Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.
BACKGROUND
Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules.


OBJECTIVE
To develop an automated stroke-classification system to help quantify hitting load in tennis.


METHODS
Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals.


RESULTS
Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types.


CONCLUSIONS
With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.
David
D
Whiteside
Olivia
O
Cant
Molly
M
Connolly
Machar
M
Reid
D000293:Adolescent; D054874:Athletic Performance / Q000502:physiology*; D001696:Biomechanical Phenomena; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D013715:Tennis / Q000502:physiology*; D014741:Video Recording; D000076251:Wearable Electronic Devices; D055815:Young Adult
IMU; accelerometer; activity; sport