampling the output of the 6 DOF IMU. Parallel to that, it also gathers biometric information like pulse rate and skin temperature. All the collected information is stored locally on the device during the sports activity. Later, it can be downloaded to a PC and transferred to a cloud-based service, where visualization of the recorded data and more detailed game/training statistics can be performed.

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An IMU-based mobile system for golf putt analysis

Cited by 21 publications

References 18 publications

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

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

Smart Wearables for Tennis Game Performance Analysis

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