ABSTRACT This article presents a novel approach for real‐time posture recognition in monitoring scenarios, utilising a virtual camera simulated on a UAV within virtual environments. Leveraging the MediaPipe Pose library, key points of the body skeleton are extracted, focusing on a subset of 8 key points for computational efficiency. Through the integration of heuristic algorithms based on physical proportions of the human body, the proposed methodology provides accurate estimations of three distinct postures: lying, standing, and sitting. This heuristic‐based approach offers a computationally efficient alternative to traditional machine learning and deep learning methods, ensuring real‐time performance and scalability. The efficiency of the framework is demonstrated through experiments that show its potential applications in various fields, including healthcare, virtual reality, and human‐computer interaction. This approach achieved an average precision of 98.08% for virtual images. Success rates were 100%, 95.8%, and 98.9% for standing, sitting, and lying postures, respectively. Furthermore, the original classification model, which was tuned for virtual images, was tested on real images without any alteration to the parameter values. Its good performance demonstrates its potential for generalisation and application in diverse environments. Overall, this work contributes to the advancement of posture recognition technology, offering a versatile and accessible solution for posture analysis in dynamic monitoring environments.

Research in various fields has highlighted the benefits of using extended reality (XR) in many applications. However, the use of musical instruments in this context remains largely unexplored. This article presents a systematic review in which the existing literature is compiled and analyzed to identify the current state of research. A systematic search of four electronic databases of articles published in English since 2010 was conducted. After merging duplicates, a total of 2849 unique records were identified, of which 74 were included in the analysis. The review reveals a diverse landscape of XR applications, ranging from music learning to rehabilitation therapies. Diverse interaction modalities and factors affecting the musician’s experience have been identified. XR technologies offer unprecedented opportunities for musicians; however, technological limitations pose challenges to creating fully satisfying experiences. This review is intended to guide future research that supports the development of new forms of musical expression in XR.

This paper describes the mechanical devices, the movements and the trajectory generation of a novel wheelchair prototype capable of climbing staircases. The key feature of the mechanical design is the use of two decoupled mechanisms for each axle, one used to negotiate steps, and the other to position the axle with respect to the chair to accommodate the overall slope. This decoupling makes many different climbing strategies possible, the overall mechanism becoming extraordinarily versatile from a control point of view. A control system is necessary to synchronize the movements of all the actuators of the wheelchair so that its centre of mass can follow arbitrary spatial trajectories. Different trajectories have been designed in order to keep the seat as erect as possible to guarantee passenger comfort. Experimental results with the real prototype are reported which show the efficiency of our mechanism, the accuracy of the developed kinematic models for control, and the comparison of comfort for two different spatial trajectories