Parkinson’s Disease Management App

Optimizing Parkinson’s Disease medications to minimize symptoms is a difficult process because it typically relies on brief assessments during widely spaced doctor’s office visits. The goal of this application is to allow patients to perform quantitative self-assessment of their symptoms and to correlate these symptoms with medication regimen and adherence. The project also empowers patients with a game for motor training with biofeedback that aims to produce additional improvements in their symptoms.

This project will be built on the CollaboRhythm platform to leverage its medication tracking and visualization tools for correlating medication adherence with other measurements, in this case including the quantitative self-assessments and parameters from the motor training tasks.

The specific technical goals of the project are:

  1. Design and implement a piece of hardware or an app using cell phone sensors to quantify commonly used finger-tapping tests that are used in the evaluation of bradykinesia.
  2. Implement software algorithms for analyzing the data from the bradykinesia algorithm in order to produce a bradykinesia score.
  3. Integrate the bradykinesia scores into CollaboRhythm so that they can be correlated with medication regimen and medication adherence.
  4. Design and implement a biofeedback game with portable EEG for motor training.
  5. Integrate the results for the motor training game into CollaboRhythm so that they can be correlated with medication regimen and bradykinesia scores.
  6. Design and implement a fall risk assessment tool.
  7. Integrate the results from the fall risk assessment tool into CollaboRhythm.
  8. Create fall risk minimization exercises for patients to conduct to reduce fall risk.

Registered Participants:

  • Team from Greece with expertise in sensor-based quantification of Parkinson's Disease motor symptoms including a hardware hacker and two experts in sensor signal analysis
  • Team of two hackers from Austria with extensive experience in telemedicine and healthcare technology and a particular interest in elderly monitoring and fall prevention
  • A researcher with extensive experience in Parkinson's Disease research involving voice analysis
  • A graduate student with experience in biomedical signal analysis and healthcare applications
  • An undergrad student with hardware and software prototyping experience
  • An engineer/entrepreneur with significant experience in developing healhcare hardware and software