Facial Sensors Can Now Read the Pain You Hide

Pain has long been assessed using a scale of one to ten, by pointing to an expression, or by describing it to a nurse. These methods rely heavily on the individual’s ability to speak, recall, or honestly report their pain.

However, a recent study from Rutgers University has identified an unexpected second channel: a subtle signal running across the face that is too small to see with the naked eye and moves in step with the heartbeat. The signal can be captured only by cameras scanning the skin around the eyes at about 30 frames per second.

Led by Dr Elizabeth Torres and doctoral researcher Mona Elsayed of the Sensory-Motor Integration Laboratory, the team recorded 45 healthy adults as they performed short tasks. To induce sustained pain, the participants’ arm was placed in a blood-pressure cuff inflated to produce deep, painful sensations.

A simple webcam then recorded their faces, while specialised software mapped 68 points around the brows, cheeks and jaw to detect tiny movements. Simultaneously, a chest sensor tracked heart rate in real time on 21 volunteers.

The results showed that the area around the eyes served as the key indicator. When pain occurred, fluctuations in the movements around the eyebrows and eyelids spiked sharply, far more than those around the cheeks or jaw.

‘In a matter of seconds, we could see the body’s pain response reflected in small facial movements. The more irregular the heart rate, the more evident it becomes on the face,’ said Torres.

Interestingly, the link between heart rate and eye movement weakened when participants’ minds were occupied. When asked to perform a drawing task requiring high cognitive focus, the pain signal was less clearly monitored.

‘Greater cognitive load essentially shifts the pain signal,’ Torres added.

Nevertheless, the researchers note that the study, published in Frontiers in Neuroscience, is small in scale and used artificially induced, short-term pain. Further testing is needed to determine whether the same pattern applies to chronic pain such as arthritis.

While facial scanning will not replace direct communication, the technology holds great potential as a second channel for patients who have difficulty speaking, such as children, stroke survivors, or people with dementia.

Currently, Torres and colleagues are adapting the method into a mobile smartphone-based tool through Rutgers’ licensed spin-off company. The system is designed to track pain progression over time and determine whether a treatment is working effectively.