Motion Technologies in Support of Fence Athletes: A Systematic Review

Author:

Aresta Simona1ORCID,Musci Mariapia2ORCID,Bottiglione Francesco2ORCID,Moretti Lorenzo34ORCID,Moretti Biagio34ORCID,Bortone Ilaria3ORCID

Affiliation:

1. Department of Electrical and Information Engineering (DEI), Politecnico di Bari, 70126 Bari, Italy

2. Department of Mechanical Mathematics and Management (DMMM), Politecnico di Bari, 70126 Bari, Italy

3. Department of Translational Biomedicine and Neuroscience (DiBraiN), School of Medicine, University of Bari “Aldo Moro”, AOU Consorziale Policlinico, Piazza Giulio Cesare 11, 70124 Bari, Italy

4. Orthopaedic & Trauma Unit, AOU Consorziale Policlinico, Piazza Giulio Cesare 11, 70124 Bari, Italy

Abstract

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.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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