Ubiquitous (pervasive) computing is a term for a synergetic use of sensing, communication and computing. Pervasive use of computing has seen a rapid increase in the current decade. This development has propagated in applied sport science and everyday life. The work presents a survey of recent developments in sport and leisure with emphasis on technology and computational techniques. A detailed analysis on new technological developments is performed. Sensors for position and motion detection, and such for equipment and physiological monitoring are discussed. Aspects of novel trends in communication technologies and data processing are outlined. Computational advancements have started a new trend – development of smart and intelligent systems for a wide range of applications – from model-based posture recognition to context awareness algorithms for nutrition monitoring. Examples particular to coaching and training are discussed. Selected tools for monitoring rules' compliance and automatic decision-making are outlined. Finally, applications in leisure and entertainment are presented, from systems supporting physical activity to systems providing motivation. It is concluded that the emphasis in future will shift from technologies to intelligent systems that allow for enhanced social interaction as efforts need to be made to improve user-friendliness and standardisation of measurement and transmission protocols.

Position data of players and athletes are widely used in sports performance analysis for measuring the amounts of physical activities as well as for tactical assessments in game sports. However, positioning sensing systems are applied in sports as tools to gain objective information of sports behavior rather than as components of intelligent spaces (IS). The paper outlines the idea of IS for the sports context with special focus to game sports and how intelligent sports feedback systems can benefit from IS. Henceforth, the most common location sensing techniques used in sports and their practical application are reviewed, as location is among the most important enabling techniques for IS. Furthermore, the article exemplifies the idea of IS in sports on two applications.

A comparison of methods for analyzing drop jump performance. Med. Sci. Sports Exerc., Vol. 31, No. 3, pp. 437-442, 1999. Drop jumping is a popular form of plyometric training. Different techniques are applied to determine parameter values quantifying drop jumps, such as the jump height or the durations of the phases of downward and upward movements of the center of mass (CM) during foot contact with the ground after dropping. The flight-time method estimates the jump height from the time between the instant of leaving the ground and the instant of landing. In video-based methods, markers are placed on the skin of the subject to define the positions of the body segments. The time-dependent positions of the CM and parameter values are then calculated utilizing models of the human body. If the vertical velocity of the CM can be estimated at one instant, the parameter values can be calculated from the vertical ground reaction forces. The purpose of this study was to find out which technique yields the lowest errors compared with the results obtained by the double force plate technique. In this investigation, two force plates were used, one located under the drop platform. Twenty-five drop jumps were analyzed with eight different methods. There were large differences between the reference method and other methods. Using the height of the drop platform (0.39 m) to estimate the velocity at the end of the free fall, in conjunction with data from one force plate, resulted in a mean difference of 4.2% (SD: 9.6%) in the calculated jump height. Using video information to estimate the time that the velocity of the CM fell to zero after the drop phase, in conjunction with data from one force plate, resulted in differences in the jump height of up to 17%. Differences between the reference method and video based methods were comparatively small (mean value of differences in jump height: −0.007 m, SD: 0.013 m for the best of these methods) but not negligible. Nevertheless, video based methods turned out to be the most promising alternative to the reference method to determine accurate variables concerning drop jump performance.

This article proposes a comprehensive investigation of the automatic classification of functional gait disorders based solely on ground reaction force (GRF) measurements. The aim of the study is twofold: (1) to investigate the suitability of stateof-the-art GRF parameterization techniques (representations) for the discrimination of functional gait disorders; and (2) to provide a first performance baseline for the automated classification of functional gait disorders for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with gait disorders (GDs) and data from 161 healthy controls (N). Patients were manually classified into four classes with different functional impairments associated with the “hip”, “knee”, “ankle”, and “calcaneus”. Different parameterizations are investigated: GRF parameters, global principal component analysis (PCA)-based representations and a combined representation applying PCA on GRF parameters. The discriminative power of each parameterization for different classes is investigated by linear discriminant analysis (LDA). Based on this analysis, two classification experiments are pursued: (1) distinction between healthy and impaired gait (N vs. GD) and (2) multi-class classification between healthy gait and all four GD classes. Experiments show promising results and reveal among others that several factors, such as imbalanced class cardinalities and varying numbers of measurement sessions per patient have a strong impact on the classification accuracy and therefore need to be taken into account. The results represent a promising first step towards the automated classification of gait disorders and a first performance baseline for future developments in this direction.

A prototype system for monitoring, transmitting and processing performance data in sports for the purpose of providing feedback has been developed. During training, athletes are equipped with a mobile device and wireless sensors using the ANT protocol in order to acquire biomechanical, physiological and other sports specific parameters. The measured data is buffered locally and forwarded via the Internet to a server. The server provides experts (coaches, biomechanists, sports medicine specialists etc.) with remote data access, analysis and (partly automated) feedback routines. In this way, experts are able to analyze the athlete’s performance and return individual feedback messages from remote locations.