High prediction of left and right hand? ​

1. Using the features electrical and infrared I got an interesting result.
2. It was a very small sample so it could be anecdotal, but at 99% it is hard to ignore.
3. I added some more data and it stayed in the 99% area
4. Only works with both data features, IR and EC using time series classification.
5. Individually using IR and EC on their own give low prediction.

Vero board amplifier gives stable acurate readings. ​

No noise in the data. ​

ML algorithm comparison for identity with raw data. ​

• LR -> LogisticRegression(solver='liblinear', multi_class='ovr')
• LDA -> LinearDiscriminantAnalysis()
• KNN -> KNeighborsClassifier()
• CART -> DecisionTreeClassifier()
• NB -> GaussianNB()
• SVM -> SVC(gamma='auto')

PCB CAD 3D ​

Amplifier modified version (0.2) ​

Amplifier filter round shape ​

Amplifier filter round shape ​

Amplifier filter square shape ​

Battery holder ​

Animated GIFs ​

Electrical ​

Infrared ​

Images ​

Normalized data by time and frequency. ​

Electrical activity normalized. ​

Electrical activity normalized with FFT comparison ​

Infrared activity normalized. ​

Infrared activity normalized with FFT comparison. ​

Earlier images. ​

Low pass filter amplifier in the raw for the ECG. ​

Look for the electrical signal/pulse in the graph. ​

Links ​

• Arduino Heart Rate Monitor Using MAX30102 and Pulse Oximetry

• Interfacing GY-31 TCS3200 Color Sensor Module with Arduino

• Pulse oximetry: Understanding its basic principles facilitates appreciation of its limitations

• Interfacing MAX30102 Pulse Oximeter Heart Rate Module with Arduino

• Does Your Medical Image Classifier Know What It Doesn’t Know?

• Infrared Laser Spectroscopy At Your Fingertips

• Design and Development of a Low-Cost Arduino-Based Electrical BioImpedance Spectrometer

This site ​

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