The emergence of wearable devices has brought great simplicity and convenience to people's daily lives. However, due to the small form-factor, low-profile hardware interfaces, the input scheme for such wearable devices becomes a bottleneck and even sabotages their functionalities. The state-of-the-art interaction schemes, including voice input, inertial measurement unit (IMU)based input, or acoustic based input, all require a stable environment, which is critical for wearable device. To break this stalemate, we propose a stable QWERTY keyboard input for wearable devices based on bone-conduction models. Using the characteristics of human anatomy, we achieve a low-cost and high precision text input system, named Osteoacusis input (Oinput), with the help of human bones. To be specific, we first investigate a new set of bone-conduction theories. Through this set of theories, we combine a strong anti-noise cyclic neural network to achieve a high-precision QWERTY keyboard recognition for text input. Furthermore, in order to improve the user experience, we leverage slightly keyboard layout changing, dimensionality and feature selection to reduce the power consumption while preserving the convenience and stability. We have conducted experiments on 30 volunteers. The results show that Oinput has superior robustness with a high recognition accuracy of 93.3% in average. Moreover, Oinput's calibration mechanism increases the accuracy by more than 99%.