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."
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.
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."
Rahul Islam, Sang Won Bae.
FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings.
Proc. ACM Hum.-Comput. Interact. 8, MHCI, Article 260 (September 2024). doi: 10.1145/3676505