sleep-paralysis-interrupt

A machine learning classifier that detects sleep paralysis.

Full report (PDF) here: https://github.com/xaviervilla/sleep-paralysis-interrupt/blob/master/CSE195_Final_Report.pdf

To do:

• Set up MATLAB 2019B
• Install Matlab Coder, DSP ToolBox, Classification Learner and other dependencies
• Use signal processing to find characteristics of accelerometer data for different activities
• Compute Mean, Standard Deviation, and Principle Component Analysis Coeeficients of accelerometer data
• Create a classifier that analyzes and labels accelerometer data based on the characteristics
• Read documentation for Matlab Coder and Classification Learner, figure out what is required for c code translation
• Convert classification learner into C
• Go back into Matlab and simulate a live stream of accelerometer data
• Get classifier to work with live stream of data (smaller bursts of samples 16, 64, 128 etc.)
• Get real-time classifier converted into C
• Compile in C for an Atmel 8 bit system
• Configure the generated C code as a library for Arduino IDE
• Get code to compile in the IDE with random sample data
• Get code to FIT on an Atmega328P... *sigh
• Remove Gyroscope from the classifier and data stream to save dynamic memory
• Remove PCA computations completely from the library to save program storage space
• Optimize classifier for new data stream without PCA and Gyroscope
• Recompile Matlab into C code after each of the above steps
• Finally load the code onto an Arduino
• Find a good library for MPU6050 accelerometer chip
• Format data in a way that allows the matlab functions to be called
• Get MPU library and custom library to both fit on an Atmega328P
• Solder up the mpu6050 and turn on an LED when walking is detected
• Turn Arduino light on when Sleep paralysis is detected
• Determine the cause of classifier only working when accelerometer is in specific orientation
• Fix code to allow for different orientations of the accelerometer
• Reduce size of code for more stability
• Record better accelerometer data for training and predicting
• Make it easier to make fundamental changes and recompile all of the code to the arduino
• Improve accuracy and reduce false alarms using some type of counter to measure confidence
• Get more capable embedded controller, perhaps arduino mega or RPi zero
• Enable low pass filtering in MPU6050 of 30 Hz and sample rate of 200hz
• Set up interrupts to communicate with sensor through SPI instead of I2C
• Recompile code with much better sample frequency for better predictions on better platform
• Design wearable for overnight testing
• Come up with way to record overnight data, or at least data without tethering to PC??
• Possibly reenable mean function if it improves accuracy
• Reinvestigate frequency domain characteristics and determine if necessary
• Disable gravity subtraction (world-frame view) for sensor data and re-train
• Investigate accuracy/memory tradeoff of KNN vs. Decision Tree models
• Impliment sliding window of samples to improve speed of response rather than descrete sample windows
• Record better quality SP data for training