STOP-ME PROJECT

STOPme - Supporting Termination Of stereotyPies in patients with Rett syndrome by advanced ambient intelligence

Project purpose

The support of patients with cognitive impairment requires effective and unobtrusive monitoring systems. The STOPme project focuses on Rett syndrome, a rare neurodevelopmental disorder characterized by severe motor and cognitive deficit, leading to stereotypies and cardiorespiratory alterations. STOPme aims to assess the patient's psychomotor activation status in real time, monitoring the onset of neuromotor stereotypies and patterns of hypo- and hyperventilation and then conditioning the patient to stop stereotypies. The project includes the development of a concept of a multi-level interactive environment, and integrates the functions of the wearable system with home automation, exploring its feasibility and market prospects.

Project purpose

To develop a wearable system of multimodal sensing (biopotentials, breathing, and biomechanical parameters) and artificial intelligence, which detects motor stereotypies and abnormal cardiorespiratory patterns of patients with Rett syndrome, actuating the environment (lights and sounds, as well as haptic feedback) to interrupt them and modulate cardiovascular parameters.

Expected results

Technological: multimodal recording system integrated with AI at the edge, and concept of an intelligent environment for their disruption.

Scientific: multimodal dataset for the characterization of stereotypies in the Rett; characterization of respiratory and cardiovascular patterns in these patients; evaluation of the impact of automatic solutions for the termination of stereotypes.

Although designed for Rett, the system will be adaptable to other pathologies.

Achieved results

• A dataset was created containing signals recorded by five magneto-inertial sensors positioned on the upper limbs and torso of the patients, with real-time manual annotation of hand stereotypies. In some cases, this was combined with a respiratory signal (with associated event annotation) and an electrocardiographic trace, recorded using a home polysomnograph. This dataset was used to train the artificial intelligence responsible for recognizing the stereotypies.
• An integrated system has been developed, consisting of a hub to collect biomechanical and physiological signals provided by commercial sensors and used to recognize motor and respiratory stereotypies in Rett syndrome through artificial intelligence techniques. The wearable component of the system utilizes innovative textile sensors based on conductive polymers made with inks specifically chosen to improve the electrical properties of the printed electrodes, particularly ionic conductivity. These are used for acquiring electrocardiograms (ECG) and surface electromyography (sEMG), as well as for measuring respiration via a custom strain-gauge. Furthermore, the system integrates features that could prove useful—after appropriate patient training—to interrupt these stereotypies: namely, haptic (vibroactuators) and environmental (lights and sounds) actuations. The system also creates an event log (diary) for neurologists, which records the frequency and duration of motor (hand) and respiratory stereotypy events. This serves to verify the effectiveness of pharmacological therapies, ensuring continuous monitoring during wakefulness (as stereotypies stop during sleep onset and sleep) without the direct intervention of caregivers.

CUP: F23C24000440006

importo totale del progetto: 439.650,00 € [359.800,00 € (UniCA) + 79.850,00 € (IUSS)]

importo finanziato: 439.650,00 €

fonte di finanziamento: Unione Europea, NextGenerationEU – Ministero dell’Università e della Ricerca – Piano Nazionale di Ripresa e Resilienza

Ecosistema RAISE - Robotics And AI for Socio- Economic Empowerment - Spoke 2: Smart Devices and Technologies for Personal and Remote Healthcare

data inizio: 10/06/2024

data fine: 31/08/2025

Partners: Università degli Studi di Cagliari (UniCA, capofila)

Istituto Universitario Studi Superiori (IUSS), Pavia

Responsabile scientifico: Prof. Danilo Pani

Contatto:

danilo.pani@unica.it