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

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

Whole-Body Motion Capture and Beyond: From Model-Based Inference to Learning-Based Regression

Herkömmliche markerlose Motion Capture (MoCap)-Methoden sind zwar effektiv und erfolgreich, haben aber mehrere Einschränkungen: 1) Sie setzen ein charakterspezifi-sches Körpermodell voraus und erlauben daher keine vollautomatische Pipeline und keine Verallgemeinerung über verschiedene Korperformen; 2) es werden keine Objekte verfolgt, mit denen Menschen interagieren, während in der Realität die Interaktion zwischen Menschen und Objekten allgegenwärtig ist; 3) sie sind in hohem Maße von ausgeklügelten Optimierungen abhängig, die eine gute Initialisierung und starke Prioritäten erfordern. Dieser Prozess kann sehr zeitaufwändig sein. In dieser Arbeit befassen wir uns mit allen oben genannten Problemen. Zunächst schlagen wir eine vollautomatische Methode zur genauen 3D-Rekonstruktion des menschlichen Körpers aus RGB-Videos mit mehreren Ansichten vor. Wir verarbeiten alle RGB-Videos vor, um 2D-Keypoints und Silhouetten zu erhalten. Dann passen wir modell in zwei aufeinander folgenden Schritten an die 2D-Messungen an. In der ersten Phase werden die Formparameter und die Posenparameter der SMPL nacheinander und bildweise geschtäzt. In der zweiten Phase wird eine Reihe von Einzelbildern gemeinsam mit der zusätzlichen DCT-Priorisierung (Discrete Cosine Transformation) verfeinert. Unsere Methode kann verschiedene Körperformen und schwierige Posen ohne menschliches Zutun verarbeiten. Dann erweitern wir das MoCap-System, um die Verfolgung von starren Objekten zu unterstutzen, mit denen die Testpersonen interagieren. Unser System besteht aus 6 RGB-D Azure-Kameras. Zunächst werden alle RGB-D Videos vorverarbeitet, indem Menschen und Objekte segmentiert und 2D-Körpergelenke erkannt werden. Das SMPL-X Modell wird hier eingesetzt, um die Handhaltung besser zu erfassen. Das SMPL-XModell wird in 2D-Keypoints und akkumulierte Punktwolken eingepasst. Wir zeigen, dass die Körperhaltung wichtige Informationen für eine bessere Objektverfolgung liefert. Anschließend werden die Körper- und Objektposen gemeinsam mit Kontakt- und Durch-dringungsbeschrankungen optimiert. Mit diesem Ansatz haben wir den ersten Mensch-Objekt-Interaktionsdatensatz mit natürlichen RGB-Bildern und angemessenen Körper und Objektbewegungsinformationen erfasst. Schließlich präsentieren wir das erste praktische, leichtgewichtige MoCap-System, das nur 6 Inertialmesseinheiten (IMUs) benötigt. Unser Ansatz basiert auf bi-direktionalen rekurrenten neuronalen Netzen (Bi-RNN). Das Netzwerk soll die zeitliche Abhängigkeit besser ausnutzen, indem es vergangene und zukünftige Teilmessungen der IMUs zu- sammenfasst. Um das Problem der Datenknappheit zu lösen, erstellen wir synthetische Daten aus archivierten MoCap-Daten. Insgesamt läuft unser System 10 Mal schneller als die Optimierungsmethode und ist numerisch genauer. Wir zeigen auch, dass es möglich ist, die Aktivität der Testperson abzuschätzen, indem nur die IMU Messung der Smart-watch, die die Testperson trägt, betrachtet wird. Zusammenfassend lässt sich sagen, dass wir die markerlose MoCap-Methode weiter-entwickelt haben, indem wir das erste automatische und dennoch genaue System beisteuerten, die MoCap-Methoden zur Unterstützung der Verfolgung starrer Objekte erweiterten und einen praktischen und leichtgewichtigen Algorithmus mit 6 IMUs vorschlugen. Wir glauben, dass unsere Arbeit die markerlose MoCap billiger und praktikabler macht und somit den Endnutzern fur den taglichen Gebrauch näher bringt.Though effective and successful, traditional marker-less Motion Capture (MoCap) methods suffer from several limitations: 1) they presume a character-specific body model, thus they do not permit a fully automatic pipeline and generalization over diverse body shapes; 2) no objects humans interact with are tracked, while in reality interaction between humans and objects is ubiquitous; 3) they heavily rely on a sophisticated optimization process, which needs a good initialization and strong priors. This process can be slow. We address all the aforementioned issues in this thesis, as described below. Firstly we propose a fully automatic method to accurately reconstruct a 3D human body from multi-view RGB videos, the typical setup for MoCap systems. We pre-process all RGB videos to obtain 2D keypoints and silhouettes. Then we fit the SMPL body model into the 2D measurements in two successive stages. In the first stage, the shape and pose parameters of SMPL are estimated frame-wise sequentially. In the second stage, a batch of frames are refined jointly with an extra DCT prior. Our method can naturally handle different body shapes and challenging poses without human intervention. Then we extend this system to support tracking of rigid objects the subjects interact with. Our setup consists of 6 Azure Kinect cameras. Firstly we pre-process all the videos by segmenting humans and objects and detecting 2D body joints. We adopt the SMPL-X model here to capture body and hand pose. The model is fitted to 2D keypoints and point clouds. Then the body poses and object poses are jointly updated with contact and interpenetration constraints. With this approach, we capture a novel human-object interaction dataset with natural RGB images and plausible body and object motion information. Lastly, we present the first practical and lightweight MoCap system that needs only 6 IMUs. Our approach is based on Bi-directional RNNs. The network can make use of temporal information by jointly reasoning about past and future IMU measurements. To handle the data scarcity issue, we create synthetic data from archival MoCap data. Overall, our system runs ten times faster than traditional optimization-based methods, and is numerically more accurate. We also show it is feasible to estimate which activity the subject is doing by only observing the IMU measurement from a smartwatch worn by the subject. This not only can be useful for a high-level semantic understanding of the human behavior, but also alarms the public of potential privacy concerns. In summary, we advance marker-less MoCap by contributing the first automatic yet accurate system, extending the MoCap methods to support rigid object tracking, and proposing a practical and lightweight algorithm via 6 IMUs. We believe our work makes marker-less and IMUs-based MoCap cheaper and more practical, thus closer to end-users for daily usage

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

Epidemiology of Injury in English Women's Super league Football: A Cohort Study

INTRODUCTION: The epidemiology of injury in male professional football has been well documented (Ekstrand, Hägglund, & Waldén, 2011) and used as a basis to understand injury trends for a number of years. The prevalence and incidence of injuries occurring in womens super league football is unknown. The aim of this study is to estimate the prevalence and incidence of injury in an English Super League Women’s Football squad. METHODS: Following ethical approval from Leeds Beckett University, players (n = 25) signed to a Women’s Super League Football club provided written informed consent to complete a self-administered injury survey. Measures of exposure, injury and performance over a 12-month period was gathered. Participants were classified as injured if they reported a football injury that required medical attention or withdrawal from participation for one day or more. Injuries were categorised as either traumatic or overuse and whether the injury was a new injury and/or re-injury of the same anatomical site RESULTS: 43 injuries, including re-injury were reported by the 25 participants providing a clinical incidence of 1.72 injuries per player. Total incidence of injury was 10.8/1000 h (95% CI: 7.5 to 14.03). Participants were at higher risk of injury during a match compared with training (32.4 (95% CI: 15.6 to 48.4) vs 8.0 (95% CI: 5.0 to 10.85)/1000 hours, p 28 days) of which there were three non-contact anterior cruciate ligament (ACL) injuries. The epidemiological incidence proportion was 0.80 (95% CI: 0.64 to 0.95) and the average probability that any player on this team will sustain at least one injury was 80.0% (95% CI: 64.3% to 95.6%) CONCLUSION: This is the first report capturing exposure and injury incidence by anatomical site from a cohort of English players and is comparable to that found in Europe (6.3/1000 h (95% CI 5.4 to 7.36) Larruskain et al 2017). The number of ACL injuries highlights a potential injury burden for a squad of this size. Multi-site prospective investigations into the incidence and prevalence of injury in women’s football are require