Home/Multimodal Capture/Tactile Data Glove

TACTILE / ELECTROMAGNETIC TRACKING

Tactile Data Glove

Multiple configurations

Tactile mapping with electromagnetic tracking

It records not just how the hand moves, but when and where contact happens. A palm-side tactile array combined with electromagnetic fingertip poses provides multimodal research input for dexterous manipulation, teleoperation mapping and visuo-tactile fusion.

360 pointsStandard tactile points
200 HzTactile sampling
180 HzTracking output
PHYSEN AI +Tactile Data Glove, Tactile data glove appearance
Tactile data glove appearance
From a single device to a complete solution.

Data outputs, interface adaptation and delivery scope are confirmed per task.

Data CaptureMultimodal Research Platform
01

See how contact is distributed

A fabric piezoresistive array covers the main palm-side contact areas, with 360 points standard and 600 optional. Raw ADC values and point mapping are recorded; converting to force or pressure requires independent calibration.

02

Reconstruct hand pose from fingertip poses

Five electromagnetic sensors provide 6-DoF tracking of each fingertip, and an algorithm reconstructs a 25-DoF hand pose. The reconstruction is not 25 independently measured joints, nor ground-truth robot actions.

03

Connect to multimodal research

Data is accessed over USB with a licensed SDK; wireless is optional. In joint experiments with Ego, UMI or robots, calibration, cross-device timing and end-effector mapping must be validated separately.

TWO STREAMS. ONE EXPERIMENT.

Where the hand is, and where it touches.

Touch and pose are recorded separately and, after calibration and time alignment, become traceable research data.

01 / CONTACT

360-point tactile sensing

200 Hz standard · rated

Palm 240Fingers 120ADC

Preserves contact location and relative change. Single-point raw readings can only be interpreted as physical quantities under agreed conditions after dedicated calibration.

Tactile specs
02 / SPATIAL POSE

5 fingertip poses

180 Hz tracking output · rated

5 × 6DoFPosition + rotation

Electromagnetic tracking captures fingertip poses, from which an algorithm reconstructs a 25-DoF hand pose. Observations, reconstruction and robot action mapping are stored in separate layers.

Tracking limits

Joint capture plan: touch + pose + Ego video + task events. The two streams run at different sampling rates; cross-device hardware sync and robot adaptation are not built in and need project validation.

THE DETAILS MATTER

Every detail, spelled out.

Palm tactile array and fingertip electromagnetic tracking for dexterous manipulation and teleoperation research.

Standard, optional and upgrades

ItemStandardOptional / future
Tactile points360 points600 points optional; point layout confirmed separately
Tactile sampling200 Hz400 Hz is a future upgrade path, not a current delivery commitment
ConnectionWired USBOptional Bluetooth / wireless, requires receiver and battery module
PowerUSBOptional battery module; capacity and battery life confirmed per setup
AppearanceConfirmed by style and handColor, outer material and protection customizable
Base package1 glove + 1 data cableTwo-hand sets and tracking equipment listed separately

The number of gloves shown is not the standard set. Inclusion of the electromagnetic field generator / reference source, receiver, SDK license and related accessories must be confirmed separately.

Tactile array and measurement metrics

ParameterSpec / configuration
Sensing principleFlexible fabric piezoresistive tactile array
Standard point layout24 points per finger (16 fingertip + 8 mid-finger); palm 16 × 15 = 240 points; 360 points in total
CoverageMain palm-side areas, excluding the base of the four fingers; final coverage per the point-mapping table
Point densityRated 9 points/cm²
Single-point trigger / max range20 g / 1500 g
Standard sampling rate200 Hz
Raw outputPer-point ADC values / total ADC; not directly N or Pa
Repeatability / consistencyBoth ±15% FS (full scale), not ±15% of the current reading

Contact area, pre-stretch, fit tightness and ambient temperature and humidity all affect readings. Across people, days or re-wearing, record a baseline and validate calibration.

Electromagnetic tracking and pose reconstruction

ParameterSpec / configuration
Sensors5 electromagnetic tracking sensors, each providing 6-DoF tracking
Tracking output3-axis position + 3-axis rotation per fingertip; rated 180 Hz
Hand reconstructionAlgorithm-reconstructed 25-DoF pose, not 25 independently measured joints
0.2 mm figureTracking accuracy 0.2 mm / resolution <0.2 mm
Data latencyTable rates 10 ms wired / 30 ms wireless, and the text says below these values; actual latency must be stated with measurement endpoints and test conditions
Deployment and integrationTracking volume, reference frame, field generator specs and metal-interference conditions

Documented tracking figures are not treated as measured accuracy, and sensor link latency is not robot end-to-end control latency. Different sampling rates do not mean the two streams are hardware-synchronized.

Power, environment and reliability

ParameterSpec / configuration
Battery1200 mAh
Battery life / chargingRated 2.5 hours / 2 hours; load and test conditions not stated
Operating temperature-15 °C to 60 °C, rated
Operating humidity0–90% RH
Reliability>500,000 impacts at 10 kg (rated)
Protection and cleaningNo water-resistance claim for the electronics; avoid liquids, sharp objects and pulling on cables; clean with a soft dry cloth

Communication, SDK and data integration

ParameterSpec / configuration
Wired interfaceUSB communication; custom PID confirmed per project
Wireless interfaceOptional Bluetooth / wireless receiver and battery module; protocol version and compatibility to be confirmed
SDKDocumentation provides Windows / macOS / Linux SDK licenses; license scope per agreement, not open source
Custom developmentData frame protocol, point-mapping table and development versions provided as agreed
Tactile dataPer-point ADC / total ADC, tied to hand, point table and calibration version
Pose dataPosition and rotation of each fingertip; reconstructed pose and algorithm version stored separately
Recommended archiveRaw frames, device version, hand, anonymous participant ID, wearing baseline, task events, timestamps and clock mapping
Platform adaptationPairing planned with the Ego Thor series, UMI, Atlas and FMG + IMU; export format and cross-device sync error need project validation

Raw ADC, calibrated force values, estimated hand pose and mapped robot actions should be stored separately. Data alignment and platform adaptation are part of the integration scope, not a default promise of current device capability.

FROM CAPTURE TO ARCHIVE

From power-on to a complete data episode.

  1. 01

    Confirm configuration and fit

    Check left/right hand, tactile points, tracking equipment and connection; inspect cables and contact areas.

  2. 02

    Set baseline and coordinates

    After putting it on, record the pre-stretch baseline, confirm the electromagnetic reference source, coordinate frame and metal-interference conditions, and complete the calibration the task needs.

  3. 03

    Check both data streams

    Check the actual rate, dropped frames and latency of the 200 Hz tactile and 180 Hz pose outputs separately, saving raw data and versions.

  4. 04

    Joint capture and archiving

    Link to vision or real-robot data, validate time mapping, then generate contact events, reconstructed poses and training data, keeping all processing records.

CONFIGURE. VERIFY. DELIVER.

From selection to delivery,
make the configuration clear.

View solutions
  • Freeze 360 / 600 points, left/right quantities, connection method and appearance version; check the standard package per single glove.
  • Confirm the electromagnetic field generator, tracking volume, metal-environment limits, and the battery, wireless receiver and SDK license list.
  • Validate tactile repeatability under agreed loading area and calibration conditions, and clarify how the 0.2 mm and latency figures are measured.
  • After confirming the frame protocol and point mapping, complete decoding tests and accept tactile, pose, cross-device alignment and robot mapping separately.
  • Compare results with and without tactile or pose data on fixed tasks and the same training budget; no automatic improvement in robot success rate is promised.

START WITH YOUR TASK

Tell us about your research task.

From device selection to data and training workflows, we'll help you find the right place to start.

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