TECH4DYNAMICS

The assessment of motor function has not yet reached its full potential, either because qualitative analysis methodologies are used, namely specific questionnaires, which, despite providing important data, depend on the experience of the observer and the limited amount and accuracy of the information they provide; because of the costs associated with more advanced methodologies, which require greater expense and specialization in analysis, representing an additional barrier to obtaining a complete understanding of motor function; or because of the time needed to collect and obtain the scores, which in some cases, when the result is released, the subjects are no longer in the same condition as when they were evaluated. In response to this kind of issues, in recent years, portable technology has sought to play a more active role in the assessment of motor functions, both through wearables or specialized equipment. Bates and Sunderam (2023), Beswick et al. (2022), Bortolani et al. (2022), and Sica et al. (2021) conducted systematic reviews that highlight the different applications that this type of technology has had in movement analysis, demonstrating the growing importance of equipment development. Nevertheless, these publications still point to a lack of validation and refer mainly to populations with specific neural conditions, leaving open the need to promote further studies in this area. Inertial Measurement Units (IMUs) are low-cost sensors with wide applicability in various contexts (Bortolani et al., 2022; Camomilla et al., 2018). A significant portion of current portable technology has incorporated these sensors, allowing the transfer of the applicability of specific IMU-based equipment to devices used in everyday life (e.g., smartphones, smartwatches). This also enables real-time analysis of motor function, which could potentially alert users, for instance, to consult their doctor.

The project mission consists of develop and implement IMU-based methods for the assessment of motor function across different age groups and health conditions, especially in neurodegenerative diseases. The collection of continuous data in fine and gross motor skills and the testing of its validity associated with non-linear analysis methods opens up new frontiers in the understanding of complex human movement patterns, allowing for a more precise and personalized assessment of motor functions. In addition, this innovative approach by incorporating artificial intelligence (AI) and deep learning models into the analysis of motor function data, makes it possible to develop more effective interventions and technologies for different populations, promoting advances in the optimization of physical performance, health and rehabilitation, with applications in exercise, clinical and educational contexts. AI-powered systems can automatically analyze large datasets captured from IMUs and other wearable sensors, identifying subtle patterns and trends that may not be discernible through traditional analysis. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can be used to detect and classify different types of motor activities or dysfunctions by processing complex time-series data (Cheriet et al., 2023). These models not only enhance the accuracy of the analysis but also enable predictive insights, such as forecasting the progression of neurodegenerative diseases based on early motor function signals (Patel et al., 2021; Sigcha et al., 2023). Furthermore, the project will utilize AI models for the personalization of assessments. By continuously learning from individual data over time, the system can adapt to the user's unique motor patterns, thus providing highly tailored recommendations for movement improvement or rehabilitation. Another innovative layer that AI can provide is the real-time feedback mechanism, where users can receive instant guidance or alerts if abnormal motor patterns are detected, which is critical in clinical settings for early intervention.