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Attribution 4.0 International
Affective Computing group, MIT Media Lab
Imagine in the future, you are excited about a big day coming up when you have an interview for your dream job opportunity. You select on y…
To facilitate real-world applications of personal sensing such as, triggering a mobile intervention or detecting early depression ons…
Large language models (LLMs) are capable of generating plausible explanations of how they arrived at an answer to a question. However, thes…
Self-supervised representation learning (SSL) powers modern AI systems and enables the learning of information rich data representations fr…
Wearable devices allow us to continuously and passively record elements of our physiology (e.g., electrodermal activity, skin temperature) …
Many digital mental health apps show promising results for stress and anxiety management, but they often fail to keep users engaged. Our pr…
People inherently use experiences of their past while imagining their future, a capability that plays a crucial role in mental health. …
A new way to help people with neurodivergence to manage emotion regulation and understand their stress response.Participants describe their…
By establishing correlations between sleep parameters and wellbeing indicators, our project hopes to further understanding of fluctuations …
For those with Autism Spectrum Disorder (ASD), the ability to understand, name, or recognize the sensations signifying different emotional …
Working with leading neuroscientist Richard Davidson and the Healthy Minds Institute, we are developing objective biomarkers to interpret a…
As of 2020, approximately 40 million adults, or 18.1% percent of the adult population, in the United States suffer from anxiety disorders, …
Computer-mediated forms of communication (i.e., social media, texting) have quickly become the predominant way that we keep in touch w…
Teddy is a cutting edge data collection platform available for both Android and iOS that lets you collect data from study participants in a…
Current methods to assess depression and select appropriate treatments have many limitations and need improvement. Diagnosis is usually bas…
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This project is supported by the Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), the National Institutes of Healt…
Try Affective Network!Emotional contagion in online social networks has been of great interest over the past years. Previous studies have m…
This project aims to improve the prediction accuracy of wellbeing (stress, mood, and health levels) using temporal machine learning models.…
Depression correlated with anxiety is one of the key factors leading to suicidal behavior, and is among the leading causes of death worldwi…
The SNAPSHOT study seeks to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques. It is an NIH-funde…