Ilaria Bortone

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Recent Grants

Grant: Close

TELOS - Tailored neurorehabilitation thErapy via multi-domain data anaLytics and adaptive seriOus games for children with cerebral palSy

Open Date: 2020-10-01

Close Date: 2023-03-01

Grant: Close

Robot4Children (R4C)

Open Date: 2016-10-01

Close Date:

Grant: Close

KISS-Health (Knowledge Intensive Social Services for Health)

Open Date: 2012-06-01

Close Date: 2015-06-01

Articles (10)

Year:

2023

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.

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2023

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Collaborators (8)

eugenio di sciascio

full professor

Politecnico di Bari

ITALY
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Madia Lozupone

University of Bari Aldo Moro

ITALY
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Domenico Lofù

Assistant Professor (RTD-A) in Information Processing Systems

Politecnico di Bari

ITALY
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Francesco Panza

University of Bari Aldo Moro

ITALY
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Tommaso Di Noia

Polytechnic University of Bari

ITALY
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Nicola Quaranta

Full Professor Otolaryngology

Università degli Studi di Bari

ITALY
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Mario Altamura

Associate Professor in Psychiatry

University of Foggia

ITALY
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Giancarlo Sborgia

University of Bari

ITALY
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