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High Dimensional Computing Gesture Recognition

NCT07155460 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 10

Last updated 2026-01-20

No results posted yet for this 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.

Conditions

  • Healthy Volunteers

Interventions

DEVICE

HDC-GCog

Surface electromyography records

Sponsors & Collaborators

  • Commissariat à l'Energie Atomique (CEA) Grenoble

    collaborator UNKNOWN

  • CLINATEC

    collaborator UNKNOWN

  • University Hospital, Grenoble

    lead OTHER

Study Design

Allocation
NA
Purpose
OTHER
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-01-15
Primary Completion
2026-04-30
Completion
2026-06-30

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07155460 on ClinicalTrials.gov