Neuromotor Impairments
Conditions
Keywords
Engagement, Pediatric Rehabilitation, Neuromotor Impairments, Gait, Heart Rate Variability, Electrodermal Activity
Brief summary
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.
Interventions
DEVICEArtificial 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.
Sponsors
IRCCS Eugenio Medea
Politecnico di Milano
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DEVICE_FEASIBILITY
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
5 Years to 17 Years
Healthy volunteers
No
Inclusion criteria
* Subjects aged between 5 and 17 years with neuromotor impairments who are undergoing rehabilitation therapy using the Lokomat and GRAIL devices, according to the existing clinical plan.
Exclusion criteria
* Uncooperative subjects.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| System Usability Scale (SUS) Score | Baseline | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Service Provider-Rated Measure of Client Engagement (PRIME-SP) | Baseline | 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. |
| AI Model-Inferred Engagement Level | Baseline | 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). |
Countries
Italy
Contacts
CONTACTFabio Alexander Storm, PhD
CONTACTSimone Costantini, MSc
Outcome results
None listed