Browsing by Author "Li Kam Wa, Robert"

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    MoodScope: Building a Mood Sensor from Smartphone Usage Patterns
    (2012-09-05) Li Kam Wa, Robert; Zhong, Lin; Sabharwal, Ashutosh; Subramanian, Devika
    MoodScope is a first-of-its-kind smartphone software system that learns the mood of its user based on how the smartphone is used. While commonly available sensors on smartphones measure physical properties, MoodScope is a sensor that measures an important mental state of the user and brings mood as an important context into context-aware computing. We design MoodScope using a formative study with 32 participants and collect mood journals and usage data from them over two months. Through the study, we find that by analyzing communication history and application usage patterns, we can statistically infer a user’s daily mood average with 93% accuracy after a two-month training period. To a lesser extent, we can also estimate Sudden Mood Change events with reasonable accuracy (74%). Motivated by these results, we build a service, MoodScope, which analyzes usage history to act as a sensor of the user’s mood. We provide a MoodScope API for developers to use our system to create mood-enabled applications and create and deploy sample applications.
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    Rethinking the Vision Sensing Pipeline for Energy Efficiency
    (2016-08-02) Li Kam Wa, Robert; Zhong , Lin
    The future of computing is in allowing our devices to see what we see: Continuous mobile vision. Wearable systems will continuously interpret vision data for real-time analytics for rich context-awareness. Unfortunately, today’s system software and imaging hardware are ill-suited for this goal of “continuous mobile vision.” Current systems -- highly optimized for photography -- fail to achieve sufficient energy efficiency for the minuscule energy capacity requirements of wearable batteries. This thesis provides a rethinking of the vision system stack that includes application frameworks, operating system and sensor hardware to improve efficiency by two orders of magnitude. This cross-layer rethinking contributes: (1) a split-process application framework that eliminates redundancy in data movement and processing across multiple concurrent applications, (2) operating system optimizations for energy proportional image capture, and (3) a mixed-signal image sensor architecture that processes data in the analog domain to eliminate the efficiency bottleneck of analog-digital conversion. By exploiting the hardware/software boundary for improved energy efficiency, this thesis opens the door to integrate our devices with our real-world environments and ultimately, our own lives.