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Construction of an AI System for the Automatic Supervision of Shoulder's Rehabilitation Exercises (Rehab-SPIA)

NCT05026346 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2025-03-19

No results posted yet for this study

Summary

The current historical phase and the growing need for rehabilitation in the world make tele-rehabilitation systems, and e-Health in general, fundamental tools for increasing patient engagement and compliance with care, crucial elements for the preservation of the NHS from a perspective expenditure review and resource optimization. In particular, the rehabilitation patient has on average an adherence to the Home Exercise Program (HEP) between 30-50%, to which is frequently added a reduced effectiveness of motor learning due to the lack of feedback on the accuracy of the gesture, as is the case. it happens in the hospital or outpatient setting under the supervision of a therapist.

The new computational approaches for the analysis of data on human movement, aimed at the development of algorithms to automatically supervise the accuracy of the patient's gesture during home self-treatment exercise such as those based on Artificial Intelligence (AI) and Machine Learning (ML), especially those of the latest generation, called sub-symbolics (or connectionists) can help.

Among the most promising approaches are. Given the importance of the Home Exercise Program in shoulder disease, it was decided to select a population of patients affected by the main pathologies affecting this joint.

The main objective of the study is to create and validate a software tool for the automatic and expert analysis of the correct execution of the main rehabilitation exercises for the functional recovery of the shoulder following orthopedic pathologies.

Conditions

  • Rotator Cuff Tears

Interventions

DIAGNOSTIC_TEST

Pathologic Exercise

1. In the first phase of the project, a series of 5 active shoulder mobilization exercises characterized by an adequate range of motion will be tested to verify the set up for the video recording. 2. In the second phase shoulder movements will be recorder by a smartphone. 3. A questionnaire will be used and adapted on the basis of which to evaluate the correctness of the exercises performed by each healthy subject / patient. This questionnaire will provide a Clinical Score (CS) which assigns a numerical value to the patient's overall performance for each repetition. 4. The videos of each repetition of exercises performed by the healthy subjects / patients will then be evaluated by two different clinicians, blinded, using the questionnaire. 5. The Artificial Intelligence learning algorithm will be able to output an evaluation score that will be compared with that produced by clinicians.

Sponsors & Collaborators

  • Istituto Ortopedico Rizzoli

    lead OTHER

Principal Investigators

  • Maria Grazia Benedetti, MD · Istituto Ortopedico Rizzoli

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-04-01
Primary Completion
2024-09-30
Completion
2025-01-30

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05026346 on ClinicalTrials.gov