High Dimensional Computing Gesture Recognition (HDC-GCog)

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Healthy Volunteers
• Intervention / Treatment: Device: HDC-GCog

Detailed Description

This project aims to work on gesture recognition based on surface electromyography (EMG) recorded on the forearm. The CEA is currently developing a learning algorithm based on hyperdimensional computing designed to improve the accuracy and latency of gesture recognition. Unlike conventional computing methods, the developed approach relies on (pseudo) random hypervectors. This brings significant advantages: a simple algorithm with a well-defined set of arithmetic operations, extremely robust to noise and errors, with fast, one-pass learning that could ultimately benefit from a memory-centric architecture with a high degree of parallelism.

This research could lead to multiple applications, such as video gaming or the metaverse, but also strongly interests the healthcare field, for example in robotic prostheses, tele-surgery applications, or simply medical training using virtual reality applications.

• Study Type: Interventional
• Enrollment (Estimated): 10
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Daniel ANGLADE, MD, PhD; Phone Number: 04 38 78 17 46; Email: danglade@chu-grenoble.fr
• Study Contact Backup: Name: Caroline SANDRE-BALLESTER, PhD; Phone Number: 04 38 78 28 51; Email: csandreballester@chu-grenoble.fr

Study Locations

• France
  • Grenoble, France, 38054
    • Clinatec Cea/Chuga
    • Contact: Daniel ANGLADE, MD, PhD; Phone Number: 04 38 78 17 46; Email: danglade@chu-grenoble.fr
    • Contact: Caroline SANDRE-BALLESTER, PhD; Phone Number: 04 38 78 28 51; Email: csandreballester@chu-grenoble.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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).

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: HDC-GCog; High Dimensional Computing Gesture Recognition	Device: HDC-GCog; Surface electromyography records

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Real-time gesture recognition (latency <100ms)	Measurement of the improved gesture recognition rate with our HDC algorithm compared to other common models	3 hours
Validation of the positioning and number of electrodes used for EMG acquisition in order to maximize gesture recognition rates	Calculation of gesture recognition rates based on the number of electrodes used and their position	3 hours
Analysis of the subject's feedback regarding the ease of performing the gestures (in the form of a questionnaire)	Subject's rating of device comfort as greater than 6 on a 10-point visual analogue scale	3 hours

Collaborators and Investigators

• Sponsor: University Hospital, Grenoble
• Collaborators: Commissariat à l'Energie Atomique (CEA) Grenoble; CLINATEC

Publications and helpful links

General Publications

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

Study record dates

Study Major Dates

• Study Start (Estimated): January 15, 2026
• Primary Completion (Estimated): April 1, 2026
• Study Completion (Estimated): June 1, 2026

Study Registration Dates

• First Submitted: August 19, 2025
• First Submitted That Met QC Criteria: August 26, 2025
• First Posted (Estimated): September 4, 2025

Study Record Updates

• Last Update Posted (Actual): January 20, 2026
• Last Update Submitted That Met QC Criteria: January 15, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• 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)
• Other Study ID Numbers: 38RC25.0179

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No