The primary aim of this research is to investigate how AI can be utilized to analyze and improve movement patterns and muscle activity during aquatic exercise, enhancing rehabilitation outcomes and athletic performance.
A cohort of participants engaged in a structured aquatic exercise program while utilizing the underwater analysis system and wearable sensors to collect kinematic and electromyographic data. The high-speed cameras of type VLG-22C.I , 2040 x 1084 px, ams (CMOSIS) CMV2000, enable to determine the desired points trajectories and spatial angular variations onto either mechanical or biomechanical mobile systems through successive identifications of the joint applied mechanics and materials. AI algorithms were developed to analyze this data, focusing on identifying movement kinematics and muscle activation patterns.
Preliminary findings indicate that the integration of AI significantly improved the accuracy of movement analysis, leading to personalized feedback that enhanced exercise efficacy. Participants demonstrated notable improvements in movement quality and muscle activation, as measured by increased efficiency in movement kinematics and reduced risk of injury.
The application of AI in conjunction with the underwater analysis system and wearable sensors holds great promise for advancing aquatic exercise methodologies. This approach not only enhances the assessment capabilities of aquatic physiotherapists but also empowers athletes and individuals undergoing rehabilitation to optimize their performance and recovery. Future research will further explore the long-term benefits of this technology in various aquatic settings.
This research has significant implications for both clinical practice and athletic training. By leveraging AI technology, physiotherapists can provide more precise assessments and tailored interventions, ultimately improving rehabilitation outcomes. For athletes, the ability to receive immediate, data-driven feedback can enhance performance and reduce the likelihood of injury. Furthermore, this study paves the way for future advancements in aquatic therapy and training, potentially leading to standardized protocols that integrate AI across various aquatic settings. The study employed machine learning techniques to provide immediate objective feedback to participants, enabling real-time adjustments to exercise techniques.
Aquatic Exercise
Wearable Sensors
