Fitbits have become a popular way to keep fit, and now researchers at The University of Texas at Tyler are using wearables to find new ways to save lives.

Dr. Premananda Indic, an electrical engineering professor, has assembled teams of students tasked with creating algorithms that use data from fitness trackers to predict life- threatening scenarios.

Using the same types of sensory equipment the public uses to track their steps and heartbeats, one of Indic’s teams is working to track health events among preterm infants in the Neonatal Intensive Care Unit. The team hopes to provide a proactive way to treat negative health outcomes, before they happen.

Indic said their research shows the infant’s biometrics often follow patterns before certain health events. If monitored, the changes can be caught before the infant is in danger.

Indic has partnered with Christus Trinity Mother Frances to help incorporate the technology to track risks of slowing heart rate, cessation of breathing and oxygen desaturization.

Meanwhile, another team works with the Department of Veterans Affairs to help curtail suicide among veterans. The researchers are working off of anonymous data supplied by the VA.

Indic said suicidal ideation indicators have quantifiable physical effects, which can be monitored on a fitness tracker. The results can be tracked remotely, enabling software to flag when suicidal ideation is likely to occur.

Future applications for the trackers are practically limitless. Indic said the devices could eventually track a wide variety of health issues.

“We hope that at some point we can personalize the trackers,” Indic said. “We always say prevention is better than cure.”

The next project the researchers tackle will focus on addiction, as they develop an algorithm designed to help identify urges related to the disease.

Indic said many of the symptoms already being tracked for suicidal ideation are apparent in addiction patients. Increasingly frequent, irregular spikes in physical activity are often indicators that a patient is suffering from suicidal ideation, and he said these same physical aspects are found in addiction.

“We see that a lot of the features are the same, because they are often related,” Indic said.

The researchers also believe they can pinpoint drug use by monitoring locomotor activity for less fidgeting.

At the end of the day, though, Indic said the work is about finding new ways to save lives.

“A machine learning algorithm can be embedded in the wearable devices that can instantaneously track the features specific to suicidal ideation or addiction in the activity data, which can then alert a clinician or a caregiver through a smartphone or a wireless network if these features indicate a life- threatening condition,” he said.

The work of the researchers is being recognized all over the world. In September, one of Indic’s students, Apurupa Amperayani, was invited to present her research at the Computing in Cardiology Conference in Rennes, France.

Amperayani said it was validating to see research making a good impact, especially since she will be working on it for the next five years.