CapBand: Battery-free Successive Capacitance Sensing Wristband For Hand Gesture Recognition

Authors:
Hoang Truong University of Colorado Boulder
Shuo Zhang University of Colorado Boulder
Ufuk Muncuk Northeastern University
Phuc Nguyen University of Colorado Boulder
Nam Bui University of Colorado Boulder
Anh Nguyen University of Colorado Boulder
Qin Lv University of Colorado Boulder
Kaushik Chowdhury Northeastern University
Thang Dinh Virginia Commonwealth University
Tam Vu University of Colorado Boulder

Introduction:

the authors present CapBand, a battery-free hand gesture recognition wearable in the form of a wristband. We present CapBand, a battery-free hand gesture recognition wearable in the form of a wristband.We present successive capacitance sensing, an ultra-low power sensing technique, to capture small skin deformations due to muscle and tendon movements on the user’s wrist, which corresponds to speci￿c groups of wrist muscles representing the gestures being performed.We build a wrist muscles-to-gesture model, based on which we develop a hand gesture classi￿cation method using both motion and static features.We prototype CapBand with a custom-designed capacitance sensor array on two ￿exible circuits driven by a custom-built electronic board, a heterogeneous material-made, deformable silicone band, and a custom-built energy harvesting and management module.Evaluations on 20 subjects show 95.0% accuracy of gesture recognition when recognizing 15 di￿erent hand gestures and 95.3% accuracy of on-wrist localization.

Abstract:

We present CapBand, a battery-free hand gesture recognition wearable in the form of a wristband. The key challenges in creating such a system are (1) to sense useful hand gestures at ultra-low power so that the device can be powered by the limited energy harvestable from the surrounding environment and (2) to make the system work reliably without requiring training every time a user puts on the wristband. We present successive capacitance sensing, an ultra-low power sensing technique, to capture small skin deformations due to muscle and tendon movements on the user’s wrist, which corresponds to speci￿c groups of wrist muscles representing the gestures being performed.We build a wrist muscles-to-gesture model, based on which we develop a hand gesture classi￿cation method using both motion and static features. To eliminate the need for per-usage training, we propose a kernel-based on-wrist localization technique to detect the CapBand’s position on the user’s wrist. We prototype CapBand with a custom-designed capacitance sensor array on two ￿exible circuits driven by a custom-built electronic board, a heterogeneous material-made, deformable silicone band, and a custom-built energy harvesting and management module. Evaluations on 20 subjects show 95.0% accuracy of gesture recognition when recognizing 15 di￿erent hand gestures and 95.3% accuracy of on-wrist localization.

You may want to know: