Clinical Evaluation of Intuitive, Bidirectional Strategies for the Control of Multi-articulated Prostheses for Upper Limb Amputation
NCT ID: NCT06886295
Last Updated: 2025-04-03
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
14 participants
INTERVENTIONAL
2025-04-01
2026-12-31
Brief Summary
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Detailed Description
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Primary Objectives The primary objective is to validate a non-invasive, transradial prosthesis that integrates the latest advancements for intuitive, bidirectional and multi-articulated control. This validation is intended to be conducted in unsupervised environments (i.e., home-use). The prosthesis under validation combines intuitive movement control through pattern recognition applied to myoelectric signals, and vibrotactile sensory feedback in response to touch and object release events.
The validation is conducted through well-established functional tests in the field designed to measure dexterity and motor coordination, and through data on daily usage.
Secondary Objectives The secondary objective is to validate the proposed prosthesis based on subjective assessments regarding user satisfaction, perceived capacity to control the device, and perceived level of disability.
Additionally, the study aims to assess whether participation in this research has any impact on the control of the clinically used prosthesis. To this end, both functional tests and self-assessment questionnaires will be used to evaluate the clinical prosthesis before and after the trial protocol.
Conditions
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Study Design
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RANDOMIZED
CROSSOVER
Each condition will dispose of a non-invasive upper limb prosthesis composed at the minimum of skin-surface electrodes for myoelectric signals, a multi-articulated and instrumented hand prosthesis, a vibrotactile actuator enabled at touch/release of the objects, and a centralized control system. The two conditions will differ based on the movement control strategy, machine learning based as opposed to conventional threshold control. The former tries to simplify the control of all degrees of freedom available by decoding intuitive muscular contraction at the residual limb. The latter applies simple thresholds to the myoelectric signals to provide sequential activations of all degrees of freedom available.
TREATMENT
NONE
Study Groups
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machine learning based prosthesis control
A non-invasive upper limb prosthesis whose control is facilitated through machine learning applied to myoelectric signals. The movements on the prosthesis are triggered by intuitive muscular contractions performed at the residual limb.
Prosthetic controller
The intervention includes an upper limb prosthesis composed at the minimum by skin-surface electrodes for myoelectric signals, a vibrotactile actuator, a multi-articulated and instrumented hand prosthesis and a centralized control system. The centralized prosthetic controller processes the myoelectric signals acquired at the residual limb to enable the movements on the multi-articulated prosthesis.
conventional direct control
A non-invasive upper limb prosthesis whose control is enable by standard-in-care movement control strategy. The movements on the prosthesis are sequentially triggered through simple thresholds applied to myoelectric signals.
Prosthetic controller
The intervention includes an upper limb prosthesis composed at the minimum by skin-surface electrodes for myoelectric signals, a vibrotactile actuator, a multi-articulated and instrumented hand prosthesis and a centralized control system. The centralized prosthetic controller processes the myoelectric signals acquired at the residual limb to enable the movements on the multi-articulated prosthesis.
Interventions
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Prosthetic controller
The intervention includes an upper limb prosthesis composed at the minimum by skin-surface electrodes for myoelectric signals, a vibrotactile actuator, a multi-articulated and instrumented hand prosthesis and a centralized control system. The centralized prosthetic controller processes the myoelectric signals acquired at the residual limb to enable the movements on the multi-articulated prosthesis.
Eligibility Criteria
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Inclusion Criteria
* subjects who have expressed informed consent to participate in the study and consent to data processing.
Exclusion Criteria
* concomitant comorbidities/disabilities/chronic conditions, general or localized (Multiple Sclerosis, Parkinson's disease, muscle tone disorders, malignant neoplasms, etc.), which may interfere with the performance of the study;
* pregnancy or breastfeeding;
* declared or evident cognitive deficits that compromise the understanding of the required tasks (mini Mental State Examination≤ 24);
* difficulty in understanding the Italian language.
18 Years
65 Years
ALL
No
Sponsors
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IRCCS Istituto Ortopedico Rizzoli di Bologna
UNKNOWN
Scuola Superiore di Studi Universitari e di Perfezionamento Sant'Anna
OTHER
Responsible Party
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Christian Cipriani
Full Professor
Locations
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IRCCS Istituto Ortopedico Rizzoli
Bologna, Bo, Italy
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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IOR2024FIT4
Identifier Type: -
Identifier Source: org_study_id
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