Energy-efficiency is one of the most important design criteria for mobile systems, such as smartphones and tablets. But current mobile systems always over-provision resources to satisfy users. The root cause is that, we have no knowledge on how much of system performance/energy will exactly satisfy users. Psychophysics defines the quantified link between physical stimuli and human-perceived stimuli. So, we will leverage psychophysics to study the quantified correlation between computer architecture resources (i.e., physical stimuli) and user satisfaction (i.e., human-perceived stimuli). We then exploit such correlation to precisely apportion resources to operate tasks and accurately satisfy users. Benefiting from our precisely-defined user satisfaction criteria and well-designed algorithms, we can reduce energy consumption of computer architectures by up to 42.9% without harming user experience. To the best of our knowledge, we for the first time theoretically and accurately model such substantial correlation. Our work opens a new research domain for fundamentally improving mobiles’ energy-efficiency.

The predominant user activities on mobile architectures (e.g., smartphones) involve entering text in instant messaging apps, short message services, and social networking services. Recent research reveals that the normal use of input methods drains approximately half of the battery capacity due to their above-average power requirements and frequent use. In this paper, we first study the power characteristics of mobile input methods and find that they consistently over-provision resources to satisfy users. For example, the psychophysical evidence available indicates the response of spell-checking features within a time threshold makes users feel they have instant feedback. However, current systems perform it very quickly, which is imperceptible to users and costly in terms of energy use. Given this over- provisioning, the system can be slowed down to save energy while retaining the feeling of instant response. Inspired by this observation, we also exploit several other psychophysical facts to identify the exact criteria to satisfy users. As a result, we present a user experience-oriented technology, utexia, to optimize the energy use of mobile input methods. The evaluation shows that utexia conserves up to 42.9% in energy use while strictly ensuring a good user experience.

Limited by size, shape, and other factors, it is rather inconvenient to interact with new smart devices by traditional methods. Acoustic-based methods following a machine learning approach have been put forward to resolve this problem in previous works. But they possess limitations of heavy training overhead, low performance for unseen users, and intensive computation cost. Following our previous work in this area, we further overcome shortcomings of existing work and propose a lightweight and zero-shot text-entry system for unseen users based on acoustic sensing. The key novelty of this work is proposing a new model training strategy including dataset construction and augmentation methods to effectively enhance generalization ability of a simple learning model with as few training data as possible, based on our insight into the problem. We design and implement a real-time Android application system called EchoWrite 2.0 to validate our idea with extensive experiments. Results show that EchoWrite 2.0 can recognize digits, English letters, and words with an accuracy of 85.3%, 73.2%, and 96.9%, respectively, for unseen users without providing any data to the learning model. The comparison with related work in different aspects shows overall superiority of EchoWrite 2.0 .

Surface identification provides contextual services during humancomputer interaction, which is important for target detection and scene understanding. A robust and ubiquitous surface recognition system has a wide range of applications such as context awareness and robot operation. Existing methods have shortcomings of requiring specialized devices and limited usage scenarios. In this paper, we introduce Surtify, a surface identification system based on acoustic dispersion with a smartphone. By combining the intrinsic physical phenomenon (i.e., acoustic dispersion) with a deep learning model, Surtify can identify eleven kinds of surfaces with accuracies up to 96%, even in cross-person and cross-location scenarios.

Strength training is becoming increasingly popular among all age groups, as it helps the participants increase muscle strength, improve body flexibility, reduce health risks, and reshape physical forms. However, strength training imposes strict regulations on gestures and requires professional instruction in real time for the sake of body-building efficiency and safety. For this purpose, in this article, we propose a novel low-cost system named iCoach, to provide real-time monitoring and coaching service for strength training participants. Specifically, we design and implement a smart fitness glove, which can be seamlessly equipped with a pervasive inertial unit. With this customized but low-cost device, we can recognize various training programs, detect nonstandard behaviors while exercising, and assess exercising qualities of a user. Our primary experimental results show that iCoach can recognize 15 sets of training programs, detect three common nonstandard behaviors, and assess the quality of training with high accuracy and reliability.