Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports

• Published in: Applied sciences Vol. 12; no. 8; p. 3981
• Main Authors: Ortiz-Padilla, Vanessa E., Ramírez-Moreno, Mauricio A., Presbítero-Espinosa, Gerardo, Ramírez-Mendoza, Ricardo A., Lozoya-Santos, Jorge de J.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Acceleration; Artificial intelligence; Athletes; Biomechanical engineering; Biomechanics; Biometrics; biometry; Bones; Case studies; digital tracking; Football; force estimation; Fractures; Health risks; Human performance; Injury prevention; Jumping; Literature reviews; Polls & surveys; Sensors; Software; Sports; Sports injuries; Swimming; video analysis
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12083981

This work presents a survey literature review on biomechanics, specifically aimed at the study of existent biomechanical tools through video analysis, in order to identify opportunities for researchers in the field, and discuss future proposals and perspectives. Scientific literature (journal papers and conference proceedings) in the field of video-based biomechanics published after 2010 were selected and discussed. The most common application of the study of biomechanics using this technique is sports, where the most reported applications are american football, soccer, basketball, baseball, jumping, among others. These techniques have also been studied in a less proportion, in ergonomy, and injury prevention. From the revised literature, it is clear that biomechanics studies mainly focus on the analysis of angles, speed or acceleration, however, not many studies explore the dynamical forces in the joints. The development of video-based biomechanic tools for force analysis could provide methods for assessment and prediction of biomechanical force associated risks such as injuries and fractures. Therefore, it is convenient to start exploring this field. A few case studies are reported, where force estimation is performed via manual tracking in different scenarios. This demonstration is carried out using conventional manual tracking, however, the inclusion of similar methods in an automated manner could help in the development of intelligent healthcare, force prediction tools for athletes and/or elderly population. Future trends and challenges in this field are also discussed, where data availability and artificial intelligence models will be key to proposing new and more reliable methods for biomechanical analysis.