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High Dimensional Computing Gesture Recognition
Sponsor: University Hospital, Grenoble
Summary
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.
Key Details
Gender
All
Age Range
18 Years - 65 Years
Study Type
INTERVENTIONAL
Enrollment
10
Start Date
2026-01-15
Completion Date
2026-06
Last Updated
2026-01-20
Healthy Volunteers
Yes
Conditions
Interventions
HDC-GCog
Surface electromyography records
Locations (1)
Clinatec Cea/Chuga
Grenoble, France