The Challenge with Traditional Monitoring
Traditional long-term, passive monitoring often requires expensive hardware or dedicated phone apps, leading to significant challenges.
Cost & Compliance
Specialized hardware is costly and suffers from patient compliance and adherence issues.
Battery Drain
Dedicated phone apps can cause significant battery drain, leading users to disable them.
Compatibility Issues
Apps face compatibility problems across different operating systems and device updates.
Internet Traffic as Health Sensors
The RouterSense software runs on any Internet-connected commodity computers at home, e.g, patient's old PC/Mac or a Raspberrypi that researchers ship to the patient. Or as a VPN peer to track mobile devices outside home.
RouterSense continuously captures patient's Internet activities across devices at home and beyond, while inferring health conditions in the background.
- Always-on, Set-and-Forget: Seamlessly integrates into daily life without requiring user interaction
- Hardware-Free & Low-Cost: Leverages existing Internet infrastructure at home and/or on mobile devices; no need for new devices or network reconfiguration
- Device Agnostic: Works automatically across all phones, tablets, computers, game consoles, TVs, and IoT devices
Unlock Powerful Health Insights
By analyzing internet traffic patterns, RouterSense can track a wide range of behavioral markers and anomalies over long periods, including but not limited to:
Sleep Patterns
Monitor sleep and wake cycles based on device activity during nighttime hours. Identify screen usage as potential disruptions to sleep.
Location & Mobility
Understand patterns of being at home versus away through network connection changes.
Screen Usage
Quantify screen time across various devices and applications without invasive software, including phones, tablets, computers, TVs, game consoles, etc.
Social Habits
Analyze usage of social media and communication apps to gauge social engagement.
Example Data
This visualization showcases data from two participants in our 14-day pilot study with 27 NYU students. Each ring represents a full day of passively monitored internet activity from their mobile phones. The dots illustrate 10-minute intervals of internet usage, with red indicating high activity and blue indicating low activity. Notice the clear difference in patterns: Participant A demonstrates a consistent daily routine, including a regular sleep schedule (the prominent blue region at night), while Participant B's activity is more varied.
Meet Our Team
Rameen Mahmood - PhD student - New York University
Andrew Quijano - PhD student - New York University
Danny Y. Huang, PhD - Assistant Professor, Principal Investigator - New York University - Center for Cyber Security
Chenxi Qiu, PhD - Assistant Professor - Harvard Medical School / Beth Israel Deaconess Medical Center - Neurology
Existing Collaborators
Jeffrey Kaye, MD and Zachary Beattie, PhD - Oregon Health and Science University - Neurology
Chao-Yi Wu, PhD, OT - Harvard Medical School / Mass General Research Institute - Neurology
JR Rizzo, MD - NYU Langone - Ophthalmology
Catherine Hartley, PhD - NYU - Psychology
Andrew Kiselica, PhD - University of Georgia - Neurology
Ching Hui Sia, MD - National University Heart Centre Singapore - Cardiology
Interested in collaborating or learning more?
Contact Us