VALENCIA, 9 Nov. (EUROPA PRESS) -
The Institute of Biomechanics of Valencia (IBV) combines biomechanics and artificial intelligence (AI) to develop innovative solutions applicable in the field of health and sports. This center works on the exploration and use of new methodologies for recording and analyzing biomechanical data of human movements, based on deep learning, to develop new solutions applicable to the two aforementioned areas.
These investigations, also called Deep-Lab, are funded by the Valencian Institute of Business Competitiveness (Ivace), as reported by the IBV in a statement. Deep Learning emulates human learning in order to obtain certain knowledge and capabilities.
New data recording technologies based on Deep Learning use multiple layers of artificial neurons to automatically learn and extract features, added the institute, which noted that Deep Learning has become "an essential tool in recent times" that It can represent "an opportunity for improvement in the near future in fields such as research, health and sports."
In the field of health, the IBV has specified, the use of these tools can contribute to "a considerable improvement in the effectiveness and efficiency of services and can lead to considerable savings both in terms of consultation times and in terms of economic". Furthermore, artificial intelligence makes it possible to optimize the current registration methodologies used in functional assessment services and have a direct application for the improvement of medical care services.
Additionally, the use of artificial intelligence techniques can contribute to optimizing registration systems for research in sports and improving athlete performance, the research center has added.
As an example, he cited the possibility of carrying out recordings in a real environment, and without the need to use body markers, something "especially interesting" because "there are many sports movements and gestures that are affected or even altered in terms of the reality of movements and execution when performed in a laboratory environment and with the incorporation of body markers. The IBV has stated that this information "is of great importance for correct research to improve sports performance.
The center has pointed out that an example of the potential of this research is the improvement of the assessment processes of patients recovering from an injury. These investigations are allowing the precise analysis of movements such as elbow pronosupination, a maneuver of great relevance in the evaluation of patients with elbow pathology, one of the most common, the IBV has exposed.
"Currently, the markers used in elbow pronosupination assessment tests tend to be hidden, making proper analysis difficult in many patients, whose evaluation is critical," stated the director of innovation in technologies for biomechanical assessment at IBV. , Ignacio Bermejo.
Likewise, he pointed out that "the implementation of technology based on Deep Learning allows us to correct this problem by automatically identifying the markers, avoiding their concealment during movement and making it easier for health professionals to carry out a complete and accurate analysis of mobility." and functionality of this joint in patients who need it.
"These innovative methodologies provide a significant benefit in terms of efficiency and reliability in the patient assessment processes, which, in turn, would have a positive impact on the diagnosis, treatment and monitoring of their medical conditions," added Bermejo. .
Deep-Lab research also affects the sports field since deep learning applied to biomechanics allows analysis of movement patterns and sensor data to evaluate posture and body mechanics. This is useful in high-performance sports, physical therapy and in the prevention of sports injuries, the IBV has specified.
Another line of research and application of the project focuses on analyzing the use of systems without the need for markers in the bodies of athletes, both for clinical assessment processes of athletes and for analysis of sports performance. The project is analyzing the application of these systems in career movement.
Markerless movement analysis systems provide numerous benefits in these processes such as improving comfort for the athlete, reducing interaction with the analyzed movement, reducing preparation time, streamlining the process (especially in training sessions). repeated evaluation) and the improvement of the precision and continuity of motion capture, all of which results in an improvement in the efficiency and reliability of the evaluations.
To carry out the project, IBV collaborates with companies related to both sectors such as Unión de Mutuas, Umivale Activa, European University of Valencia, Catholic University of Valencia, IMED Valencia Hospital, COP Ortopedia and Training for You.