An interventional study of Artificial Intelligence Model for Rehabilitation Engagement Monitoring in Neuromotor Impairments, sponsored by IRCCS Eugenio Medea. Recruiting at 1 site in Italy. Open to participants aged 5 Years to 17 Years. Per ClinicalTrials.gov, last updated 2026-11-10.
Sponsored by IRCCS Eugenio Medea · Not applicable, Interventional, and Device feasibility
What is the purpose of this study? This study aims to evaluate the usability and feasibility of an artificial intelligence-based model designed to monitor in real-time the engagement and motor performance of pediatric patients during technology-assisted rehabilitation.
Who can take part? 15 participants between 5 and 17 years old with neuromotor impairments will take part, along with at least 5 of their referring physiotherapists.
What will happen in the study? Each pediatric patient will take part in a single, 1-hour rehabilitation session using either the Lokomat or GRAIL system, according to their standard clinical prescription. During the session, the physiotherapist will have access to a display showing real-time data from the AI model, including the patient's heart rate, engagement level, pleasantness, activation, and motor performance. At the end of the session, the physiotherapist will complete a System Usability Scale (SUS) questionnaire and provide direct feedback on how to improve the model.
Why is this study important? Assessing the usability of this real-time monitoring tool is a necessary step to understand if it is practical for clinical use. Providing therapists with objective, real-time insights into a child's psychological and physical state can ultimately help tailor therapy to the specific needs of each patient, improving the overall rehabilitation experience.
IRCCS Eugenio Medea is the lead sponsor of 108 studies on the registry; 51 are open to participants now.
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Participants in this experimental arm, consisting of pediatric patients with neuromotor impairments, will undergo a single 1-hour technology-assisted rehabilitation session using either the Lokomat or GRAIL system. During the session, the physiotherapist will use a display showing real-time outputs from the AI-based model, including the patient's heart rate, engagement levels, pleasantness, activation, and motor performance. The model acts as an observational support tool and will not directly alter the standard rehabilitation protocol. At the end of the session, the physiotherapist will evaluate the usability of the system.
Device: Artificial Intelligence Model for Rehabilitation Engagement Monitoring
The intervention consists of the deployment of a real-time AI-based monitoring system during a standard technology-assisted rehabilitation session. The physiotherapist is provided with a display showing continuous feedback on the patient's engagement levels, emotional state (pleasantness and activation), motor performance, and heart rate. The model processes physiological and inertial data collected via wearable sensors, acting purely as an observational support tool without altering the standard rehabilitation protocol.
System Usability Scale (SUS) Score
This validated questionnaire is intended to evaluate the usability and feasibility of a system or product. It is composed of 10 items assessing factors such as system complexity, ease of use, and functionality integration. Each item is proposed on a 5-points Likert scale, with minimum value 1 and maximum value 5. Higher overall values stand for a higher degree of agreement with respect to the statement provided by the single item. For odd items, higher values stand for higher usability. For even items, higher values stand for lower usability.
Time frame: Baseline
Service Provider-Rated Measure of Client Engagement (PRIME-SP)
This measure is intended to capture the therapist's observation of patient engagement. PRIME-SP is a validated self-reported questionnaire that is composed of three main parts: Part A, where the therapist can perform an overall evaluation of patient engagement according to a 5-point Likert scale (from 0 to 4, with higher values corresponding to positive engagement); Part B, where the therapist can perform a domain-dependent (affective, cognitive, behavioral domains) evaluation of patient engagement according to a 5-point Likert scale (from 0 to 4, with higher values corresponding to positive engagement); Part C, where the therapist can take free notes about factors and circumstances that he/she believes may have affected patient engagement in the session.
Time frame: Baseline
AI Model-Inferred Engagement Level
This objective measure is intended to capture the patient's continuous engagement level during the rehabilitation session. The AI-based model infers the engagement state using feed-forward neural networks that process real-time physiological data (such as HRV and EDA) and inertial signals (IMU). The model provides a categorical evaluation of engagement (low vs high).
Time frame: Baseline
Plan to share: Yes — The results obtained at the end of this clinical trial will be presented at national and international conferences and submitted to peer-reviewed international journals. The raw data of the study will be published among the supplementary materials of scientific articles and/or uploaded to Zenodo, a multidisciplinary repository, managed by CERN in Geneva, which allows researchers to share and preserve research results in any size and form. Depositing data in ZENODO guarantees their compliance with the FAIR principles.
Supporting information: Study protocol, Statistical analysis plan
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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