High Dimensional Computing Gesture Recognition

Starting soon · Not applicable

Conditions studied: Healthy Volunteers

In brief

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 facts

Study ID
NCT07155460
Run by
University Hospital, Grenoble
People needed
10
Starts
2026-01-15
Expected to finish
2026-06-01
Last updated by the study team
2026-01-20

Who can join

Age: 18 and older, up to 65. Sex: any. Healthy volunteers: accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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