High Dimensional Computing Gesture Recognition

NCT07155460 | Not Yet Recruiting

• 1 other identifier: 38RC25.0179
• Study Type: interventional
• Target: 10
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Surface ElectroMyoGraphy (sEMG); K-Nearest Neighbor classification algorithm (KNN); Nearest Centroids classification algorithm (NC); Random Forest classification algorithm (RF); Stochastic Gradient Descent classification algorithm (SGD); High Dimensional Computing (HDC)

Outcome Measures

Primary Outcomes (1)

• Gesture recognition rate using a device composed of 32 high-frequency surface EMG electrodes

  Calculation of gesture recognition rate expressed in percentage of gesture recognition

  3 hours

Secondary Outcomes (3)

• Real-time gesture recognition (latency <100ms)

  3 hours

• Validation of the positioning and number of electrodes used for EMG acquisition in order to maximize gesture recognition rates

  3 hours

• Analysis of the subject's feedback regarding the ease of performing the gestures (in the form of a questionnaire)

  3 hours

Study Arms (1)

HDC-GCog

EXPERIMENTAL

High Dimensional Computing Gesture Recognition

Device: HDC-GCog

Interventions

HDC-GCog DEVICE

Surface electromyography records

HDC-GCog

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Healthy, right-handed volunteer subject,
• Male or female,
• Age between 18 and 65 years inclusive,
• BMI \< 30 kg/m²,
• Minimum forearm circumference less than 15 cm,
• Subjects agree to shaving or trimming of the right forearm.
• Agreement to the study non-opposition form,
• Subject affiliated with a social security scheme,
• Registered in the national database of individuals who participate in biomedical research

You may not qualify if:

• Subject with a known motor problem in the right forearm and hand,
• Known allergy or intolerance to one of the electrode components,
• Presence of a lesion in the measurement area,
• Subject with an active medical implant (e.g. pacemaker, cochlear implant, etc.),
• Subject wearing a contraceptive implant in the measurement area.
• Female subject aware of pregnancy at the time of measurement,
• Subject refusing to shave or trim the area or whose body hair precludes shaving or trimming the area,
• Presence of a pathology likely to alter the EMG.
• Persons referred to in Articles L1121-5 to L1121-8 of the Public Health Code (corresponds to all protected persons: pregnant women, women in labour, breastfeeding mothers, persons deprived of their liberty by judicial or administrative decision, persons receiving psychiatric care under Articles L. 3212-1 and L. 3213-1 who do not fall under the provisions of Article L. 1121-8, persons admitted to a health or social establishment for purposes other than research, minors, persons subject to a legal protection measure or unable to express their consent).

Sponsors & Collaborators

• Commissariat à l'Energie Atomique (CEA) Grenoble: collaborator
• CLINATEC: collaborator
• University Hospital, Grenoble: lead

Study Sites (1)

Clinatec Cea/Chuga

Grenoble, 38054, France

Location

Related Publications (4)

• Salerno, A., Barraud, S. (2024). Evaluation and implementation of High-Dimensionnal Computing for gesture recognition using sEMG signals. Proceedings of the 2024 International Conference on Control, Automation and Diagnosis (ICCAD)

  BACKGROUND

• Salerno, A., Barraud, S. (2025). Novel and efficient hyperdimensional encoding of surface electromyography signals for hand gesture recognition, Biosensor 2025.

  BACKGROUND

• A. Sultana, F. Ahmed, Md. S. Alam, A systematic review on surface electromyography-based classification system for identifying hand and finger movements, Healthcare Analytics, 3, 100126, 2022, DOI:10.1016/j.health.2022.100126

  BACKGROUND

• Sgambato, B. G., Castellano, G. (2022). Performance comparison of different classifiers applied to gesture recognition from sEMG signals. In Bastos-Filho, T. F., de Oliveira Caldeira, E. M., Frizera-Neto, A. (Eds.), XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, Vol. 83. Springer, Cham

  BACKGROUND

Central Study Contacts

Daniel ANGLADE, MD, PhD

CONTACT

04 38 78 17 46; danglade@chu-grenoble.fr

Caroline SANDRE-BALLESTER, PhD

CONTACT

04 38 78 28 51; csandreballester@chu-grenoble.fr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: The Primary Purpose of this clinical trial is to test a prototype device for feasibility and not health outcomes.This study is conducted to confirm the design and operating specifications of a device before beginning a full clinical trial.

Study Record Dates

• First Submitted: August 19, 2025
• First Posted: September 4, 2025
• Study Start: January 15, 2026
• Primary Completion: April 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: January 20, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)