Adaptive Touch Sampling For Energy-Efficient Mobile Platforms

In today's mobile computing environments, touch display and interface is becoming a primary means to enable interactive and perceptual applications, e.g., 3D mobile gaming. To realize highly interactive and responsive applications, touch display constantly scans touch input signals at high frequency, thus wasting energy. However, it is challenging to optimize the touch scan frequency due the highly unpredictable nature of human-touch interactions. In this work, we propose an adaptive touch sampling frequency scaling algorithm based on users' touch behavior, to improve touch devices' energy efficiency while improving user experience. We implemented the proposed algorithm to demonstrate its efficacy for mobile tablet platforms, and our evaluation results show that our proposed adaptive touch scan algorithm reduces touch power consumption by up to 44% compared to the conventional fixed scan rate algorithm, while enhancing user experience when needed.