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Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study
Background Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly largeExpand
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Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults
A mobile phone app that automatically generates personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. Expand
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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.
Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in theExpand
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Using Facebook in a Healthy Lifestyle Intervention: Feasibility and Preliminary Efficacy
The purpose of this pilot quasi-experimental study was to examine the feasibility and preliminary efficacy of using Facebook in a 10-week lifestyle intervention with Head Start caregiver–preschoolerExpand
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Mood States and Everyday Creativity: Employing an Experience Sampling Method and a Day Reconstruction Method
Investigating the mood-creativity relationship in everyday life is important for innovation promotion in organizational management. This study explores the relationship between mood states andExpand
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Health Care Provider Perceptions of Consumer-Grade Devices and Apps for Tracking Health: A Pilot Study
Background The use of Web- or mobile phone–based apps for tracking health indicators has increased greatly. However, provider perceptions of consumer-grade devices have not been widely explored.Expand
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Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study
Background Today, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited inExpand
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SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration
We present SCYLLA, an FPGA-based framework that enables QoE-aware continuous mobile vision with dynamic reconfiguration to effectively address this challenge. Expand
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Distream: scaling live video analytics with workload-adaptive distributed edge intelligence
We present Distream, a distributed live video analytics system based on the smart camera-edge cluster architecture, that is able to adapt to the workload dynamics to achieve low-latency, high-throughput, and scalable video analytics. Expand
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FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision
In this paper, we present FlexDNN, an input-adaptive DNN-based framework for efficient on-device video analytics. Expand
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