Mobile Health

We present a novel wearable sensor system that measures an individual’s sweat level. The system consists of multiple cotton-covered conductive threads that are then braided into one sensor. The braided 3D structure allows robust perspiration level estimation despite the sensor distortion or the motion of the person.

For many people, the habit of leg shaking is an annoyance to themselves and people around them. By simply becoming aware of the motion, one is able to voluntarily stop shaking. SPINDLES is a low-power, low-latency, non-intrusive and high accuracy mobile application to detect leg shaking and notify the user in a timely manner. SPINDLES is robust to the placement of the phone and implements a signal processing pipeline that eliminates the need for training and calibration phases common in machine learning approaches.

We present a novel privacy-preserving pillbox system to help patients improve medication adherence while maintaining privacy and security. Existing smart pillbox systems have been successful in notifying, tracking and reporting medication adherence, but do not provide sufficient security and privacy for the patients’ medication adherence records. In the proposed system, we protect data that needs to be reported to the doctor by ensuring that the data can only be decrypted by the doctor, authorized by a face-to-face key exchange process through out-of-band communication channels. Our system also gives patients fine-grained control over how their information is transmitted and shared, which has been a major privacy concern for many patients.

The goal of this project is to identify and map possible breeding grounds of Zika carrying mosquitoes, using geotagged images, temperature, humidity and wind velocity information from a drone. We aim to visualize heat maps encoding the probability of a location being a potential breeding ground of Zika carrying mosquitos and make these visualizations widely available to stop the spread of the disease.