In this paper, we propose BodyBeat, a novel mobile sensing system for capturing and
recognizing a diverse range of non-speech body sounds in real-life scenarios. Non-speech …

We propose a novel mobile system, called EchoTag, that enables phones to tag and
remember indoor locations without requiring any additional sensors or pre-installed …

The use of smartphones and wearables as sensing devices has created innumerable
context inference apps including a class of workout tracking apps. Workout data generated …

Smart phones are used to collect and share personal data with untrustworthy third-party
apps, often leading to data misuse and privacy violations. Unfortunately, state-of-the-art …

Mobile apps that use sensors to monitor user behavior often employ resource heavy
inference algorithms that make computational offloading a common practice. However …

We report a 3-year city-wide study of an operational indoor sensing system based on
Bluetooth Low Energy (BLE) called aBeacon (short for a libaba Beacon). aBeacon is pilot …

ForcePhone is a novel system that enables commodity phones to recognize the force
applied to their touch screen and body. Researchers have shown the usefulness and …

Deep neural networks have been widely applied in various machine learning applications
for mobile data analytics in cloud. However, this approach introduces significant data …

Understanding human behavior in an automatic but nonintrusive manner is an important
area for various applications. This requires the collaboration of information technology with …

LibAS is a cross-platform framework to facilitate the rapid development of mobile acoustic
sensing apps. It helps developers quickly realize their ideas by using a high-level Matlab …