Not yet recruitingNCT07155460HDC-GCogUpdated Jan 20, 2026

High Dimensional Computing Gesture Recognition

An interventional study of HDC-GCog in Healthy Volunteers, sponsored by University Hospital, Grenoble. Not yet recruiting at 1 site in France. Open to participants aged 18 Years to 65 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-01-20.

Sponsored by University Hospital, Grenoble · Not applicable, Interventional, and Other

From the registry’s dates

Primary completion was expected by Apr 2026, 6 months ago, but the record still lists the study as not yet recruiting.

Phase: Not applicable
Study type: Interventional
Enrollment: 10
Allocation: Not applicable
Ages: 18 Years to 65 Years
Sex: All

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.

Read the 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.

Conditions studied

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)

In context

Lead sponsor

University Hospital, Grenoble is the lead sponsor of 815 studies on the registry; 205 are open to participants now.

Counted across the registry records on this site, refreshed daily.

Who can participate

Ages eligible: 18 Years to 65 Years
Sexes eligible: All
Accepts healthy volunteers: Yes

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

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 design

Phase: Not applicable
Primary purpose: Other
Allocation: Not applicable
Intervention model: Single group
Masking: None (open label)
Enrollment: 10 participants (estimated)

Study arms

Experimental
HDC-GCog
High Dimensional Computing Gesture Recognition
Device: HDC-GCog

Interventions

DeviceHDC-GCog
Surface electromyography records

What researchers measure

Primary outcomes

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
Time frame: 3 hours

Secondary outcomes

Real-time gesture recognition (latency <100ms)
Measurement of the improved gesture recognition rate with our HDC algorithm compared to other common models
Time frame: 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
Time frame: 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
Time frame: 3 hours

Study locations

1 site

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

References and documents

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

Individual participant data

Plan to share: No

Updates

Tracking since Sep 25, 2026

No changes since tracking began. The registry record was last updated on Jan 20, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗

Registry details

Key details

Study ID: NCT07155460
Lead sponsor: University Hospital, Grenoble
Collaborators: Commissariat à l'Energie Atomique (CEA) Grenoble, CLINATEC
Responsible party: Sponsor
First posted: Sep 4, 2025
Start date: Jan 15, 2026 (estimated)
Primary completion: Apr 2026 (estimated)
Completion: Jun 2026 (estimated)
Last update: Jan 20, 2026

Study contacts

Daniel ANGLADE, MD, PhD

Contact

danglade@chu-grenoble.fr

04 38 78 17 46

Caroline SANDRE-BALLESTER, PhD

Contact

csandreballester@chu-grenoble.fr

04 38 78 28 51

Oversight

Data monitoring committee: No
FDA-regulated drug: No
FDA-regulated device: No

View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is not yet recruiting, as verified in Jan 2026. You cannot join it, but the record below documents what was studied.

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