Using Wearable Inertial Sensors to Estimate Kinematic Parameters and Variability in the Table Tennis Topspin Forehand Stroke

Abstract

The study examined kinematic parameters and their inter- and intrasubject variability in the topspin forehand of seven top-level table tennis players. A wireless inertial measurement unit (IMU) system measured the movement of the playing hand to analyze the Ready position, Backswing, and Forward events, and a racket-mounted piezoelectric sensor captured the racket-ball Contact. In a four-phase cycle (Backswing, Hitting, Followthrough, and Back to Ready position), body sensors recorded the cycle and phase duration; angles in the sagittal plane at the shoulder, elbow, and wrist of the playing hand and at the knee joints; and acceleration of the playing hand at the moment of racket-ball contact. The coefficient of variation (CV) was calculated to determine the variability of kinematic parameters within and between players. The observed variability in stroke time duration was low (CV<20%) indicating constancy. The small-to-medium intraindividual variability of angles (CV<40%) indicates that each player used a broadly repeatable technique. The large intraindividual variability in movement was probably functional (i.e., motor adjustment and injury avoidance). Interindividual and intraindividual variability of knee and elbow angles was low; wrist extension was the most variable parameter (CV>40%) for all tasks, and shoulder joint variability was medium-to-large. Variability in hand acceleration was low (CV<20%). Individual players achieved relatively constant hand acceleration at the moment of contact, possibly because angular changes at one joint (e.g., shoulder) could be compensated for by changes at another (e.g., wrist). These findings can help to guide the teaching-learning process and to individualize the training process.

Statistical Parametric Mapping Reveals Subtle Gender Differences in Angular Movements in Table Tennis Topspin Backhand

“…The course of movements in the analyzed joints was accompanied by a large ( p < 0.01) variability between the two groups determined using the ANOVA SPM test. Perhaps this was also caused by the sizable intragroup differentiation in the groups of men and women, a phenomenon proved by the high SD values and emphasized in our previous studies [ 26 , 37 ]. The differences revealed by the SPM test are likely also related to a different time of occurrence of events and duration of individual phases.…”

“…Iino, Yioshioka, and Fukashimo emphasized that the possibility of using different configurations in the evaluated joints to stabilize the vertical angle of the racket in table tennis strokes can be a critical factor in playing performance [ 3 ]. A previous study of Bańkosz and Winiarski also evaluated the variability of movement by analyzing the coefficient of variation of kinematic parameters in selected important moments of the hitting movement [ 26 ]. However, the coordination of movements in individual joints was not taken into account.…”

Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes

“…Their proposed model achieved the highest overall accuracy with 86%. Moreover, according to Z. Bankosz and S. Winiarski [7] they proposed a system to be examine in the topspin forehand by using an IMU device which is a wearable inertial sensor to estimate and measure the movements of the player's hand and analysing different positions. There observation indicates that the highest variability is in the wrist joint extension.…”

Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network

“…Studies have used various sensors and data analysis methods for table tennis observations, such as video analysis to track the ball’s trajectory during service [ 11 ]; force sensors and electronic circuits to monitor the net [ 12 , 13 ] and detect ball–net impact during service [ 13 ]; inertial sensors mounted on the paddle to assess the ball’s speed and spin [ 14 ], estimate the trajectory of the paddle [ 15 ], and detect the type of stroke [ 16 , 17 ]; and inertial sensors worn on the limbs to detect shots [ 18 ], estimate kinematic parameters [ 19 ], and recognize stroke motions [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Machine learning classification algorithms are a reliable approach for recognizing basic human motions.…”