An experimental study on a motion sensing system for sports training

In sports science, motion data collected from athletes is used to derive key performance characteristics, such as stride length and stride frequency, that are vital coaching support information. The sensors for use must be more accurate, must capture more vigorous events, and have strict weight and size requirements, since they must not themselves affect performance. These requirements mean each wireless sensor device is necessarily resource poor and yet must be capable of communicating a considerable amount of data, contending for the bandwidth with other sensors on the body. This paper analyses the results of a set of network traffic experiments that were designed to investigate the suitability of conventional wireless motion sensing system design � which generally assumes in-network processing - as an efficient and scalable design for use in sports training

A Comparison of Video and Accelerometer Based Approaches Applied to Performance Monitoring in Swimming.

The aim of this paper is to present a comparison of video- and sensor based studies of swimming performance. The video-based approach is reviewed and contrasted to the newer sensor-based technology, specifically accelerometers based upon Micro-Electro-Mechanical Systems (MEMS) technology. Results from previously published swim performance studies using both the video and sensor technologies are summarised and evaluated against the conventional theory that upper arm movements are of primary interest when quantifying free-style technique. The authors conclude that multiple sensor-based measurements of swimmers’ acceleration profiles have the potential to offer significant advances in coaching technique over the traditional video based approach

Intelligent middleware for adaptive sensing of tennis coaching sessions

In professional tennis training matches, the coach needs to be able to view play from the most appropriate angle in order to monitor players activities. In this paper, we present a system which can adapt the operation of a series of cameras in order to maintain optimal system performance based on a set of wireless sensors. This setup is used as a testbed for an agent based intelligent middleware that can correlate data from many different wired and wireless sensors and provide effective in-situ decision making. The proposed solution is flexible enough to allow the addition of new sensors and actuators. Within this setup we also provide details of a case study for the embedded control of cameras through the use of Ubisense data

Detection of Illegal Race Walking: A Tool to Assist Coaching and Judging

Current judging of race walking in international competitions relies on subjective human observation to detect illegal gait, which naturally has inherent problems. Incorrect judging decisions may devastate an athlete and possibly discredit the international governing body. The aim of this study was to determine whether an inertial sensor could improve accuracy, monitor every step the athlete makes in training and/or competition. Seven nationally competitive race walkers performed a series of legal, illegal and self-selected pace races. During testing, athletes wore a single inertial sensor (100 Hz) placed at S1 of the vertebra and were simultaneously filmed using a high-speed camera (125 Hz). Of the 80 steps analyzed the high-speed camera identified 57 as illegal, the inertial sensor misidentified four of these measures (all four missed illegal steps had 0.008 s of loss of ground contact) which is considerably less than the best possible human observation of 0.06 s. Inertial sensor comparison to the camera found the typical error of estimate was 0.02 s (95% confidence limits 0.01–0.02), with a bias of 0.02 (±0.01). An inertial sensor can thus objectively improve the accuracy in detecting illegal steps (loss of ground contact) and, along with the ability to monitor every step of the athlete, could be a valuable tool to assist judges during race walk events

Local Positioning Systems in (Game) Sports

Position data of players and athletes are widely used in sports performance analysis for measuring the amounts of physical activities as well as for tactical assessments in game sports. However, positioning sensing systems are applied in sports as tools to gain objective information of sports behavior rather than as components of intelligent spaces (IS). The paper outlines the idea of IS for the sports context with special focus to game sports and how intelligent sports feedback systems can benefit from IS. Henceforth, the most common location sensing techniques used in sports and their practical application are reviewed, as location is among the most important enabling techniques for IS. Furthermore, the article exemplifies the idea of IS in sports on two applications

Wearable inertial sensors and range of motion metrics in physical therapy remote support

Abstract. The practice of physiotherapy diagnoses patient ailments which are often treated by the daily repetition of prescribed physiotherapeutic exercise. The effectiveness of the exercise regime is dependent on regular daily repetition of the regime and the correct execution of the prescribed exercises. Patients often have issues learning unfamiliar exercises and performing the exercise with good technique. This design science research study examines a back squat classifier design to appraise patient exercise regime away from the physiotherapy practice. The scope of the exercise appraisal is limited to one exercise, the back squat. Kinematic data captured with commercial inertial sensors is presented to a small group of physiotherapists to illustrate the potential of the technology to measure range of motion (ROM) for back squat appraisal. Opinions are considered from two fields of physiotherapy, general musculoskeletal and post-operative rehabilitation. While the exercise classifier is considered not suitable for post-operative rehabilitation, the opinions expressed for use in general musculoskeletal physiotherapy are positive. Kinematic data captured with gyroscope sensors in the sagittal plane is analysed with Matlab to develop a method for back squat exercise recognition and appraisal. The artefact, a back squat classifier with appraisal features is constructed from Matlab scripts which are proven to be effective with kinematic data from a novice athlete

Application of Artificial Intelligence in Basketball Sport

Basketball is among the most popular sports in the world, and its related industries have also produced huge economic benefits. In recent years, the application of artificial intelligence (AI) technology in basketball has attracted a large amount of attention. We conducted a comprehensive review of the application research of AI in basketball through literature retrieval. Current research focuses on the AI analysis of basketball team and player performance, prediction of competition results, analysis and prediction of shooting, AI coaching system, intelligent training machine and arena, and sports injury prevention. Most studies have shown that AI technology can improve the training level of basketball players, help coaches formulate suitable game strategies, prevent sports injuries, and improve the enjoyment of games. At the same time, it is also found that the number and level of published papers are relatively limited. We believe that the application of AI in basketball is still in its infancy. We call on relevant industries to increase their research investment in this area, and promote the improvement of the level of basketball, making the game increasingly exciting as its worldwide popularity continues to increase

Multi-sensor human action recognition with particular application to tennis event-based indexing

The ability to automatically classify human actions and activities using vi- sual sensors or by analysing body worn sensor data has been an active re- search area for many years. Only recently with advancements in both fields and the ubiquitous nature of low cost sensors in our everyday lives has auto- matic human action recognition become a reality. While traditional sports coaching systems rely on manual indexing of events from a single modality, such as visual or inertial sensors, this thesis investigates the possibility of cap- turing and automatically indexing events from multimodal sensor streams. In this work, we detail a novel approach to infer human actions by fusing multimodal sensors to improve recognition accuracy. State of the art visual action recognition approaches are also investigated. Firstly we apply these action recognition detectors to basic human actions in a non-sporting con- text. We then perform action recognition to infer tennis events in a tennis court instrumented with cameras and inertial sensing infrastructure. The system proposed in this thesis can use either visual or inertial sensors to au- tomatically recognise the main tennis events during play. A complete event retrieval system is also presented to allow coaches to build advanced queries, which existing sports coaching solutions cannot facilitate, without an inordi- nate amount of manual indexing. The event retrieval interface is evaluated against a leading commercial sports coaching tool in terms of both usability and efficiency

Detection of throwing in cricket using wearable sensors

One of the great controversies of the modern game of cricket is the determination of whether a bowler is using an illegal throw-like bowling action. Changes to the rules of cricket have reduced some of the confusion, yet, because of the complexities of the biomechanics of the arm it is difficult for an umpire to make a judgement on this issue. Expensive laboratory based testing has been able to quantify the action of a bowler and this testing is routinely used by cricket authorities to assess a bowling action. Detractors of the method suggest that it is unable to replicate match conditions, has long lead times for assessment and is only available to the elite. After extensive laboratory validation we present a technology and method for an in- game assessment using a wearable arm sensor for differentiating between a legal bowling action and throwing. The method uses inertial sensors on the upper and lower arm that do not impede the bowling action. Suspect deliveries, as assessed by an expert biomechanist using high speed video and motion capture reveal valid distinctive inertial signatures. The technology is an important step in the monitoring of bowling action on-field in near real-time. The technology is suitable for use in competition as well as a training tool for developing athletes.Griffith Sciences, Griffith School of EngineeringFull Tex