EITWatch: Smartwatch-Integrated Planar Electrical Impedance Tomography for Hand Gesture Recognition

A smartwatch presses its case back against your wrist all day. EITWatch turns that patch into a gesture sensor with electrical impedance tomography (EIT): tiny currents pass between electrodes while voltages are measured, and moving muscles and tendons give each gesture its own electrical signature.

Prior wrist-EIT wrapped a band around the whole wrist plus external electronics. EITWatch is the first system built around the watch’s own case back: eight electrodes in a 31 mm ring, 35 measurements at 48 Hz. Is that small patch alone enough to recognise gestures?

The device. The first wrist-EIT system built around a standard smartwatch case back, isolating the sensing to that contact patch.
The injection pattern. Fixed-source scanning varies separation and direction every frame, sampling roughly 6 to 15 mm of depth where adjacent injection only reads the shallow band.
The evidence. A 12-participant evaluation within a session, across sessions, and across people.

92.5% six-gesture recognition within a session

40 mm all sensing fits a standard watch-back footprint

8.6 h continuous sensing on a small 300 mAh battery

Sensing through the watch back

Electrical impedance tomography reads muscle motion from the skin; EITWatch asks whether a flat watch-back patch alone can carry that signal.

Diagram of electrical impedance tomography. A ring of sixteen electrodes surrounds a body cross-section; an AC signal excites one electrode pair while boundary voltages are measured, and reconstruction algorithms turn the measurements into an image of the interior. — **What EIT is.** Electrical impedance tomography senses what is happening inside the body from its surface: tiny alternating currents are passed between electrodes on the skin, and the resulting voltages are measured. The current does not travel in a straight line; it bows down into the tissue. Muscle conducts electricity noticeably better than fat or bone, and it deforms when the hand moves, so **each gesture changes the measured pattern**.

A circumferential EIT band wrapped around a cross-section of tissue with bone and fat labelled, wired to stacked circuit boards; beside it, two circular impedance maps reconstructed from the band's measurements. — **From wristband to watch back.** Prior wrist-EIT systems wrap an electrode band around the whole wrist and connect it to external electronics, so the sensing hardware is always larger than the watch itself. EITWatch asks the unanswered question: keep only the **flat patch a watch already presses against the skin**, and can that planar ring alone support gesture recognition?

Implementation

Eight electrodes under a standard watch case, measuring and classifying on the watch itself.

Eight gold-plated electrodes sit in a 31 mm ring beneath a standard 40 mm watch case. A dual-core microcontroller takes 35 measurements per frame at 48 Hz and runs the gesture model onboard.

Current between neighbouring electrodes stays shallow, so one electrode acts as a fixed source while the return steps through the other seven: wider separations reach deeper tissue.

User study

Twelve participants performed prompted gestures, verified by VR hand tracking, and returned two days later to re-don the watch. Recognition is deliberately lightweight: low-pass filtered signals, sliding windows, 175 simple features, and an ExtraTrees classifier, a randomised variant of the random forest.

The gesture set. Top rows: photographs of six static macro-gestures with the differential impedance map each one produces. Bottom rows: five dynamic micro-gestures plus relax, with blue arrows tracing each motion and the corresponding impedance maps below.

Participants performed two gesture vocabularies: six held poses and five quick, subtle micro-gestures plus a relax class. Within a session, recognition reached 92.5% for the held poses and 91.5% for the micro-gestures.

Recognition across sessions and across people remains difficult: re-donning the watch two days later dropped accuracy to 73.2% and 70.4%, and a brand-new user with no training data managed 63.1% and 55.3%. Closing that gap is the open challenge.

Next steps

Cross-session and cross-user recognition remain the bottleneck. Next, we will use representation learning and contrastive learning to learn gesture embeddings that stay stable across donning, days, and people, improving generalizability without a full retrain for every wearer.

92.5% within a session (held poses)

73.2% across sessions, two days later

63.1% across people, no user training

Team

This is research from the SPICE Lab at Northwestern University by:

Publication

EITWatch will appear at the ACM Symposium on User Interface Software and Technology (UIST ’26), November 2–5, 2026, in Detroit, Michigan. An author preprint is available here (PDF). Open hardware and firmware (schematics, Gerbers, BOM, and ESP-IDF firmware) are available on GitHub. The release was also covered by Hackster.io, CNX Software, and Borncity.

Xuanyou Liu*, Novel Alam*, and Karan Ahuja. 2026. EITWatch: Smartwatch-Integrated Planar Electrical Impedance Tomography for Hand Gesture Recognition. In Proceedings of The 39th Annual ACM Symposium on User Interface Software and Technology (UIST ’26). To appear. (*equal contribution)

@inproceedings{liu2026eitwatch,
  title={EITWatch: Smartwatch-Integrated Planar Electrical Impedance Tomography for Hand Gesture Recognition},
  author={Liu, Xuanyou and Alam, Novel and Ahuja, Karan},
  booktitle={Proceedings of the 39th Annual ACM Symposium on User Interface Software and Technology},
  year={2026},
  note={To appear}
}

Overview