Several research programs are tackling the use of Wireless Sensor Networks (WSN) at specific fields, such as e-Health, e-Inclusion or e-Sport. This is the case of the project “Ambient Intelligence Systems Support for Athletes with Specific Profiles”, which intends to assist athletes in their training. In this paper, the main developments and outcomes from this project are described. The architecture of the system comprises a WSN deployed in the training area which provides communication with athletes’ mobile equipments, performs location tasks, and harvests environmental data (wind speed, temperature, etc.). Athletes are equipped with a monitoring unit which obtains data from their training (pulse, speed, etc.). Besides, a decision engine combines these real-time data together with static information about the training field, and from the athlete, to direct athletes’ training to fulfill some specific goal. A prototype is presented in this work for a cross country running scenario, where the objective is to maintain the heart rate (HR) of the runner in a target range. For each track, the environmental conditions (temperature of the next track), the current athlete condition (HR), and the intrinsic difficulty of the track (slopes) influence the performance of the athlete. The decision engine, implemented by means of (m; s)-splines interpolation, estimates the future HR and selects the best track in each fork of the circuit. This method achieves a success ratio in the order of 80%. Indeed, results demonstrate that if environmental information is not take into account to derive training orders, the success ratio is reduced notably.

Human activity recognition (HAR) for boxing training typically requires specialized hardware or large labeled datasets to identify different types of punches and analyze their basic properties. However, measuring the unpredictability of boxers, a key characteristic to deceive opponents during combats, has remained an unexplored challenge. We tackled this challenge by combining a series of novel techniques and ideas. Unpredictability was computed as the entropy of the trajectories of the Markov chain characterizing the training session, estimated from the combinations (sequences of punches) thrown by the boxer to the training bag. Punch combinations were obtained by detecting punches as outliers in the raw acceleration sensor datastream coming from the bag, and separating them by type with an ensemble of PCA $+$ GMM clusters. Punches were then divided into combinations using statistical analysis of separation intervals. The detection procedure works even with noisy data, in contrast to force-threshold methods commonly reported in the literature. As distinctive features of our system, it uses only unlabeled data from the current training session, and sensor positions and orientations inside the bag are unconstrained. However, punch clustering achieves up to 94% accuracy, comparable to state-of-the-art supervised approaches. These features are currently unseen in the literature and demonstrate the capacity for unsupervised learning techniques to address challenging problems in sports. Multiple validation experiments were consistent with boxers’ level and session performance. Overall, our approach provides significant contributions to the field of HAR and has the potential to improve the way boxing and other sports are trained and evaluated.

In this paper, we propose a solution for gunshot location in national parks. In Spain there are agencies such as SEPRONA that fight against poaching with considerable success. The DiANa project, which is endorsed by Cabañeros National Park and the SEPRONA service, proposes a system to automatically detect and locate gunshots. This work presents its technical aspects related to network design and planning. The system consists of a network of acoustic sensors that locate gunshots by hyperbolic multi-lateration estimation. The differences in sound time arrivals allow the computation of a low error estimator of gunshot location. The accuracy of this method depends on tight sensor clock synchronization, which an ad-hoc time synchronization protocol provides. On the other hand, since the areas under surveillance are wide, and electric power is scarce, it is necessary to maximize detection coverage and minimize system cost at the same time. Therefore, sensor network planning has two targets, i.e., coverage and cost. We model planning as an unconstrained problem with two objective functions. We determine a set of candidate solutions of interest by combining a derivative-free descent method we have recently proposed with a Pareto front approach. The results are clearly superior to random seeding in a realistic simulation scenario.

Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (³He), a potential enabling element for fusion reactors, on the Moon. ³He is abundant on the Moon’s surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACO_R) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases.

Cable-driven parallel robots (CDPRs) are increasingly used for load manipulation tasks involving predefined toolpaths with intermediate stops. At each stop, where the platform maintains a fixed pose, and the motors keep the cables under tension, the system must evaluate whether it is safe to proceed by detecting anomalies that could compromise performance (e.g., wind gusts or cable impacts). This paper investigates whether anomalies can be detected using only motor torque data, without additional sensors. It introduces an adaptive unsupervised outlier detection algorithm based on Gaussian Mixture Models (GMMs) to identify anomalies from torque signals. The method starts with a brief calibration period—just a few seconds—during which a GMM is fit on known anomaly-free data. Real-time torque measurements are then evaluated using the Mahalanobis distance from the GMM, with statistically derived thresholds triggering anomaly flags. Model parameters are periodically updated using the latest segments identified as anomaly-free to adapt to changing conditions. Validation includes 14 long-duration test sessions simulating varied wind intensities. The proposed method achieves a 100% true positive rate and 95.4% average true negative rate, with 1-second detection latency. Comparative evaluation against power threshold and non-adaptive GMM methods indicates higher robustness to drift and environmental variation.

This paper presents a novel system for effort control in team-sport training, where each component is associated with a particularized program. The system relies on a decision engine that analyzes training data and provides instructions to athletes, and on a sensing infrastructure which monitors athletes' biometrics and movements and gathers environmental data such as humidity or temperature. The prototype has been developed using IMOTE2 and MicaZ sensors under TinyOS and BioHarness biometric belts. The decision technique estimates the ratios of each effort regime, based on best-fit polynomial approximations using historical data from previous training sessions. The complete system results in a virtual coach, operating in real time and automatically. This methodology has been piloted in an experiment with the UCAM Volleyball Murcia team, top of the Spanish national women's volleyball league. Data obtained during training sessions have provided a knowledge base for the algorithms developed and allowed for validation of the results.