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  • New work by researchers at CMU and the Indian Institute of Technology (IIT) Gandhinagar shows that adding an inexpensive thermal camera to wearable devices could substantially improve how accurately they estimate calories burned.

    Thermal Camera Senses Breathing To Improve Exercise Calorie Estimates

    NEWS

    Any fitness buff will tell you that the estimates of calories burned made by smartphones, smartwatches and other wearable devices vary wildly. That's because these devices lack the sensors...

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    What Makes Digital Support Effective?

    PROJECT

    Online mental health support communities have grown in recent years for providing accessible mental and emotional health support through volunteer counsel...

  • matching for peer support - an illustration of 7 different silhouette images with dashed lines to visualize potential interconnectedness

    Matching for Peer Support

    PROJECT

    Online mental health communities (OMHCs) have emerged in recent years as an effective and accessible way to obtain peer support, filling crucial gaps of t...

  • Smartwatch sensor technology developed by CMU researchers could help doctors make more accurate diagnoses of attention deficit hyperactivity disorder in children.

    Smartwatches Could One Day Help Diagnose ADHD in Children

    NEWS

    Smartwatch sensor technology developed by Carnegie Mellon University researchers could help doctors make more accurate diagnoses of attention deficit hyperactivity disorder (ADHD)....

  • We built a visualization dashboard called Rx RiskMap that communicates and explains the results of a model to predict overdose risk across the state of Pennsylvania. The data shown is outdated and depicted for demonstration purposes only.

    Predicting and Visualizing Overdose Risk for Public Health

    PROJECT

    Overdose due to opioid misuse and abuse is currently a critical public health issue in the United States and worldwide. Machine learning (ML) approaches h...

  • New CMU-led research has developed a model that can predict how stay-at-home orders affect the mental health of people with chronic neurological disorders.

    Machine Learning Model Predicts Health Conditions of People With MS During Stay-at-Home Periods

    NEWS

    Research led by Carnegie Mellon University has developed a model that can accurately predict how stay-at-home orders like those put in place during the COVID-19 pandemic affect the mental ...

  • splash screen for Bloomwood Stories: Block Party

    Bloomwood Stories

    PROJECT

    Bloomwood Stories: Block Party is a visual novel that aims to increase players' health self-efficacy and connect them to health resources at their local l...

  • screenshot of the Frolic app in the App Store

    Hammer and Kalarchian Win Shape of Health Competition

    NEWS

    A new game developed by Jessica Hammer and Melissa Kalarchian, two Pittsburgh-based researchers, won first prize in a national competition sponsored by the United States Department of Heal...

  • sensing relationships icon: a continuous loop between a person and their friends with a mobile phone in the middle

    Understanding Mental Health with Mobile Sensing

    PROJECT

    Understanding how our family and friends affect our mental health People are social beings in nature and our friends and family play a crucial part in ou...

  • Audio Emotion Recognition

    PROJECT

    We have built an emotion recognition system based on prosodic features (i.e. intensity, pitch, formant frequencies of sounds) combined with short-term per...

  • Virtual Rehabilitation Assistants (VRAs)

    PROJECT

    VRAs are applications of wearable devices which track patient home exercises in order to quantify exercise metrics regarding compliance, performance, and ...

  • Virtual Coach for Stroke Rehabilitation Therapy

    PROJECT

    This Virtual Coach evaluates and offers corrections and feedback for rehabilitation of stroke survivors. The Virtual Coach is composed of a tablet for cli...