A new deep-learning model, WearNet, developed by IEEE Fellow Chenyang Lu and collaborators, may offer a solution to the widespread underdiagnosis and undertreatment of depression and anxiety. WearNet utilizes data collected from wearables, focusing primarily on daily step count, to identify patterns associated with these mental health disorders. Jessica Reyes and Patricia Wu break down the story with Dr. Gwilym Roddick.
Diverse data collection
Leveraging a vast and diverse dataset from the National Institutes of Health’s All of Us program, WearNet’s strength lies in its ability to learn complex patterns and make predictions applicable to diverse populations. This addresses a major concern of generalizability often associated with machine learning models trained on smaller datasets.
AI transforming the mental health industry
The integration of artificial intelligence technologies, such as transformer encoders and convolutional neural networks, enables WearNet to decipher intricate associations between activity data and mental health conditions.
While promising, the research emphasizes the importance of privacy and ethical considerations in implementing wearable technology for mental health support. Individuals should have control over their data and any resulting predictions or analyses.
The future of AI aides and mental health
Future research aims to build an end-to-end infrastructure for detection, test WearNet’s capabilities in prospective clinical trials, and develop timely interventions based on wearable screenings.
To learn more about Dr. Gwilym Roddick, check out his LinkedIn.
