Smart Wearables for Tennis Game Performance Analysis

For monitoring the progress of athletes in various sports and disciplines, several different approaches are nowadays available. Recently, miniature wearables have gained popularity for this task due to being lightweight and typically cheaper than other approaches. They can be positioned on the athlete’s body, or in some cases, the devices are incorporated into sports requisites, like tennis racquet handles, balls, baseball bats, gloves, etc. Their purpose is to monitor the performance of an athlete by gathering essential information during match or training. In this chapter, the focus will be on the different possibilities of tennis game monitoring analysis. A miniature wearable device, which is worn on a player’s wrist during the activity, is going to be presented and described. The smart wearable device monitors athletes’ arm movements with sampling the output of the 6 DOF IMU. Parallel to that, it also gathers biometric information like pulse rate and skin temperature. All the collected information is stored locally on the device during the sports activity. Later, it can be downloaded to a PC and transferred to a cloud-based service, where visualization of the recorded data and more detailed game/training statistics can be performed

Diferencias cinemáticas entre el revés a una y dos manos de tenis usando giróscopos. Un estudio exploratorio

The main objective of this article is to compare angular kinematics and intersegmental coordination of the upper limbs between one-handed and two handed backhands in a sample of 20 male competition players by using gyroscopes and compare ball speeds and accuracy obtained in both types of backhands. The angular kinematics, intersegmental coordination, ball speed and accuracy were compared during a specific stroke performance test using four inertial sensors (trunk, head, arm and forearm). We hypothesize that there will be significant differences in terms of ωpeak and intersegmental coordination in some of the segments measured between DH and SH by using gyroscopes, but the opposite will happen in the variables speed ball and accuracy. There are no significant differences between one-handed backhand and two-handed backhand in terms of speed and accuracy. Higher peaks angular speeds were found in the trunk and arm over the x axis in two-handed backhand which could indicate that this type of backhand generates greater trunk rotation and external rotation of the arm and forearm compared to one-handed backhand. The peak angular speeds were greater in the arm and forearm on the z axis in the case of one-handed backhand which is related to a greater extension of the forearm accompanied by a higher termination in the technical gesture. In conclusion, the proposed model of biomechanical analysis through the use of gyroscopes is especially useful for kinematic analysis of tennis strokes during field-based experimentation and could easily be adapted to other sports. It is also a low-cost and portable alternative that includes all instrumentation and data processing.El objetivo principal del presente estudio es comparar la cinemática angular y la coordinación intersegmentaria del tren superior entre el revés a una y dos manos de tenis en una muestra de 20 jugadores de nivel competición mediante el uso de giróscopos, y comparar las velocidades de pelota y la precisión obtenidas en ambos tipos de revés. La cinemática angular, la coordinación intersegmentaria, la velocidad de pelota y la precisión se obtuvieron de cada jugador mediante una prueba de golpeo realizada con cuatro sensores inerciales colocados (tronco, cabeza, brazo y antebrazo). Se sostiene la hipótesis de que se encontraran diferencias significativas en términos de ωpico y coordinación intersegmentaria en alguno de los segmentos intervinientes en el revés a una y dos manos, pero sucederá lo contrario en las variables velocidad de pelota y precisión. Tras el análisis de los resultados, no se encontraron diferencias significativas entre el revés a una y dos manos en velocidad de pelota y precisión. Sin embargo, se encontraron velocidades angulares pico significativamente más altas en el tronco y brazo sobre el eje x en el revés a dos manos, lo que podría indicar que este tipo de revés genera una rotación de tronco y una rotación externa de brazo y antebrazo mayores que las del revés a una mano. Las velocidades angulares pico fueron significativamente mayores en el brazo y antebrazo sobre el eje z en el caso del revés a una mano, lo cual está relacionado con una mayor extensión del antebrazo acompañada de una terminación más alta del gesto técnico. En conclusión, el modelo propuesto de análisis biomecánico a través del uso de giróscopos es especialmente útil para el análisis cinemático de los golpes de tenis en estudios de campo y podría adaptarse fácilmente a otros deportes, suponiendo una alternativa portable y de bajo coste que además incluye toda la instrumentación y procesamiento de los datos

Analysis of GPS and UWB positioning system for athlete tracking

In recent years, wearable performance monitoring systems have become increasingly popular in competitive sports. Wearable devices can provide vital information including distance covered, velocity, change of direction, and acceleration, which can be used to improve athlete performance and prevent injuries. Tracking technology that monitors the movement of an athlete is an important element of sport wearable devices. For tracking, the cheapest option is to use global positioning system (GPS) data however, their large margins of error are a major concern in many sports. Consequently, indoor positioning systems (IPS) have become popular in sports in recent years where the ultra-wideband (UWB) positioning sensor is now being used for tracking. IPS promises much higher accuracy, but unlike GPS, it requires a longer set-up time and its costs are significantly more. In this research, we investigate the suitability of the UWB-based localisation technique for wearable sports performance monitoring systems. We implemented a hardware set-up for both positioning sensors, UWB and the GPS-based (both 10 Hz and 1 Hz) localisation systems, and then monitored their accuracy in 2D and 3D side-by-side for the sport of tennis. Our gathered data shows a major drawback in the UWB-based localisation system. To address this major drawback we introduce an artificial intelligent model, which shows some promising results

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment

Motion synthesis for sports using unobtrusive lightweight body-worn and environment sensing

The ability to accurately achieve performance capture of athlete motion during competitive play in near real-time promises to revolutionise not only broadcast sports graphics visualisation and commentary, but also potentially performance analysis, sports medicine, fantasy sports and wagering. In this paper, we present a highly portable, non-intrusive approach for synthesising human athlete motion in competitive game-play with lightweight instru- mentation of both the athlete and field of play. Our data-driven puppetry technique relies on a pre-captured database of short segments of motion capture data to construct a motion graph augmented with interpolated mo- tions and speed variations. An athlete’s performed motion is synthesised by finding a related action sequence through the motion graph using a sparse set of measurements from the performance, acquired from both worn inertial and global location sensors. We demonstrate the efficacy of our approach in a challenging application scenario, with a high-performance tennis athlete wearing one or more lightweight body-worn accelerometers and a single overhead camera providing the athlete’s global position and orientation data. However, the approach is flexible in both the number and variety of input sensor data used. The technique can also be adopted for searching a motion graph efficiently in linear time in alternative applications

Detection of tennis activities with wearable sensors

This paper aims to design and implement a system capable of distinguishing between different activities carried out during a tennis match. The goal is to achieve the correct classification of a set of tennis strokes. The system must exhibit robustness to the variability of the height, age or sex of any subject that performs the actions. A new database is developed to meet this objective. The system is based on two sensor nodes using Bluetooth Low Energy (BLE) wireless technology to communicate with a PC that acts as a central device to collect the information received by the sensors. The data provided by these sensors are processed to calculate their spectrograms. Through the application of innovative deep learning techniques with semi-supervised training, it is possible to carry out the extraction of characteristics and the classification of activities. Preliminary results obtained with a data set of eight players, four women and four men have shown that our approach is able to address the problem of the diversity of human constitutions, weight and sex of different players, providing accuracy greater than 96.5% to recognize the tennis strokes of a new player never seen before by the system

Automatic activity classification and movement assessment during a sports training session using wearable inertial sensors

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete’s activities in an outdoor training environment. We firstly present a system that automatically classifies a large range of training activities using the Discrete Wavelet Transform (DWT) in conjunction with a Random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Secondly, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the thigh and shank of a subject, from which the flexion-extension knee angle is calculated. Finally, a curve shift registration technique is applied to both generate normative data and determine if a subject’s movement technique differed to the normative data in order to identify potential injury related factors. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Reliability of Zepp baseball on batting velocity / Raja Nurul Jannat Raja Hussain … [et al.]

In baseball and softball sports, one of the important characteristic of a successful hitter is having a fast bat swing velocity. Therefore, it is crucial to measure changes of batting velocity in training and during competitive matches. This paper quantifies the reliability of a small wearable sensor that was designed to be used in baseball and softball sports. An inertial measurement unit (IMU) manufactured by Zepp Lab, USA was used to measure the batting velocity in softball. A single female collegiate softball player completed sixty tee swings of two pre-determined swing velocities. Results from moderate velocity Vm (25.5 ± 3.5 m/s), and fast velocity range Vf (34.5 ± 3.5 m/s) were obtained. Data were collected concurrently with a 3- Dimensional (3D) motion analysis system (Qualisys Motion Capture). The reliability of the IMU was determined based on the Pearson Correlation and Intraclass Correlation (ICC) values between the IMU and the 3D data. Results indicated strong and moderate correlation between the IMU and the 3D data (Vm, r = 0.89; Vf, r = 0.59). The ICC for Vm (0.89) showed strong agreement, while fair agreement showed for Vf (0.37). However, for a total of sixty swings of two different velocities showed almost perfect agreement (ICC = 0.94). These results indicate that the Zepp Baseball device has an acceptable level of reliability in measuring batting velocity during different swing velocity ranges and it is suitable to be use in a softball-related research environment