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Automatic Stroke Assessment System (AUROS)

WPF Application that capture stroke patient's motion data and score it based on Fugl-Meyer Assessment System.

Getting Started

Pull at https://github.com/rjjatson/auro.git open the solution using Visual Studio. VS 2015 is recommended.

Prerequisites

*Installed Kinect 2.0 for windows (could be replaced by dummy input)

*Composite hand glove sensor (could be replaced by dummy input)

*Presentation pointer- pgUp and pgDown emulated

*Kinect 2.0 SDK

*Internet connection

Deployment

Deploy this project on Windows 10 OS. This project consists of 3 sub systems :

*Auros - WPF : Capturing Function

*Feature Extractor - Console : Extracting features of raw data for machine learning algorithms

*Regressor Compare - Console : Fed the extracted data into Defined Azure Web Service. This service comparing performance of 5 machine learning regression algorithms : bayesian linear, linear, NN, Boosted Decision tree, decision forestgiven input in very specified format (form preproc program). ! note : feel free to consume my azure web service but transaction limit may apply.

Running

You need to check your isolated storage for each process output typically located on C:\Users<yourname\AppData\Local\IsolatedStorage

*Auros : set up the input modules : place kinect sensor infront of subject and wear the composite glove. if it's not avalabe , you may use dummy input function by clicking dummy button on upper right screen. press pgDn/ pointer presentation/ click the button on lower right screen, follow the recording instruction. the raw data wil be available on the aforementioned isoStorage. check raw data folder on isoStorage.

*Feature Extractor : make sure raw data is avaliable, run the program. check preproc folder on isoStorage.

*Regressor Compare : make sure preproc data available. it will fed the data in my azure storage to azure machine learning. you may not modify the storage. but, you could define your own data input from different storage for the ML.

Improvement

this project demonstrates the TRAINING function of azure machine learning, i will add the function of SCORING in near future. i am publishing research paper for this project, i will add the abstract, and you may ask for the full text soon.

Authors

Ricky Julianjatsono - Initial work

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