• Fanglin Chen

    PhD Student at Carnegie Mellon University


    Hey! I am Fanglin, a PhD Student at HCII, Carnegie Mellon University. My research focus is in Ubiquitous Computing, First-Person Perspective Sensing and Life-logging technologies. Feel free to drop me a line if you are interested in my work.
  • Experience [CV]

    Carnegie Mellon University

    PhD Student in Human-Computer Interaction
    August 2015 - Present

    Dartmouth College

    M.S. in Computer Science
    August 2012 - Jun 2015

    Nankai University

    B.E. in Communication Engineering
    August 2008 - Jun 2012
  • Publication [Google Scholar]

    StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students Using Smartphones

    The StudentLife continuous sensing app assesses the day-today and week-by-week impact of workload on stress, sleep,

    activity, mood, sociability, mental well-being and academic

    performance of a single class of 48 students across a 10 week

    term at Dartmouth College using Android phones. Results

    from the StudentLife study show a number of significant correlations between the automatic objective sensor data from

    smartphones and mental health and educational outcomes of

    the student body.

    My Smartphone Knows I am Hungry

    We use inferred behavioral data and location history on smartphones to predict if you are going to eat or not in the near future. These predictors could serve as a basis for future eating trackers that work unobtrusively in the background of your phone rather than relying on burdensome user input.

    Carsafe App: Alerting Drowsy and Distracted Drivers Using Dual Cameras on Smartphones

    We present CarSafe, a new driver safety app for Android phones that detects and alerts drivers to dangerous driving conditions and behavior. It uses computer vision and machine learning algorithms on the phone to monitor and detect whether the driver is tired or distracted using the front-facing camera while at the same time tracking road conditions using the rear-facing camera.

    Unobtrusive Sleep Monitoring Using Smartphones

    We present a radically different approach for measuring sleep duration based on a novel best effort sleep (BES) model. BES infers sleep using smartphones in a completely unobtrusive way - that is, the user is completely removed from the monitoring process and does not interact with the phone beyond normal user behavior. A sensor-based inference

    algorithm predicts sleep duration by exploiting a collection of
    soft hints that tie sleep duration to various smartphone usage
    patterns (e.g., the time and length of smartphone usage or
    recharge events) and environmental observations (e.g., prolonged silence and darkness).