Validity and reliability of NOTCH® inertial sensors for measuring elbow joint angle during tennis forehand at different sampling frequencies

Portable and low-cost motion capture systems are gaining importance for biomechanical analysis. The aim was to determine the concurrent validity and reliability of the NOTCH® inertial sensors to measure the elbow angle during tennis forehand at different sampling frequencies (100, 250 and 500 Hz), using an optical capture system with sub-millimetre accuracy as a reference. 15 competitive players performed forehands wearing NOTCH and an upper body marker-set and the signals from both systems were adjusted and synchronized. The error magnitude was tolerable (5-10◦) for all joint-axis and sampling frequencies, increasing significantly at 100 Hz for the flexion–extension and pronation-supination angles (p = 0.002 and 0.023; Cohen d > 0.8). Concordance correlation coefficient was very large (0.7–0.9) in all cases. The within-subject error variation between the test–retest did not show significant differences (p > 0.05). NOTCH® is a valid, reliable and portable alternative to measure elbow angles during tennis forehand

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

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

Comparative analysis between subjective and instrumental quality assessment through advanced technology: a pilot study on tennis serve

The purpose of this study in the first instance is to evaluate objectively, with data provided by latest-generation inertial sensors, the dynamic qualities of the technical-sporting gestures such as serve in tennis. Furthermore, the possible correlation between the aforementioned data and the evaluation monitoring of the specialized technical staff (Italian Tennis Federation qualified Coach) was assessed, in essence, a comparison between objective instrumental data and quality technical analysis. The study is not based on probative statistical numbers, five athletes, but the interest of the research is focused on establishing the validity, reliability and reproducibility of the information deriving from the acquisition with inertial instrumentation in the sport of tennis. The work seemed useful also by virtue of the fact that in the literature not many works have been produced on the subject at the moment, and in any case not with the latest technologies as in our case (K-Track, K-Sport Universal, Stats Perform, Montecchio PU, Italy). As mentioned, the research took into consideration the technical fundamental of the serve, an element that has taken on more and more importance in modern tennis in the achievement of the point and therefore in the result of the game. The serve is the stroke that marks the beginning of each point and that can influence the continuance of the same. Moreover, due to the speed of the surfaces of the fields and the game, the serve became in effect a substantial percentage of the final victory of the match. This is the basic motivation that led us to analyse this fundamental and its biomechanical composition, however, highlighting those elements that best qualify the gesture in a performative sense, trying to establish parameters that can be considered helpful for the technical staff and for the tennis player,in order to improve their performance. Keywords: IMU, tennis serve, comparative analysis, technical evaluation, professional evaluation, physical dat

Motion Sensors-Based Human Behavior Recognition And Analysis

Human behavior recognition and analysis have been considered as a core technology that can facilitate a variety of applications. However, accurate detection and recognition of human behavior is still a big challenge that attracts a lot of research efforts. Among all the research works, motion sensors-based human behavior recognition is promising as it is low cost, low power, and easy to carry. In this dissertation, we use motion sensors to study human behaviors. First, we present Ultigesture (UG) wristband, a hardware platform for detecting and analyzing human behavior. The hardware platform integrates an accelerometer, gyroscope, and compass sensor, providing a combination of (1) fully open Application Programming Interface (API) for various application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. Second, we study the hand gesture recognition problem when a user performs gestures continuously. we propose a novel continuous gesture recognition algorithm. It accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Then, we apply the Hidden Markov Model to classify each segment into one of predefined hand gestures. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously. Third, we study the hand gesture recognition problem when a user is moving. We propose a novel mobility-aware hand gesture segmentation algorithm to detect and segment hand gestures. We also propose a Convolutional Neural Network to classify hand gestures with mobility noises. For the leave-one-subject-out cross-validation test, experiments with human subjects show that the proposed segmentation algorithm achieves 94.0% precision, and 91.2% recall when the user is moving. The proposed hand gesture classification algorithm is 16.1%, 15.3%, and 14.4% more accurate than state-of-the-art work when the user is standing, walking, and jogging, respectively. Finally, we present a tennis ball speed estimation system, TennisEye, which uses a racket-mounted motion sensor to estimate ball speed. We divide the tennis shots into three categories: serve, groundstroke, and volley. For a serve, we propose a regression model to estimate the ball speed. In addition, we propose a physical model and a regression model for both groundstroke and volley shots. Under the leave-one-subject-out cross-validation test, evaluation results show that TennisEye is 10.8% more accurate than the state-of-the-art work

Analysis of the backpack loading efects on the human gait

Gait is a simple activity of daily life and one of the main abilities of the human being. Often during leisure, labour and sports activities, loads are carried over (e.g. backpack) during gait. These circumstantial loads can generate instability and increase biomechanicalstress over the human tissues and systems, especially on the locomotor, balance and postural regulation systems. According to Wearing (2006), subjects that carry a transitory or intermittent load will be able to find relatively efficient solutions to compensate its effects.info:eu-repo/semantics/publishedVersio