Healthy VolunteersHDC-GCogNCT07155460
High Dimensional Computing Gesture Recognition
A study testing HDC-GCog in people with Healthy Volunteers, run by University Hospital, Grenoble at 1 site in France.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Healthy Volunteers
- What is being tested?
- HDC-GCog
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10 participants
- Where
- 1 siteFrance
- Study dates
- 15 Jan 2026to Jun 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
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- 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
Who cannot join
- 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,
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- 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).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site
Clinatec Cea/Chuga
Grenoble, 38054
Daniel ANGLADE, MD, PhD, 04 38 78 17 46, danglade@chu-grenoble.fr
Caroline SANDRE-BALLESTER, PhD, 04 38 78 28 51, csandreballester@chu-grenoble.fr
About this study
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
- Healthy Volunteers
- Being tested
- HDC-GCog
- 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)
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
HDC-GCogDevice
Surface electromyography records
| Study group | Role | What they receive |
|---|---|---|
| HDC-GCog | Experimental | High Dimensional Computing Gesture Recognition Receives: Device: HDC-GCog |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 hours
Secondary outcomes (3)
Real-time gesture recognition (latency <100ms)
Measurement of the improved gesture recognition rate with our HDC algorithm compared to other common models
Measured: 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
Measured: 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
Measured: 3 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Sep 2025
- Study start15 Jan 2026 (estimated)
- Primary completionApr 2026 (estimated)
- Study completionJun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Healthy VolunteersStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 10 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University Hospital, Grenoble
- Sponsor type
- University, hospital or other
- Collaborators
- Commissariat à l'Energie Atomique (CEA) Grenoble, CLINATEC
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07155460
- Sponsor's protocol ID
- 38RC25.0179
- Acronym
- HDC-GCog
- Status last verified
- Jan 2026
- First submitted
- 19 Aug 2025
- Last updated
- 20 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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 at a glance
From the registry record, last updated by the sponsor on 20 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- High Dimensional Computing Gesture Recognition
- ClinicalTrials.gov ID
- NCT07155460
- Acronym / sponsor ID
- HDC-GCog / 38RC25.0179
- 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)
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 10 participants (estimated)
- Lead sponsor
- University Hospital, GrenobleUniversity, hospital or other
- Collaborators
- Commissariat à l'Energie Atomique (CEA) Grenoble, CLINATEC
- Study start
- 15 Jan 2026 (estimated)
- Primary completion
- Apr 2026 (estimated)
- Study completion
- Jun 2026 (estimated)
- First posted
- 4 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| HDC-GCog | Experimental | Device: HDC-GCogHigh Dimensional Computing Gesture Recognition |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (9 listed)
- 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,
Key exclusion criteria (9 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| HDC-GCogSurface electromyography records | Device | HDC-GCog |
Primary outcome
Gesture recognition rate using a device composed of 32 high-frequency surface EMG electrodes
Time frame: 3 hours
Calculation of gesture recognition rate expressed in percentage of gesture recognition
3 secondary outcomes
Real-time gesture recognition (latency <100ms)
Time frame: 3 hours
Validation of the positioning and number of electrodes used for EMG acquisition in order to maximize gesture recognition rates
Time frame: 3 hours
Analysis of the subject's feedback regarding the ease of performing the gestures (in the form of a questionnaire)
Time frame: 3 hours
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Jan 2026.
- BackgroundSalerno, 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)
- BackgroundSalerno, A., Barraud, S. (2025). Novel and efficient hyperdimensional encoding of surface electromyography signals for hand gesture recognition, Biosensor 2025.
- BackgroundA. 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
- BackgroundSgambato, 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 dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing HDC-GCog in people with Healthy Volunteers, run by University Hospital, Grenoble at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 20 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Daniel ANGLADE, MD, PhD (04 38 78 17 46, danglade@chu-grenoble.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Gesture recognition rate using a device composed of 32 high-frequency surface EMG electrodes (time frame: 3 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07155460, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Jan 2026.