With the increasing rise of professionalism in sport, teams and coaches are looking to technology to monitor performance in both games and training to find a competitive advantage. Wheelchair court sports (wheelchair rugby, wheelchair tennis, and wheelchair basketball) are no exception, and the use of microelectromechanical systems (MEMS)-based inertial measurement unit (IMU) within this domain is one innovation researchers have employed to monitor aspects of performance. A systematic literature review was conducted which, after the exclusion criteria was applied, comprised of 16 records. These records highlighted the efficacy of IMUs in terms of device validity and accuracy. IMUs are ubiquitous, low-cost, and non-invasive. The implementation in terms of algorithms and hardware choices was evidenced as a barrier to widespread adoption. This paper, through the information collected from the systematic review, proposes a set of implementation guidelines for using IMUs for wheelchair data capture. These guidelines, through the use of flow-charts and data tables, will aid researchers in reducing the barriers to IMU implementation for propulsion assessment.

This paper presents the design and implementation of wavelet coherence (WC) processor on low cost field-programmable gate array (FPGA). This design is adapted to estimate the wavelet coherence between two EEG signals in a minimal delay in order to support real time applications. The produced CWT coefficients were saved in static RAM chips and prepared for the WC analysis starting with the smoothing operation as an essential computation for the WC algorithm. The WC algorithm was analyzed in the means of choosing the suitable word length for the stages of the design and to simplify the employed functions in the algorithm. Several controllers that handle signal transmission among the design components were designed using hardware description language (VHDL). By using 4 parallel-processing smoothing circuits, the design is capable to calculate the coherogram between two EEG signals (1024 point each) in a total time of 128.64 ms. Image quality methods were applied for coherogram comparison between hardware and software. Hardware results were compared against the rigorous software standard WC according to the following measures; normalized mean square error (NMSE), normalized average difference (NAD) and structural content (SC) are 0.0045, 0.0485 and 0.921 respectively. (C) 2017 Elsevier B.V. All rights reserved.

Technology to aid the acquisition and performance of motor skills is becoming increasingly commonplace however there is distinct disconnect between these technological interventions and a detailed understanding of how to design technology to best instruct a learner. Using a single inertial sensor with bespoke concurrent visual feedback, based in a MATLAB data visualisation environment, this paper presents a skill acquisition framework to facilitate home based physiotherapy interventions. When athletes and patients are prescribed at home based physiotherapy interventions the current literature reports low rates of adherence. In addition the lack of monitoring and exercise classification raises concerns towards the quality of rehabilitation program outcomes. A trial was conducted randomly assigning twenty two uninjured participants to two categories, one with the aid of the rehabilitation software and the other a control group with no feedback. Both groups received the same visual instructions on the three simple leg static stretching tasks that are indicative of lower limb injury physiotherapy interventions. The results showed statically significant improvements in both the program adherence as well as the error mitigation of the feedback group in comparison to the control. Substantiating the skill acquisition framework errors for the feedback group seemed to lessen over time synonymous with an immediate learning effect as a result of the concurrent feedback. The findings suggest that at home physiotherapy interventions could be enhanced by using a concurrent biofeedback skill acquisition based single inertial sensor system. Evidently improving adherence, technique and allowing for the data to be accrued over time and relayed to practitioners and coaching staff ultimately giving them heightened confidence in monitoring physiotherapy progress. The wider implications means this research could be useful in tracking and providing feedback for a range of sports injury circumstances to ultimately improve the outcomes of the physiotherapy interventions.

In this paper, we propose a novel Attitude and Heading Reference System (AHRS) based analysis method for sport wheelchair propulsion. Wireless inertial measurement units were attached to each wheel and an AHRS calculation was used to obtain the rotation angle of the sensors. The sensor rotation angle from the AHRS contains both the wheel rotation angle and the wheelchair turn angle in the horizontal plane. A coordinate system conversion from global coordinates to viewing coordinates was required to extract the wheel rotation angle. Both the distance travelled and the velocity were calculated using the angle. Four different trials were performed with two inertial sensors on each wheel. Straight-line distance tests gave high agreement among four sensors (28.164±0.028m). The difference between two sensors on the same wheel did not increase even for trials over 10 minutes, which implies a reduction in the effect of gyroscope drift. The velocity curves demonstrate agreement for acceleration and deceleration accompanied by push and traveling resistance.

This paper presents accelerometers as a viable alternative or add-on in the quest for improved athlete assessment techniques. In elite level team sports, global positioning systems (GPSs) are an important tool for workload management. These devices, however, have limitations particularly when it comes to high speeds or indoor environments. Tri-axial accelerometer data were synchronously collected with GPS data from senior elite level Australian Football League athletes. For each athlete (n=44), the accelerometer data were filtered and the step frequency was extracted for periods of constant GPS running speed (>8 km/h). A quadratic fit was observed and applied to each athlete, modeling their step frequency to running speed (mean r^2 =0.8554 ± 0.064). The resulting model showed increased accuracy at higher speeds (speeds above 15 km/h <;10% error and speeds above 20 km/h <;5% error).

Wireless tracking of players for indoor team sports can provide information for both coaching assistance and individual improvement. Body worn accelerometer sensors have been used extensively to monitor and provide information such as body movements and heart rate. By also employing a beacon valuable data like player positioning and offensive play checks in the game of basketball can be provided using radio-frequency interference (RF) signal positioning techniques. Positional accuracy from a previous study using uncertainty ellipses, not yet published, shows signal variations of 3 dB having little impact on the position estimation error for a single player. The introduction of multiple moving players in the vicinity to the player worn beacon shows a variation (with no obstruction) of <3 dB for a distance of 5.5 m confirming a minimal impact from multipath interference. This equates to a localization error of similar to 1 m or less. In depth fading studies show favorable statistical results. Previous sensor accelerometer studies show that a unique signal pattern can be used to determine if a player is bouncing the ball. Further investigation in this study shows this can also be applied to passing the ball. A prototype player tracking system was tested and confirmed wireless RF signal tracking of offensive players is viable. The results of this study can be used in design considerations for other systems employing wearable wireless sensors and beacons to estimate position and body actions for team players in basketball and potentially other sports within the indoor environment.

A memory efficient field programmable gate array (FPGA) method is described that facilitates the processing of the continuous wavelet transform (CWT) arithmetic operations. The CWT computations were performed in Fourier space and implemented on FPGA following several optimization schemes. First, the adapted wavelet function was stored in a lookup table instead of computing the equation each time. Second, the utilization of FPGA memory was highly optimized by only storing the nonzero values of the wavelet function. This reduces 89% of the memory storage and allows fitting the entire design into the FPGA. Third, the design decreases the number of multiplications and shortens the time to produce the CWT coefficients. The proposed design was tested using EEG data and demonstrated to be suitable for extracting features from the event related potentials. Fourth, wavelet function scales were eliminated which saves further resources. The achieved computation speed allows for real time CWT application.

Wireless player tracking is being considered for use within indoor team sports for coaching assistance. Few research studies exist into wireless RF signal tracking of players in indoor team sports, in particular the game of basketball. Utilising the RF signal trilateration positioning technique with three anchor nodes per quarter court, players wearing RF beacons could be tracked and their positional data recorded in real time. This study builds on a previous study conducted under static conditions by Kirkup et al. (2013). Identifying the player with the ball is also important in recording and assessing team strategies. Accelerometer sensors located on the body have been widely used to monitor the movements of the body and can potentially be used to recognize when a player is bouncing the basketball (dribbling). This paper reports the findings of using the received signal strength positioning technique with one anchor node to estimate the position of a moving basketball player towards the node and the use of a wearable triaxial accelerometer sensor to identify the person bouncing the ball. Both slow and fast game pace conditions are tested. Player position accuracy can be achieved to within 0.5m for distances less than 9m. A study of the power law variation in signal strength over distance gives an exponent n of 2.23 under moderate paced conditions with a Pearson squared correlation coefficients of r2 = 0.42. This means that 42% of the variation can be explained by the approximation. Under moving conditions, changes in the transmit antenna orientation will result in an increase of random variation in position prediction which is included in this result. The acceleration signature from a wrist sensor of the hand-ball contact process while dribbling distinctively shows ball dribbling possession. A single moving player scenario is discussed and measured results with and without the basketball demonstrate the accuracy and feasibility of the positioning and ball possession techniques. The techniques will provide valuable information for further study into multiple player and multiple beacon wireless indoor positioning.

This paper presents a feedback GUI to improve the motor skills of a subject performing a golf putt. In this paper inertial sensors (gyroscopes) and video were used to capture the swing. Feedback was provided by a graphical user interface created in Matlab and displayed the video of the putt and quantitative values such as the putt tempo (ratio Backswing duration : Downswing duration) and score which gives an indication of how close the putt tempo is to the ideal rato of (2:1). A zero-crossing method was used to determine the swing phases and durations from the rotational velocity.The effectiveness of the feedback GUI was tested using 10 participants (4 experienced and 6 inexperienced). Each participant executed two sets of 15 putts over distances of 3m, 6m and 9m on an artificial turf putting surface with feedback provided by the GUI between the two sets of putts. The results indicated that overall tempo ratio of experienced and inexperienced participants became closer to 2:1 after the feedback. The standard deviation also decreased which meant that participants also improved their putting consistency. The results indicate that the participants were able to improve their skill in terms of putting performance indicators after using the feedback GUI. (C) 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University

Time lost due to injury or illness requiring rehabilitation is a major problem. Activity is an important part of rehabilitation, however compliance and adherence can be challenging. This paper addresses this issue by presenting an automatic system for monitoring activity allowing objective assessment of the activity. The system consisted of a smartphone based activity capture platform connected wirelessly to back-end server for analysis and storage and a web server to provide a user-friendly interface for feedback and education purposes. The system was validated by comparison with 3 accepted standard measuring devices and found to match their results well. The system also monitored the data of the participants over a continuous period of a number of days. It is evident that human factors play a part in both of the data collection strategies.

Sensor-based biomechanical monitoring of sporting activity requires the interpretation of large data-sets of time series data-sets. Visualization techniques are a powerful method for displaying these data in a meaningful way to assist in understanding the complex interrelationships of the data and biomechanics. In particular, repetitive actions such as seen in many sports, including swimming can benefit from such analysis where overlay and visual comparison of multiple strokes can be advantageous. Many other disciplines, such as medicine visualize repetitive data and are translational opportunities for the investigation of biomechanical data, such as swimming. This paper presents a case study in which inertial sensor time series data from an elite and sub-elite swimmer were compared using visualization techniques to highlight differences in their action and performance. In particular, the metrics of body roll velocity was captured from the gyroscope sensor and was used as the key time series data to be visualized. Visualization techniques investigated were time-series overlay, phase space portraits, ribbon plot overlay, and wavelet scalograms. The phase space portraits, ribbon plots, and wavelet scalograms demonstrated clearly self-consistency of the swimmer's action. As a cross-comparison tool, these techniques showed clear difference between the elite swimmer, who had lower variability and thus a more consistent action than the sub-elite swimmer. This paper has demonstrated that there is merit in further examination of these techniques as a tool for feedback. It was found that all the methods presented unique views of stroke biomechanics in a nontechnical yet intuitive way for clearer communication.

Indoor positioning techniques are being considered for use within team sports where players are tracked and recorded providing coaching assistance. While Global Positioning System (GPS) is the de facto standard employed to estimate the location of players, this is unsuitable for accurate indoor positioning due to roof and wall signal obstruction. Over a dozen techniques have been suggested for wireless indoor positioning of static and/or moving objects with improved methods for limiting the position estimation errors. For those indoor techniques that rely on radio frequency (RF) signal propagation, propagation statistics are useful in determining path loss and signal degradation. This static indoor propagation knowledge is critical in the design of wireless indoor positioning systems and ultimately the accuracy of the position estimation. Signal path propagation losses and variations produced from shadowing, multipath, fading, scattering or diffraction from objects can hinder the designer's efforts to provide a reliable and accurate positioning system. However, sporting indoor environments generally have an open floor area where play is conducted free from objects apart from the players themselves. At the elite sporting level these indoor play areas are designed and constructed under international specifications. Consequently, static wireless RF signal propagation knowledge obtained from one of these commonly built play areas is applicable to hundreds of other venues around the world. Many of these indoor sporting areas are used for a variety of sports, for example, basketball, volleyball, indoor cricket, indoor soccer, netball and handball. A player positioning installation could potentially be used for all of these sports. This paper reports the wireless RF signal propagation of a 2.4GHz waist mounted beacon for an indoor sporting venue ‘play area’, which has been designed to international standards. A basketball court constructed of highly polished wood was chosen as the sample ‘play area’. Propagation models are discussed and comparisons between predicted and measured results demonstrate the validity of the technique.

Modern communication systems (Web 1.0, Web 2.0, cloud computing) and mobile wireless technologies (smartphones, iPads, monitoring devices) have, as with all industries, progressed in healthcare over recent years from being a minor, to being a very significant component of the environment. This paper will discuss how advancements in information technology, wireless communication systems and sensor technology have provided new opportunities concerning practices for managing Chronic Disease (CD). This paper will also address future software, touching on Web 3.0 and how, combined with Web 2.0 and cloud computing, has the potential to produce the ultimate architecture of participation. Understanding the benefits of such systems, devices and their increasing emergence and connection with modern healthcare settings, is vital for implementing future successful e-health solutions for people with CD.

Neural co-activation in frontal and central cortex was examined during a visual oddball task using wavelet coherence. EEG was recorded during a visual oddball task, presented to 12 participants with a random mix of 15% oddball targets and 85% frequent non-target letters over 265 trials. Wavelet coherence of individual trials was shown to distinguish frequent and oddball trials. Averaged wavelet coherence showed significant differences: oddball targets showed higher delta-theta activity whereas frequent background stimuli showed higher gamma activity. Increased gamma coherence appeared to be related to expectation of the targets with our analysis showing an R(2) of 0.935 for the relationship between averaged sections of gamma coherence and the number of intervening (frequent) trials since the last oddball.

Video analysis is a very important tool that is used by players, coaches and sports scientists since it is more intuitive and familiar than other analysis methods. One of the issues with using video is searching the video data to look for specific events to examine. The common technique for searching the video data involves watching the video and hand scoring it for later use or by visually searching until the event of interest appears. There have been attempts to automatically score the data using image processing techniques but these require elaborate multicamera systems coupled with complex image processing software. Inertial sensors are cheap and readily available and have been used to great effect to analyse athlete's performance and to detect selected events in the performance. The advantage of inertial sensors is that they can be mounted on the person or sporting equipment and can continually monitor the performance without suffering the problems of lighting, angle, and occlusion which occur in video systems. This paper describes a technique to extract events using inertial sensors and using the timing of those events to index into synchronised video. The event type and timing information derived from the inertial sensor data can be stored and used as search keys for specific events in the video. This technique is demonstrated through a tennis visualisation system that uses strokes derived from a racquet mounted sensor as the index into the video.

This paper addresses the requirement of multiplier truncation on the FPGA based continuous wavelet transform (CWT) scalogram and compares it with the one produced by Matlab-software as a reference. A method was developed to give an appropriate truncation in the multiplier stage of the CWT. The Fast Fourier Transform (FFT) algorithm was used to compute the CWT at each time and scale. The VHDL language was used for design and implementation using Altium designer software targeting Spartan 3AN FPGA. The obtained results showed that hardware implementation achieved high degree of accuracy. The produced hardware scalogram in comparing with the software one has a NMSE of 0.0013, NAD of 0.0227 and SC quality measure of 0.998.

Over the last decade, inertial sensors have become a valuable tool for extracting quantitative data from athletes. Due to their small size, unobtrusive nature and relative affordability, there is considerable interest in using multi-channel multi-sensor configurations to gain further insight into sporting performance parameters. As the amount of raw information that can be recorded in a single training session increases, so too does the complexity of the data mining algorithms required to emphasise, extract and derive its performance metrics. This paper details a developed system that uses a distributed server-client architecture to collect and store large sets of athlete data as well as providing mechanisms for later analysis and visualisation for feedback. The server utilises MATLAB with the Athlete Data Processing Toolbox. A local SQL server handles data storage and PHP with AJAX/JSON is used to communicate with clients. Clients use a web browser interface to communicate with the server and provide relevant analysis and visualisation tools to the end user.

This paper examines three methods to measure the upper arm rotation, the main contributor to produce a fast first serve in tennis. Accepted videography techniques were compared with a novel inertial gyroscope system and marker-based technique. A network of two inertial sensors on the upper arm and the chest was used to measure upper arm rotation angle and remove body artifacts. A marker-based virtual gyroscope (MBVG) was derived from Vicon marker positions in the standard Plug-in-Gait model using a vector-based method of marker trajectories and a series of geometric transformations. The results indicate that there is a close temporal feature match for all three methods when applied to the tennis serve. This paper shows that gyroscopes as well as the MBVG can be advantageous for tennis serve assessment.

Tennis is a popular game played and viewed by millions of people around the world. There is a large impetus for players to improve their game and technology is becoming an important tool in doing this. This chapter discusses the current technology used in tennis and also discusses the biomechanics of the various strokes, so that the application of the technology can be better understood. Since the serve is a crucial part of a player’s game, this chapter focuses on the serve, but still discusses the other tennisstrokes. The chapter is divided into 2 parts: the biomechanics of the strokes and the technology used to monitor tennis. The technology section details some of the major tools to monitor and analyze the tennis swing, including high speed digital cameras, marker-based optical systems, and inertial sensors. Examples are provided of how these technologies can be applied. Finally, a small discussion is presented, which gives an idea of future directions in tennis monitoring.

In this paper, an accelerometer measurement system comprising three accelerometer nodes was used to identify the correlation between the skill level and the characteristics of the first serve swing in tennis. Three MEMS accelerometers were mounted on the knee, leg, and wrist of the tennis players. The kinematic model for the first serve was observed. Furthermore, this study revealed that side-forward motion of the hand along with the forward motion of the waist of an athlete can be used as indicator to assess the athlete's skill level. It is envisaged that this application can provide feedback to tennis players.