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 specic 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 classication 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 dierent hand gestures and 95.3% accuracy of on-wrist localization.