Abstract
Project Title: REBALANCE - REinforcing BALANCE with a neurally-driven wearable assistive device The REBALANCE project aims to develop and validate an innovative wearable assistive device for balance support, driven by neurophysiological signals. The main goal is to address the issue of balance loss, a common condition related to aging and neurological disorders, which significantly increases the risk of falls. The device will integrate advanced technologies from artificial intelligence, biomechanics, and neurophysiology to provide personalized, real-time assistance. Objectives: 1. Wearable Device Development: Design a lightweight and comfortable prototype that can be worn during daily activities. 2. Integration of Neurophysiological Sensors: Utilize electromyography (EMG) and electroencephalography (EEG) to acquire data on muscle and brain activity related to balance control. 3. Machine Learning Algorithm Implementation: Develop AI models capable of interpreting neurophysiological signals to predict balance loss before it occurs and provide proactive support. 4. Clinical Validation: Test the device's effectiveness on a sample of elderly subjects and/or patients with balance impairments, evaluating improvements in stability, reduction in fall risk, and user acceptability. 5. Neurophysiological Analysis: Study how the use of the device influences the neural mechanisms of balance control, fostering brain and muscle plasticity. Expected Results: 1. A functional prototype of the wearable device: An integrated system capable of monitoring balance and providing proactive assistance. 2. High-precision predictive algorithms: Machine Learning models that can forecast balance instabilities with sufficient lead time for intervention. 3. Improved balance and reduced falls: Clinical demonstration of the device's efficacy in enhancing subjects' stability and safety. 4. Advanced scientific knowledge: Publications in peer-reviewed journals on the neurophysiological mechanisms underlying balance control and the effectiveness of robotic assistance. 5. Potential for commercialization: Creation of a solid technological foundation for the development of future products in the fields of rehabilitation and geriatric care. The REBALANCE project intends to translate basic neurophysiological research into a tangible technological solution, with a significant impact on the quality of life for millions of people. Results 1) Predictive analysis Building on the shared expertise of the involved group and participants' previous projects (SoftACT MAECI, DAREFALLSPREDICT PNC, FARSEEING EU), the project's outputs will contribute to advancing the development of a class of wearable devices to prevent Loss of Balance for the elderly. A critical finding from the present study underscores the necessity for fall prevention wearable devices to generate substantial forces—a capability currently constrained by significant technical limitations. Future research directions will focus on developing digital-twin kinematic models of human movement to identify optimal intervention windows. This approach aims to modify end-users' movement patterns through subtle "nudging" mechanisms that correct kinematics proactively, rather than relying on reactive fall remediation strategies. The identification of physiological responses to loss of balance (LoB) across diverse demographic cohorts provides a robust biomechanical framework for refining both the wearable device architecture and its control algorithms. Building upon previous work conducted by consortium members in the frame of the AGE-IT PNRR funded project, current efforts are advancing toward a soft prototype incorporating Shape Memory Alloys (SMAs) as actuators, thereby enhancing user comfort and device embodiment. Furthermore, insights gained from a collaborative project—funded through a previous PNRR cascade funding initiative and focused on soft wearable lower limb exoskeletons—will inform subsequent design iterations. 2) Final comments This accumulated knowledge base will be systematically integrated into future research endeavors aimed at engineering an ergonomic, potentially soft-robotics-based wearable system designed to mitigate fall risk in elderly populations. A target for funding will be the next HORIZON-RIA (Research and Innovation Action), with the resubmission of the proposal “AI4ANTIFALL: Personalized Fall Prediction, Prevention and Protection through Generative AI and Wearable Support Systems for Neurological Patients and the Elderly”, which reached a score of 10.5/12 at the last call.
Dettagli del progetto
Responsabile scientifico: Lorenzo Chiari
Strutture Unibo coinvolte:
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi"
Coordinatore:
Università degli Studi di PADOVA(Italy)
Contributo totale Unibo: Euro (EUR) 111.031,00
Durata del progetto in mesi: 24
Data di inizio
18/10/2023
Data di fine:
28/02/2026
