Clinical Evaluation of Intuitive, Bidirectional Strategies for the Control of Multi-articulated Prostheses for Upper Limb Amputation

NCT06886295 | Recruiting DMC

• 1 other identifier: IOR2024FIT4
• Study Type: interventional
• Target: 14
• Locations: 1 country
• Sites: 1

Brief Summary

The investigators propose to validate a non-invasive upper limb prosthesis capable of combining: 1) intuitive movement control through machine learning applied to myoelectric signals, and 2) vibrotactile sensory feedback in response to touch and object release events. The prosthesis is composed at the minimum of skin-surface electrodes for myoelectric signals, vibrotactile actuators, a multi-articulated and instrumented hand prosthesis and a centralized control system. Such system is validated for several weeks in non-supervised environments.

Conditions

Upper Limb Amputation

Keywords

amputation; prosthesis; pattern recognition; regression; vibrotactile; sensory feedback; myoelectric; EMG

Outcome Measures

Primary Outcomes (5)

• Southampton Hand Assessment Procedure (SHAP)

  The test consists of sequentially manipulating 6 lightweight and 6 heavyweight abstract objects and 14 activities of daily living over a specific formboard. Lightweight objects should be manipulated first. The task involves pushing a button to start a timer, picking up and moving the object from the rear slot to the front slot on the board, and completing the task by depressing the button on the timer again.

  Measured at the baseline with the clinical prosthesis already in use (if any), then measured at the first and last week of each home-trial. Lastly, measured again after at least 1 wash-out week from the last home-trial

• Clothespin Relocation Test (CRT)

  The objective is to evaluate the performance of a prosthetic user in a controlled environment and subsequently provide a measure of the user's expected functionality level outside the laboratory/clinic. The user is instructed to allocate three clothespins between a horizontal rod and a vertical rod and asked to fill out a post-test survey after five trials of the CRT.

  Measured at the baseline with the clinical prosthesis already in use (if any), then measured at the first and last week of each home-trial. Lastly, measured again after at least 1 wash-out week from the last home-trial

• Pick and Lift Test (PLT)

  The PLT measures motor coordination, i.e., the ability to coordinate the grip force and the load force while lifting an object, as well as the reliability of the recorded control signal while transporting the object. While the subject is sitting on a chair with the intact arm parallel to the trunk, and the amputated limb extending anteriorly on a table, he/she is asked to lift a small object from the table with the prosthesis.

  Measured at the baseline with the clinical prosthesis already in use (if any), then measured at the first and last week of each home-trial. Lastly, measured again after at least 1 wash-out week from the last home-trial

• Virtual Egg Test (VET)

  The VET resembles the task of transporting fragile and robust objects, thus requiring both gross and fine dexterity. The test is composed of 11 Virtual Eggs that collapse if the grasping force exceeds their breaking thresholds, ranging from 0.4 N to 11.5 N. The test aims to transport each Virtual Egg over the barrier in the centre of the test platform without breaking it and as fast as possible. The metrics measured during the test are combined and provide two indexes that evaluate, respectively, gross and fine dexterity.

  Measured at the baseline with the clinical prosthesis already in use (if any), then measured at the first and last week of each home-trial. Lastly, measured again after at least 1 wash-out week from the last home-trial

• Usage of the prosthesis

  The prosthesis, for as long as it is powered, will log real-time information about its usage during the day. Such data will allow us to calculate the total wear/use time, as well as the quality of the usage (which moments are used and how often). The data should also provide basic information for debugging in case of system failures and adverse events.

  Usage data will be recorded by the prosthesis during the two 4 weeks home-trials

Secondary Outcomes (6)

• NASA-TLX questionnaire

  Administered after each functional test (primary outcomes). Measured at the baseline with the clinical prosthesis already in use (if any), then measured at the first and last week of each home-trial. Lastly, measured again at the end of the study

• abilHAND-ULA

  Administered before, after and at each week of the two home-trials

• OPUS-UEFS

  Administered before, after and at each week of the two home-trials

• TAPES

  Administered before, after and at each week of the two home-trials

• QuickDASH

  Administered before, after and at each week of the two home-trials

Study Arms (2)

machine learning based prosthesis control

EXPERIMENTAL

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.

Device: Prosthetic controller

conventional direct control

ACTIVE COMPARATOR

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.

Device: Prosthetic controller

Interventions

Prosthetic controller DEVICE

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; machine learning based prosthesis control

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• subjects with transradial amputation, and minimum experience in the use of myoelectric prosthesis;
• subjects who have expressed informed consent to participate in the study and consent to data processing.

You may not qualify if:

• subjects with obvious visual and oculomotor defects;
• 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.

Sponsors & Collaborators

• Scuola Superiore di Studi Universitari e di Perfezionamento Sant'Anna: lead
• IRCCS Istituto Ortopedico Rizzoli di Bologna: collaborator

Study Sites (1)

IRCCS Istituto Ortopedico Rizzoli

Bologna, Bo, 40136, Italy

RECRUITING

Central Study Contacts

Enzo Mastinu, MSc, BSc, PhD

CONTACT

0039 050 883111; enzo.mastinu@santannapisa.it

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Full Professor

Model Details: The investigators propose a pilot single-center study, randomized with crossover design to compare the intuitive control strategy with the standard-in-care direct control strategy. Each condition will be assessed with a 4 weeks home-trial, separated by at least 1 wash-out week. 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.

Study Record Dates

• First Submitted: March 10, 2025
• First Posted: March 20, 2025
• Study Start: April 1, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: April 3, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)