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Neurinnov, in collaboration with the CBV USSAP center and the CAMIN INRIA team, has conducted clinical investigations using various control interfaces, including EMG, IMU, contact sensors, and voice commands, to operate a motor neuroprosthesis. This neuroprosthesis is based on neural electrical stimulation, enabling the activation of multiples muscles via a single electrode. The clinical investigations have successfully demonstrated the feasibility of achieving grasping movements induced by neural electrical stimulation, which are controlled by the participant through external interfaces.
These external interfaces were based on existing technologies but were only suitable for research purposes due to their lack of portability. The current investigation aims to validate fully portable interfaces designed by Neurinnov, which are intended to be integral components of a future medical device that includes an implanted stimulator and its neural electrodes. The study's goal is to demonstrate that these interfaces can be used by participants with sufficient success rate (clinical performance) to support daily use.
Our main hypothesis is that the participants can effectively use at least two of the six control interfaces presented to them to detect their intention to perform a motor action within a software environment under constant conditions. These interfaces include voice commands, inertial measurement unit (IMU) sensors, surface electromyography (EMG) sensors, switch, joystick, and earswitch.
Full description
Various control interfaces (CIs) are used to capture user intent for operating assistive devices. In recent years, several methods have been developed to detect user intent for controlling invasive motor neuroprostheses for upper limbs. The Freehand System® utilized an external shoulder position sensor on the contralateral side to detect user intent and control hand grasp stimulation. In some studies, a switch was integrated with the sensor to turn the system on and off and to select the type of grasp (palmar or lateral). Brain-computer interface (BCI) systems based on electroencephalography (EEG) have also been employed. The Implantable Stimulator-Telemeter ('IST-10'), the second generation of the Freehand, had ten stimulation channels and was used with an implantable joint angle sensor. The third generation, the Implanted Stimulator Telemeter (IST-12), used myoelectric signals. However, no direct comparison has been made between these different modalities, nor has their relevance been determined.
Therefore, this study aims to evaluate the performance (efficacy: reliability and precision) of six non-invasive control interfaces. The efficacy criterion is defined by the ability to reliably and accurately control a motor action as illustrated by software on a screen. The six control interfaces are: (1) inertial measurement unit (IMU) sensors that record movements of the contralateral shoulder; (2) surface electromyography (EMG) sensors that capture voluntary muscle contractions of the contralateral limb; (3) a pressure sensor button (switch sensor); (4) a pressure sensor joystick (joystick sensor); (5) a voice recognition sensor (voice sensor) that incorporates a machine learning model capable of recognizing specific words spoken by the participant; and (6) an Ear-Switch® sensor that detects movements of a muscle inside the ear, the tensor tympani.
The study will be conducted over six sessions:
(V1) Selection Visit: The selection visit will be conducted by the coordinating investigator, who will monitor the participant throughout the trial.
(V2) Inclusion Visit: This visit will include:
(V3 to V5) Experimental Visits:
(V6) End-of-Study Visit: This final visit will consist of a clinical and psychological follow-up consultation to ensure the absence of any adverse effects.
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30 participants in 1 patient group
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Central trial contact
Charles FATTAL Charles FATTAL, MD, PhD; David GUIRAUD, PhD
Data sourced from clinicaltrials.gov
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