View source for Smart App for PD

{{StudentProjectTemplate
|Summary=A smart-watch app for monitoring symptoms in Parkinson's Disease
|Programme=Information Technology; Embedded Intelligent Systems
|Keywords=Android app development, signal processing, inertial sensors, movement analysis
|References=Pérez-López, Carlos, et al. "Monitoring Motor Fluctuations in Parkinson’s Disease Using a Waist-Worn Inertial Sensor." Advances in Computational Intelligence. Springer International Publishing, 2015. 461-474.

LeMoyne, Robert, and Timothy Mastroianni. "Use of Smartphones and Portable Media Devices for Quantifying Human Movement Characteristics of Gait, Tendon Reflex Response, and Parkinson’s Disease Hand Tremor." Mobile Health Technologies: Methods and Protocols (2015): 335-358.
|Prerequisites=courses in signal analysis or image analysis, design of embedded intelligent systems, and programming are required. Previous experience with Java or app development and courses in artificial intelligence are appreciated.
|Supervisor=Anita Sant'Anna,
|Examiner=Antanas Verikas
|Level=Master
|Status=Open
}}
Research question: How to implement power efficient signal processing methods on a smart-watch for monitoring motor symptoms in Parkinson's Disease? What are the accuracy vs. power efficiency trade-offs to be considered and how to decide appropriately?

The project will review general signal analysis approaches to monitoring motor symptoms using inertial sensors, make an inventory of the requirements and characteristics of each method, select or modify a method that would be appropriate for implementation on a smart-watch, and execute the implementation and testing of the solution. 

Deliverables will include and inventory (literature review) of signal processing methods for monitoring motor symptoms in Parkinson's Disease; and a prototype implementation of the solution on a Moto 360 smart-watch. 

A scientific publication based on this project work is encouraged.