Summary: Researchers are developing AI-driven smartphone applications to detect signs of depression non-invasively.
One system, PupilSense, monitors pupillary reflexes to identify potential depressive episodes with 76% accuracy. Another tool, FacePsy, analyzes facial expressions and head movements to detect subtle mood shifts, with unexpected findings like increased smiling potentially linked to depression.
These tools offer a privacy-protective, accessible way to identify depression early, leveraging everyday smartphone use.
Key Facts:
- PupilSense uses eye measurements to detect depression with 76% accuracy.
- FacePsy analyzes facial expressions and head movements to detect mood changes.
- These AI tools run in the background, offering a non-invasive depression detection method.
Source: Stevens Institute of Technology
It has been estimated that nearly 300 million people, or about 4% of the global population, are afflicted by some form of depression. But detecting it can be difficult, particularly when those affected don’t (or won’t) report negative feelings to friends, family or clinicians.
Now Stevens professor Sang Won Bae is working on several AI-powered smartphone applications and systems that could non-invasively warn us, and others, that we may be becoming depressed.
“Depression is a major challenge,” says Bae. “We want to help.”
“And since most people in the world today use smartphones daily, this could be a useful detection tool that’s already built and ready to be used.”
Snapshot images of the eyes, revealing mood
One system Bae is developing with Stevens doctoral candidate Rahul Islam, called PupilSense, works by constantly taking snapshots and measurements of a smartphone user’s pupils.
“Previous research over the past three decades has repeatedly demonstrated how pupillary reflexes and responses can be correlated to depressive episodes,” she explains.
The system accurately calculate pupils’ diameters, as comparing to the surrounding irises of the eyes, from 10-second “burst” photo streams captured while users are opening their phones or accessing certain social media and other apps.
In one early test of the system with 25 volunteers over a four-week period, the system — embedded on those volunteers’ smartphones — analyzed approximately 16,000 interactions with phones once pupil-image data were collected. After teaching an AI to differentiate between “normal” responses and abnormal ones, Bae and Islam processed the photo data and compared it with the volunteers’ self-reported moods.
The best iteration of PupilSense — one known as TSF, which uses only selected, high-quality data points — proved 76% accurate at flagging times when people did indeed feel depressed. That’s better than the best smartphone-based system currently being developed and tested for detection depression, a platform known as AWARE.
“We will continue to develop this technology now that the concept has been proven,” adds Bae, who previously developed smartphone-based systems to predict binge drinking and cannabis use.
The system was first unveiled at the International Conference on Activity and Behavior Computing in Japan in late spring, and the system is now available open-source on the GitHub platform.
Facial expressions also tip depression’s hand
Bae and Islam are also developing a second system known as FacePsy that powerfully parses facial expressions for insight into our moods.
“A growing body of psychological studies suggest that depression is characterized by nonverbal signals such as facial muscle movements and head gestures,” Bae points out.
FacePsy runs in the background of a phone, taking facial snapshots whenever a phone is opened or commonly used applications are opened. (Importantly, it deletes the facial images themselves almost immediately after analysis, protecting users’ privacy.)
“We didn’t know exactly which facial gestures or eye movements would correspond with self-reported depression when we started out,” Bae explains. “Some of them were expected, and some of them were surprising.”
Increased smiling, for instance, appeared in the pilot study to correlate not with happiness but with potential signs of a depressed mood and affect.
“This could be a coping mechanism, for instance people putting on a ‘brave face’ for themselves and for others when they are actually feeling down,” says Bae. “Or it could be an artifact of the study. More research is needed.”
Other apparent signals of depression revealed in the early data included fewer facial movements during the morning hours and certain very specific eye- and head-movement patterns. (Yawing, or side-to-side, movements of the head during the morning seemed to be strongly linked to increased depressive symptoms, for instance.)
Interestingly, a higher detection of the eyes being more open during the morning and evening was associated with potential depression, too — suggesting outward expressions of alertness or happiness can sometimes mask depressive feelings beneath.
“Other systems using AI to detect depression require the wearing of a device, or even multiple devices,” Bae concludes. “We think this FacePsy pilot study is a great first step toward a compact, inexpensive, easy-to-use diagnostic tool.”
The FacePsy pilot study’s findings will be presented at the ACM International Conference on Mobile Human-Computer Interaction (MobileHCI) in Australia in early October.
About this artificial intelligence and depression research news
Author: Kara Panzer
Source: Stevens Institute of Technology
Contact: Kara Panzer – Stevens Institute of Technology
Image: The image is credited to Neuroscience News
Original Research: Open access.
“FacePsy: An Open-Source Affective Mobile Sensing System – Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings” by Sang Won Bae et al. Proceedings of the ACM on Human-Computer Interaction
Abstract
FacePsy: An Open-Source Affective Mobile Sensing System – Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings
Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring.
While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored due to the difficulties in developing efficient mobile systems.
In this study, we aim to introduce FacePsy, an open-source mobile sensing system designed to capture affective inferences by analyzing sophisticated features and generating real-time data on facial behavior landmarks, eye movements, and head gestures – all within the naturalistic context of smartphone usage with 25 participants.
Through rigorous development, testing, and optimization, we identified eye-open states, head gestures, smile expressions, and specific Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive episodes (AUROC=81%).
Our regression model predicting PHQ-9 scores achieved moderate accuracy, with a Mean Absolute Error of 3.08.
Our findings offer valuable insights and implications for enhancing deployable and usable mobile affective sensing systems, ultimately improving mental health monitoring, prediction, and just-in-time adaptive interventions for researchers and developers in healthcare.
