Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neuromuscular coordination, strength, and proper execution to succeed in a competition. However, to investigate and analyze a sports movement, it is necessary to understand its nature and goal and to identify the factors that affect its performance. The present work aims to define the best model to screen élite and novice fencers to develop further a tool to support athletes’ and trainers’ activity. We conducted a cross-sectional study in a fencing club to collect anthropometric and biomechanical data from élite and novice fencers. Wearable sensors were used to collect biomechanical data, including a wireless inertial system and four surface electromyographic (sEMG) probes. Four different ML algorithms were trained for each dataset, and the most accurate was further trained with hyperparameter tuning. The best Machine Learning algorithm was Multilayer Perceptron (MLP), which had $96.0$% accuracy and 90% precision, recall, and F1-score when predicting class novice (0); and 93% precision, recall, and F1-score when predicting class élite (1). Interestingly, the MLP model has a slightly higher capacity to recognize élite fencers than novices; this is important to determine which training planning and execution are the best to achieve good performances.

Sports biomechanics enables thorough examination of athletic movements to enhance athletic performance and/or reduce injury risk. Few studies have looked at the possibilities of cutting-edge technology in fencing, even though it presents an intriguing scenario for sports biomechanics due to the significant demands it places on the body in terms of neuromuscular coordination, strength, power, and musculoskeletal system impact. The aim of the study is to identify and summarise current evidence on the application of motion technologies in support of fence athletes and to provide a framework for the assessment and training of fencers, including performance measures and protocols. Peer-reviewed research was identified from electronic databases using a structured keyword search. Details regarding experimental design, study group characteristics, and measured outcomes were extracted from retrieved studies, summarised, and information regrouped under themes for analysis. The methodological quality of the evidence was evaluated. Thirty-five studies were included in the present review, which showed kinetic, kinematic, muscle recruitment and coordination differences among athletes as gender and athletic training differed. Findings revealed that most of the included studies investigated the lunge technique in professional athletes using Optoelectronic Systems and force platforms as preferred motion technologies. Only nine studies reported the assessment of muscle activation during task execution ($25.7$%). Less than 20% of the study recurred to Artificial Intelligence/Machine Learning (AI/ML) approaches in the analysis. The potential contribution of the user’s kinematic/kinetic data and physiological measures is still underestimated. The recommendations provided in this study could help promote and support further cross-sectional and longitudinal studies in the field.

The anticipated rise in the adoption of autonomous vehicles (AVs) has highlighted the need to redesign vehicle control architectures. This involves compromises guaranteeing performance and safety while addressing passenger comfort, which becomes increasingly critical as passengers shift from active drivers to passive occupants, making them vulnerable to motion sickness (MS). Despite the significant number of research studies examining MS mechanisms, quantification methods, and mitigation strategies, current control approaches seldom incorporate passenger states into the vehicle control loop. Nevertheless, a review of the literature reveals that existing definitions of a “holistic controller” are either vague or fragmented. Acknowledging the necessity to harmonize diverse methodologies and delineate a consolidated definition of a holistic framework, this review initiates with a thorough exposition of vehicle-related state estimation and control methodologies, accentuating the proposed literature solutions on holistic approaches. A critical distinction emerges between traditional integrated control, relying on separate, loosely-coupled modules with limited inter-module communication and vehicle-centric optimization, and the proposed holistic control featuring a multi-level architecture with bidirectional information flow, adaptive parameter weighting, and simultaneous consideration of vehicle and passenger subsystems to achieve multi-objective optimization encompassing safety, comfort, and overall efficiency. Additionally, a comprehensive discourse on the necessary additional module regarding the passenger state estimation techniques is presented, with a particular emphasis on those targeting head motion, which is closely associated with the onset of MS, with the following discussion focused on the sensing strategies employed in relation to the underlying vehicle estimation frameworks. In light of the aforementioned insights, the paper proposes the concept of a holistic controller, defined as a multi-level structure that collectively considers heterogeneous subsystems to achieve multi-objective optimization by managing trade-offs between competing objectives. Finally, the requirements and feasibility of such a framework in real-world applications are discussed, outlining how current research is evolving, defining the incoming demand for modular frameworks, high-performance computing, and shared solutions.