This article presents the design and experimental evaluation of a non-invasive wearable sensor system that can be used to acquire crucial information about athletes’ performance during inline figure skating training. By combining distance and time-of-flight sensors and gyroscopes, the system is able to detect when jumps are performed and provides a live view of the data (e.g., the number and height of jumps) through a graphical user interface. The main novelty of our approach lies in the way in which the optical sensors are orientated. Typically, the sensors are orientated horizontally and positioned in pairs on the ground, where they measure the time interval between the moment the athlete leaves the ground and the moment they land. In our system, an optical sensor is placed under each foot and is vertically orientated so as to constantly measure the distance from the ground. In addition, a gyroscope sensor is placed on the athlete’s back, which provides information on the direction and angular momentum of the movement. By combining this data, the system provides the accurate detection of various jumps and technical elements without any constraints on the training ground. In this paper, the system is also compared to similar platforms in the literature, although there are no other specific systems that are available for inline figure skating. The results of the experimental evaluation, which was performed by high profile athletes, confirm its effectiveness in correctly detecting jumps, especially considering its compromise between precision and the overall cost of the equipment.

LEGO Mindstorms robots are widely used as educational tools to acquire skills in programming complex systems involving the interaction of sensors and actuators, and they offer a flexible and modular workbench to design and evaluate user–machine interaction prototypes in the robotic area. However, there is still a lack of support to interoperability features and the need of high-level tools to program the interaction of a robot with other devices. In this paper, we introduce Legodroid, a new Java library enabling cross-programming LEGO Mindstorms robots through Android smartphones that exploits their combined computational and sensorial capabilities in a seamless way. The library provides a number of type-driven coding patterns for interacting with sensors and motors. In this way, the robustness of the software managing robot’s sensors dramatically improves.

We present an empirical study on the ability of Large Language Models (LLMs) to understand code by detecting semantically equivalent and inequivalent programs, that is, whether they compute the same result given the same input or not. To probe this, we deliberately perturb the program text by introducing semantics-preserving code transformations, namely copy propagation and constant folding. Using a benchmark of 11 Python functions with both equivalent and non-equivalent variants, we evaluate seven state-of-the-art LLMs (including ChatGPT, Claude, Gemini, and Deep-Seek) under zero-shot prompting, with and without minimal context. Despite strong performance in code generation tasks, the models often fail in this deeper reasoning challenge, misclassifying 41% of equivalent cases without context and 29% with context. Although prompting can improve performance, it does not address the underlying limitations of the models. We argue that improving LLMs themselves, through targeted fine-tuning, contrastive learning on equivalent and nonequivalent implementations, or training on transformation-invariant code, will be necessary for robust semantic understanding. Meanwhile, practitioners can achieve better results by selecting stronger models, carefully engineering prom-pts, or writing code with tools that normalize low-level differences before inference.