Title of article :
Standing Handball Throwing Velocity Estimation with a Single Wrist-Mounted Inertial Sensor
Author/Authors :
Gençoğlu, Celal Department of Physical Education and Sport Teacher - Faculty of Sport Sciences - Dokuz Eylül University, Turkey , Gümüş, Hikmet Department of Coaching Education - Faculty of Sport Sciences - Dokuz Eylül University, Turkey
Abstract :
It is well known that overarm throwing is one of the most performed activities in the handball. Shoulder
and glenohumeral injuries incidence are high in handball because of both pass, and shooting activity was executed
repeatedly in high angular speed. Objectives. This study set out to investigate the usefulness of inexpensive
commercial inertial movement sensors for prediction of throwing velocity in handball. Methods. After the IMU sensor
(500 Hz) placed to the wrist of the dominant arm, players (n=4; 24.4 ±1.4 years, 181.75 ±11 cm height, 84.58 ±16 kg
weight) performed 30 standing overarm throwing from a seven-meter distance with 1-minute rest between trials.
Throwing velocity compared between radar speed gun and estimations of accelerometer data. Recorded acceleration
data filtered (Butterworth 20 Hz 2nd order) than the acceleration vector magnitude calculated. Each throwing data
aligned such as 125 data points of before and after the peak acceleration (250ms). Performance metrics of prediction
models (Generalized Linear Model, Gradient Boosted Trees, and Support Vector Machine) calculated with root mean
square, absolute error, and correlation coefficient parameters. Results. There were reasonably small absolute errors
and root mean square values of the machine learning models. Also, there was a very high correlation between measured
and predicted velocities with all three models. Conclusion. This is the first study to examined machine learning models
to predict handball throwing velocity using a high-frequency triaxial accelerometer. The finding of the present study
revealed that the wrist-attached accelerometer precisely estimates the throwing velocity in handball. Further research
is required to quantifying the overarm activities in handball, which included block, defensive contact, passing, or
shooting. Therefore, the accelerometer-based collected data may provide detection of movement in game-play
automatically so that the upper extremity load of players can be monitored and avoid the possible overuse injury risk.
Keywords :
Throwing Velocity , Machine Learning , Tri-axial Accelerometer , Inertial Sensor
Journal title :
Annals of Applied Sport Science