High Dimensional Computing Gesture Recognition
Condition(s) studied
Investigational drug(s) / intervention(s)
HDC-GCog: Surface electromyography records
Study 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.
Eligibility
Primary outcome measure(s)
- Gesture recognition rate using a device composed of 32 high-frequency surface EMG electrodes — 3 hours
Calculation of gesture recognition rate expressed in percentage of gesture recognition
Trial sites (1)
| Facility | City | Region | Status |
|---|---|---|---|
| Clinatec Cea/Chuga | Grenoble | France |
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Other trials for the same condition
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
View NCT07155460 on ClinicalTrials.gov ↗ ← All trials in France