EI-Lite: Electrical Impedance Sensing for Micro-gesture Recognition and Pinch Force Estimation
Abstract
Micro-gesture recognition and fine-grain pinch press enables intuitive and discreet control of devices, offering significant potential
for enhancing human-computer interaction (HCI). In this paper,
we present EI-Lite, a lightweight wrist-worn electrical impedance
sensing device for micro-gesture recognition and continuous pinch
force estimation. We elicit an optimal and simplified device architecture through an ablation study on electrode placement with 13
users, and implement the elicited designs through 3D printing. We
capture data on 15 participants on (1) six common micro-gestures
(plus idle state) and (2) index finger pinch forces, then develop
machine learning models that interpret the impedance signals generated by these micro-gestures and pinch forces. Our system is
capable of accurate recognition of micro-gesture events (96.33%
accuracy), as well as continuously estimating the pinch force of the
index finger in physical units (Newton), with the mean-squarederror (MSE) of 0.3071 (or mean-force-variance of 0.55 Newtons)
over 15 participants. Finally, we demonstrate EI-Lite’s applicability
via three applications in AR/VR, gaming, and assistive technologies.
for enhancing human-computer interaction (HCI). In this paper,
we present EI-Lite, a lightweight wrist-worn electrical impedance
sensing device for micro-gesture recognition and continuous pinch
force estimation. We elicit an optimal and simplified device architecture through an ablation study on electrode placement with 13
users, and implement the elicited designs through 3D printing. We
capture data on 15 participants on (1) six common micro-gestures
(plus idle state) and (2) index finger pinch forces, then develop
machine learning models that interpret the impedance signals generated by these micro-gestures and pinch forces. Our system is
capable of accurate recognition of micro-gesture events (96.33%
accuracy), as well as continuously estimating the pinch force of the
index finger in physical units (Newton), with the mean-squarederror (MSE) of 0.3071 (or mean-force-variance of 0.55 Newtons)
over 15 participants. Finally, we demonstrate EI-Lite’s applicability
via three applications in AR/VR, gaming, and assistive technologies.
