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

Payload-Directed Control of Geophysical Magnetic Surveys

Using non-navigational (e.g. imagers, scientific) sensor information in control loops is a difficult problem to which no general solution exists. Whether the task can be successfully achieved in a particular case depends highly on problem specifics, such as application domain and sensors of interest. In this study, we investigate the feasibility of using magnetometer data for control feedback in the context of geophysical magnetic surveys. An experimental system was created and deployed to (a) assess sensor integration with autonomous vehicles, (b) investigate how magnetometer data can be used for feedback control, and (c) evaluate the feasibility of using such a system for geophysical magnetic surveys. Finally, we report the results of our experiments and show that payload-directed control of geophysical magnetic surveys is indeed feasible

Ball Oscillating Bouncer

The purpose of this report is to document the need, objectives, marketing and engineering requirements, as well as validate the design of an autonomous control device capable of continuously bouncing a table tennis ball on a paddle. This includes the design of a self correcting system using lightweight materials, and as few sensors and components as possible to achieve a compact, portable design. To accomplish this, the system is designed to react to a ball falling from as short a distance as 10 centimeters above the paddle, meaning all sensor processing, control processing, and motor drives should be able to react within an appropriate timeframe. The overall system is broken down into four main parts: a sensor and sensor processing system, a controller and control processing system, an electromechanical motor system, and a DC power supply system. The sensor system shall be capable of detecting and analyzing ball trajectory and forward that information to the control processor. The control processor will generate a response to react to the ball trajectory and forward that information to the motor drives which will physically act to correct the ball position, thus completing the control sequence. ● System shall behave completely autonomously ● Four main subsystems: Sensor/Processor, Controller/Processor, Motors and Drives, and DC Power Supply. ● System will need to react quickly, sometimes in as short a time as 142 milliseconds

Artificial Intelligence Of Things For Ubiquitous Sports Analytics

To enable mobile devices to perform in-the-wild sports analytics, particularly swing tracking, remains an open question. A crucial challenge is to develop robust methods that can operate across various sports (e.g., golf and tennis), different sensors (cameras and IMU), and diverse human users. Traditional approaches typically rely on vision-based or IMU-based methods to extract key points from subjects in order to estimate trajectory predictions. However, these methods struggle to generate accurate swing tracking, as vision-based techniques are susceptible to occlusion, and IMU sensors are notorious for accumulated errors. In this thesis, we propose several innovative solutions by leveraging AIoT, including the IoT with ubiquitous wearable devices such as smartphones and smart wristbands, and harnessing the power of AI such as deep neural networks, to achieve ubiquitous sports analytics. We make three main technical contributions: a tailored deep neural network design, network model automatic search, and model domain adaptation to address the problem of heterogeneity among devices, human subjects, and sports for ubiquitous sports analytics. In Chapter 2, we begin with the design of a prototype that combines IMU and depth sensor fusion, along with a tailored deep neural network, to address the occlusion problems faced by depth sensors during swings. To recover swing trajectories with fine-grained details, we propose a CNN-LSTM architecture that learns multi-modalities within depth and IMU sensor fusion. In Chapter 3, we develop a framework to reduce the overhead of model design for new devices, sports, and human users. By designing a regression-based stochastic NAS method, we improve swing-tracking algorithms through automatic model generation. We also extend our studies to include unseen human users, sensor devices, and sports. Leveraging a domain adaptation method, we propose a framework that eliminates the need for tedious training data collection and labeling for new users, devices, and sports via adversarial learning. In Chapter 4, we present a framework to alleviate the model parameter selection process in NAS, as introduced in Chapter 3. By employing zero-cost proxies, we search for the optimal swing tracking architecture without training, in a significantly larger candidate model pool. We demonstrate that the proposed method outperforms state-of-the-art approaches in swing tracking, as well as in adapting to different subjects, sports, and devices. Overall, this thesis develops a series of innovative machine learning algorithms to enable ubiquitous IoT wearable devices to perform accurate swing analytics (e.g., tracking, analysis, and assessment) in real-world conditions

Laser based tracking and spin measurement

The sports ball market is extremely competitive and in the US alone valued in excess of $1305 million (SGMA 2008). Original equipment manufacturers (OEMs) are continually trying to create a competitive edge over their rivals. In order to research and develop sports balls it is vital to quantitatively measure launch and flight characteristics of the ball, in an attempt to create a ball that has better flight and/or impact characteristics. A launch or flight monitor allows consistent measurement and benchmarking of the ball under test. Current top of the range soccer ball monitors are assessed for performance. Predominantly the sports engineering community uses high speed video (HSV) cameras in this benchmarking process. This technique however is extremely susceptible to errors in spin measurement. These errors are explored in detail and recommendations are given in order to improve the measurements. The properties of laser light make it an ideal tool for accurate, non-contact measurements. It has gained such widespread use, that living in the 21" century it is inconceivable to avoid laser technology. In this thesis, optical laser techniques are pursued for ball launch angle, velocity and spin measurement. In order to successfully utilise these techniques a system that is capable of accurately steering the laser beam to the desired target is developed. A novel laser tracking system (NLTS) has been designed, developed and proven to work successfully, allowing tracking capability of an arbitrarily moving soccer ball, that has no special fiducials. The system is demonstrated to be capable of measuring the position of the ball in space, therefore the NLTS is capable of acting as a launch monitor. The system is proven to track soccer balls in the laboratory and in a more realistic player testing environment. A valuable design feature is that the natural and ambient lighting conditions are inconsequential for the operation of the system. The tracking technique could be applied to any sports ball and could conceivably be transferred to other applications, e.g. military and automotive. Single point vibrometry work and the NLTS are combined to add spin measurement capability. Actual and measured spin rate values show high levels of similarity when tracking a ball with angular, but no translational velocity. A purpose built 'pendulum rig' is used to carry out measurements on a ball with both translational and angular velocity. The testing highlights how influential the radial measurement distance from the spin axis is, regarding the outputted spin rate value. The current set-up would require further development to allow accurate spin rate measurement using the 'pendulum rig'. The main sources of error and recommendations for future developments of this device are outlined and discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

Kinematics and Robot Design IV, KaRD2021

This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects