TapWristband: A Wearable Keypad System Based on Wrist Vibration Sensing

Abstract

Fine-grained human motion detection has become increasingly important with the growing popularity of human computer interaction (HCI). However, traditional gesture-based HCI systems often require the design of new operation modes rather than conforming to user habits, thus increasing system learning costs. In this paper, we present TapWristband, a novel wearable sensor-based vibration sensing system that detects finger tapping by measuring wrist vibrations. We first perform real-world experiments to collect measurements for modeling the effects of the tapping motion on wearable wristband sensors including piezoelectric transducer (PZT) and inertial measurement unit (IMU). We find that a damped vibration model can be used to represent the relaxing phase of a vibration response due to tapping motion. Thus, we propose a mutual cross-correlation-based event segmentation algorithm to extract the vibration signal during the relaxing phase. After that, we develop feature extraction and classification algorithms to recognize the tapping patterns of five fingers across twelve key locations of a keypad system. Finally, we performed extensive experiments with thirteen participants to evaluate our system. Experimental results show that our low-cost vibration sensing system can achieve an average accuracy of over 93% with a tapping speed of over 100 taps per minute in real-world tapping scenarios.