When facing the task of conducting valid movement analyses, the high movement velocities in most sports are challenging. The use of optical video-based motion capture systems, for example, is usually accompanied by some crucial constraints, i. e. the necessity of a high sampling rate, the large data volume when using high frequency systems, the confined spatial frame, and the large effort required to extract the kinematic data (Krüger & Edelmann-Nusser, 2010; Wang et al., 2016). To minimize these drawbacks, and due to the ongoing miniaturization of integrated sensors, an increasing variety of sensor-based systems has been developed and used for motion capture in sports (e.g., Gawin, 2010; Jaitner & Gawin, 2010; Pei et al., 2017). In this context, the question arises whether these sensor systems provide the same accuracy and reliability as extensive optical video-based analyses. Taha, Hassan, Yap and Yeo (2016) combined an integrated sensor unit and the Kinect depth sensor that utilizes infrared light for motion capture. The kinematics of a subject’s wrist movements while executing badminton smash movement patterns with the upper limb were recorded simultaneously by the two systems. Even though the measured average accelerations differed between the two systems, the authors concluded that the outcome patterns revealed by the Kinect depth sensor were comparable to the values of the integrated sensors.
Another attempt to compare a sensor-based system and a common video-based system is a study by Kerner and Witt (2013) . To test the usability of a sensor-based whole-body system (Xsens Technologies, https://www.xsens.com/ ) for kinematic analyses, somersault movements of a female gymnast were recorded using this system and synchronously videotaped using a common videometry system (Simi Motion, http://www.simi.com/de/home.html ). A strong deviation between the two systems from 30 % up to 43 % was revealed for the center of gravity of the athlete´s body and the amplitudes of the arm movement velocities.
The analysis of movements in the racket sport badminton is also characterized by high movement velocities, especially when the shuttle, the racket, and the upper body segments are addressed (e. g. Kwan et al., 2011; Tsai & Chang, 1998). In elite badminton, shuttle velocities of approximately 100 m/s and racket speeds of more than 50 m/s were reported (Jaitner & Gawin, 2010; Kwan et al., 2011). A manufacturer of racket sport equipment has reported an initial shuttle speed of 116.9 m/s (Yonex Corporation, 2010). This value was recorded, while an international top player executed jump smashes, the stroke technique in badminton, where the highest shuttle velocities are generated (Tsai & Chang, 1998). When performing a jump smash, a player hits the shuttle as hard as possible downwards into the opponent´s court while airborne after a jump. The aim is to generate the highest possible shuttle velocity.
Because of these high movement velocities, there have been many possible solutions generated to obtain kinematic data using different sensor-based measurement systems in the field of badminton. Jaitner and Gawin (2010) developed a mobile measurement device to analyze the movements of the racket arm and the racket (Jaitner & Gawin, 2007, 2010). The usability of the mobile device that was based on two-dimensional piezoelectric accelerometers (working at a sample rate of 1000 Hz) that were attached to the racket arm and racket was evaluated using a three-dimensional high-frequency video system (Basler, sample rate 250 Hz, https://www.baslerweb.com/ ). The comparison between the obtained high-frequency video data and the values from the accelerometers revealed moderate correlations between the acceleration of the racket arm and the racket with the shuttle velocities (Jaitner & Gawin, 2010). The reliable recording of exact position data by the sensor based system, however, was not possible, because the utilized technology only included 2D-accelerometers, no gyroscopes and no magnetic field sensors.
Other approaches to determine kinematic or performance parameters using various sensor measurement set-ups were, for example, the combination of high-frequency videometry and strain gauges at the racket (Kwan et al., 2010; Kwan & Rasmussen, 2010), inertial sensors (Kiang et al., 2009; Wang et al., 2016), acoustic sensors (Kiang et al., 2009) or electrocardiographic equipment (Sakurai & Ohtsuki, 2000; Tsai et al., 2006; Tsai, Huang, et al., 2005; Tsai, Yang, et al., 2005, for an overview see Wang et al., 2016).
Meanwhile, in the follow-up of these scientific attempts to develop and evaluate reliable measurement devices for movement analysis in racket sports, there are now ready-made commercially available solutions. One example in badminton is a sensor racket made by Oliver (Plasma TX 5, https://www.oliver-sport.de/plasma-tx5-rds/ ). An inertial sensor unit has been integrated into the grip of this racket (figure 1) to record velocities, recognize stroke techniques, and measure physical activity.
This racket with the integrated sensor succeeded in matching the weight (88 g.), balance, and price of common badminton rackets without integrated sensors. The balance remains unaffected by the sensor unit, as the weight of the replaced grip material is similar to the weight of the sensor unit.
Regarding the difficulties determining valid kinematic data in the sport of badminton, the recent research project focuses on whether the output of the above-mentioned sensor racket agrees with high speed video data. The measurement of valid velocities would be a valuable tool for performance analysis in the training process of skilled badminton players.