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However, existing approaches either directly instrument users' hands, which can reduce tactile sensation, or are limited in the types of objects and interactions they can detect. This work introduces HandSAW, a wireless wrist-worn device incorporating a Surface Acoustic Wave (SAW) sensor with enhanced bandwidth and signal-to-noise ratio while rejecting through-air sounds. The device features a sealed mass-spring diaphragm positioned on top of the sound port of a MEMS microphone, enabling it to capture SAWs generated by objects and through touch interaction events. This custom-designed wearable platform, paired with a real-time ML pipeline, can distinguish 20 passive object events with &gt;99% per-user accuracy and a 91.6% unseen-user accuracy, as validated through a 16-participant user study. For devices that do not emit SAWs, our active tags enable HandSAW to detect those objects and transmit encoded data using ultrasonic signals. 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