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Reed D. Gurchiek, Hasthika S. Rupasinghe Arachchige Don, Lasanthi C. R. Pelawa Watagoda, Ryan S. McGinnis, Herman van Werkhoven, Alan R. Needle, Jeffrey M. McBride, and Alan T. Arnholt

velocity estimation error. The MIMU methods discussed here are limited by sensor imperfections and model assumptions. In other biomechanics contexts, some employ machine learning (ML) techniques both for classification 14 – 17 and regression. 18 – 24 Mannini and Sabatini 18 estimated running speeds less

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Anantha Narayanan, Farzanah Desai, Tom Stewart, Scott Duncan, and Lisa Mackay

but the use of cut points is still necessary when organizing these data into intensity categories. Manually defined algorithms have also been used to classify raw data into activity types with varying levels of success. 14 More recently, researchers have employed machine-learning techniques to

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Arne Jaspers, Tim Op De Beéck, Michel S. Brink, Wouter G.P. Frencken, Filip Staes, Jesse J. Davis, and Werner F. Helsen

Australian football (AFL) found that artificial neural networks (ANNs), a machine learning approach, more accurately predicted the RPE in response to ELIs compared with traditional statistics. 9 Other machine learning techniques could be used for this task as well, and each technique has strengths and

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Enrico Perri, Carlo Simonelli, Alessio Rossi, Athos Trecroci, Giampietro Alberti, and F. Marcello Iaia

. In recent years, data mining approaches have been gaining interest in sport science. Among these techniques, machine learning (ML) allows for the development of algorithms based on mathematical models able to discover multidimensional linear and nonlinear patterns in large data sets. 18 , 19

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Yumeng Li, Shuqi Zhang, and Christina Odeh

, it is still unclear which postural control variables could be used for the early detection of PD. Machine learning is a branch of artificial intelligence that enables computer systems to learn from data and analyze data without being explicitly programmed. Interest in machine learning has grown

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David Whiteside, Olivia Cant, Molly Connolly, and Machar Reid

Context:

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.

Purpose:

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.

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Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer

( Crouter, Horton, & Bassett, 2012 ; Freedson, Pober, & Janz, 2005 ) to machine learning models ( Mackintosh, Montoye, Pfeiffer, & McNarry, 2016 ; Trost, Wong, Pfeiffer, & Zheng, 2012 ), which use count-based or raw data as inputs. Machine learning models have generally yielded more accurate predictions

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Fahim A. Salim, Fasih Haider, Dees Postma, Robby van Delden, Dennis Reidsma, Saturnino Luz, and Bert-Jan van Beijnum

interactive multimodal feedback to coaches and players. The approach described in this paper precisely addresses the above issue. The context of the current paper is the Smart Sports Exercises project in which we aim to use multimodal sensor data and machine learning techniques to not only enable players and

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Ryan M. Chambers, Tim J. Gabbett, and Michael H. Cole

orientation criteria. The second step extracted features of the accelerometer and gyroscope signals from each event. These calculations included summary statistics using different time windows based on the event time stamp and formed the 33 variables for the machine learning process. Variable selection was

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Ferdous Wahid, Rezaul Begg, Noel Lythgo, Chris J. Hass, Saman Halgamuge, and David C. Ackland

Normalization of gait data is performed to reduce the effects of intersubject variations due to physical characteristics. This study reports a multiple regression normalization approach for spatiotemporal gait data that takes into account intersubject variations in self-selected walking speed and physical properties including age, height, body mass, and sex. Spatiotemporal gait data including stride length, cadence, stance time, double support time, and stride time were obtained from healthy subjects including 782 children, 71 adults, 29 elderly subjects, and 28 elderly Parkinson’s disease (PD) patients. Data were normalized using standard dimensionless equations, a detrending method, and a multiple regression approach. After normalization using dimensionless equations and the detrending method, weak to moderate correlations between walking speed, physical properties, and spatiotemporal gait features were observed (0.01 < |r| < 0.88), whereas normalization using the multiple regression method reduced these correlations to weak values (|r| < 0.29). Data normalization using dimensionless equations and detrending resulted in significant differences in stride length and double support time of PD patients; however the multiple regression approach revealed significant differences in these features as well as in cadence, stance time, and stride time. The proposed multiple regression normalization may be useful in machine learning, gait classification, and clinical evaluation of pathological gait patterns.