Combining inertial and visual sensing for human action recognition in tennis

In this paper, we present a framework for both the automatic extraction of the temporal location of tennis strokes within a match and the subsequent classification of these as being either a serve, forehand or backhand. We employ the use of low-cost visual sensing and low-cost inertial sensing to achieve these aims, whereby a single modality can be used or a fusion of both classification strategies can be adopted if both modalities are available within a given capture scenario. This flexibility allows the framework to be applicable to a variety of user scenarios and hardware infrastructures. Our proposed approach is quantitatively evaluated using data captured from elite tennis players. Results point to the extremely accurate performance of the proposed approach irrespective of input modality configuration

A virtual coaching environment for improving golf swing technique

As a proficient golf swing is a key element of success in golf, many golfers make significant effort improving their stroke mechanics. In order to help enhance golfing performance, it is important to identify the performance determining factors within the full golf swing. In addition, explicit instructions on specific features in stroke technique requiring alterations must be imparted to the player in an unambiguous and intuitive manner. However, these two objectives are difficult to achieve due to the subjective nature of traditional coaching techniques and the predominantly implicit knowledge players have of their movements. In this work, we have developed a set of visualisation and analysis tools for use in a virtual golf coaching environment. In this virtual coaching studio, the analysis tools allow for specific areas require improvement in a player's 3D stroke dynamics to be isolated. An interactive 3D virtual coaching environment then allows detailed and unambiguous coaching information to be visually imparted back to the player via the use of two virtual human avatars; the first mimics the movements performed by the player; the second takes the role of a virtual coach, performing ideal stroke movement dynamics. The potential of the coaching tool is highlighted in its use by sports science researchers in the evaluation of competing approaches for calculating the X-Factor, a significant performance determining factor for hitting distance in a golf swing

Two stream network for stroke detection in table tennis

This paper presents a table tennis stroke detection method from videos. Themethod relies on a two-stream Convolutional Neural Network processing inparallel the RGB Stream and its computed optical flow. The method has beendeveloped as part of the MediaEval 2021 benchmark for the Sport task. Ourcontribution did not outperform the provided baseline on the test set but hasperformed the best among the other participants with regard to the mAP metric.<br

Automatic camera selection for activity monitoring in a multi-camera system for tennis

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 describe and evaluate a system for automatic camera selection from a network of synchronised cameras within a tennis sporting arena. This work combines synchronised video streams from multiple cameras into a single summary video suitable for critical review by both tennis players and coaches. Using an overhead camera view, our system automatically determines the 2D tennis-court calibration resulting in a mapping that relates a player's position in the overhead camera to their position and size in another camera view in the network. This allows the system to determine the appearance of a player in each of the other cameras and thereby choose the best view for each player via a novel technique. The video summaries are evaluated in end-user studies and shown to provide an efficient means of multi-stream visualisation for tennis player activity monitoring

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

The relationship between habitual dietary protein intake and dual task performance in sedentary, recreationally active, and masters athlete older adults

As the body ages, physical and cognitive declines can result in balance and mobility deficits, but research has shown that proper nutrition and exercise can help maintain physical and mental capacity. The purpose of this study was to analyze the relationship between habitual dietary protein intake and dual task performance in sedentary (SED), recreationally active (RA), and masters athletes (MA). To measure physical activity levels, the Rapid Physical Activity Questionnaire (RAPA) was completed by all participants. The participants were placed into a high or low protein group using the ASA-24 hour dietary recall. If the participant consumed less than 0.8 g/kg of protein per day, they were placed in the low protein group; if the participant consumed more than 0.8 g/kg of protein per day, they were placed in the high protein group. Participants completed four different walking tasks: habitual speed, maximal speed, dual-task habitual speed, and dual-task maximal speed. Gait speed was measured over a distance of 10 meters. SED, RA, and MA consumed a mean of 0.84, 1.13, and 1.57 grams of protein per kilogram body weight per day, respectively. MA consumed significantly more protein than SED or RA participants (α \u3c .05). The low protein group consumed 0.84 g/kg of protein ± 0.39 while the high protein group consumed 1.30 g/kg of protein ± 0.50. There was no significant correlation between amount of protein consumed and dual task performance. While the results were for dual task performance not statistically significant, they may have clinical significance; when comparing the high and low protein groups for the dual task habitual trial, the high protein group covered the 10-m distance 0.73 seconds faster than the low protein group. Clinically, the higher protein group may be able to perform activities of daily living more efficiently

Supervised Learning for Table Tennis Match Prediction

Machine learning, classification and prediction models have applications across a range of fields. Sport analytics is an increasingly popular application, but most existing work is focused on automated refereeing in mainstream sports and injury prevention. Research on other sports, such as table tennis, has only recently started gaining more traction. This paper proposes the use of machine learning to predict the outcome of table tennis single matches. We use player and match statistics as features and evaluate their relative importance in an ablation study. In terms of models, a number of popular models were explored. We found that 5-fold cross-validation and hyperparameter tuning was crucial to improve model performance. We investigated different feature aggregation strategies in our ablation study to demonstrate the robustness of the models. Different models performed comparably, with the accuracy of the results (61-70%) matching state-of-the-art models in comparable sports, such as tennis. The results can serve as a baseline for future table tennis prediction models, and can feed back to prediction research in similar ball sports.Comment: 9 pages, 8 figure