Clinatec Cea/Chuga
Grenoble, 38054, France
Location contact
Caroline SANDRE-BALLESTER, PhD
CONTACT
Daniel ANGLADE, MD, PhD
CONTACT
NCT Number: NCT07155460
The primary objective of this study is the Improvement of gesture recognition and classification accuracy through the use of the HDC algorithm compared to other classification methods (KNN, RF, SGD, NC). The recognition rate will be expressed by the sensitivity and specificity of gesture recognition. The model will be trained on a portion of the dataset and tested on the remaining part to avoid any bias.
The secondaries objectives are the :
* Improvement of gesture recognition accuracy with our HDC algorithm compared to other standard models. * Calculation of gesture recognition rates depending on the number of electrodes used and their position. * Subject's assessment of device comfort rated above 6 on a 10-level visual analog scale. * Subject's assessment of ease of performing the gesture rated above 6 on a 10-level visual analog scale.
Trial opening soon.
Get Notified18 year–65 year
All sexes
Interventional
Not applicable
Grenoble, 38054, France
Caroline SANDRE-BALLESTER, PhD
CONTACT
Daniel ANGLADE, MD, PhD
CONTACT
This project aims to work on gesture recognition based on surface electromyography (EMG) recorded on the forearm. The CEA is currently developing a learning algorithm based on hyperdimensional computing designed to improve the accuracy and latency of gesture recognition. Unlike conventional computing methods, the developed approach relies on (pseudo) random hypervectors. This brings significant advantages: a simple algorithm with a well-defined set of arithmetic operations, extremely robust to noise and errors, with fast, one-pass learning that could ultimately benefit from a memory-centric architecture with a high degree of parallelism.
This research could lead to multiple applications, such as video gaming or the metaverse, but also strongly interests the healthcare field, for example in robotic prostheses, tele-surgery applications, or simply medical training using virtual reality applications.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Surface electromyography records
Time frame: 3 hours
Calculation of gesture recognition rate expressed in percentage of gesture recognition
Time frame: 3 hours
Measurement of the improved gesture recognition rate with our HDC algorithm compared to other common models
Time frame: 3 hours
Calculation of gesture recognition rates based on the number of electrodes used and their position
Time frame: 3 hours
Subject's rating of device comfort as greater than 6 on a 10-point visual analogue scale
Contact information is provided by the study sponsor or research team.
Caroline SANDRE-BALLESTER, PhD
CONTACT
Daniel ANGLADE, MD, PhD
CONTACT
University Hospital, Grenoble
Other
Acronym: HDC-GCog
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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