Hello Sam, thank you for your comment. The whole project was done using the built in Classify function, which proved to produce more accurate results than Predict.

After extracting the numerical data from the Spectrogram images using the UpToMeasurements function, the propertyVector function puts it all in an organized array for each of the motion types. These arrays of data are the input for each of the corresponding classes in the Classify function. I hope this answers your question; I didn't post the whole code because there are thousands of lines of data manipulation and extraction which would make this thread lengthy and boring (there was a lot of trial and error involved in this project as well), but I believe I have posted the important parts which produced the results. Anyway, the classify function was built by trying all many combinations of properties for the ComponentMeasurements fuction, so lets say I have a list of properties that I know have produced good results call that list "properties", and I also have a list of all the properties available to the function, call that "allProperties". The property exploration algorithm I used was somewhat as follows:

Do[propertyVector[Append[properties, allProperties[[i]]]]; Training; Test; Print[allProperties[[i]]]; classifier = Classify[trainingSet, PerformanceGoal -> "Quality"]; Print[ClassifierMeasurements[classifier, testSet, "Accuracy"]], {i, 1, Length[allProperties]}]

This way I could see which properties produced a higher accuracy in the classifiers performance. Note that "Training" and "Test" are written above. Once I got a good set of properties I would rebuild the classifier.

propertyVector[properties]; Training; Test; classifier = Classify[trainingSet, PerformanceGoal -> "Quality"];

• "trainingSet" is inside "Training" *

That is how I built the classifier, please be sure to reply to this comment if you have any other questions. I would be happy to send you my data, the Classify function or one of the test notebooks where I explored the properties and the accuracy they provided in the function.

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Hello Frederick, thank you for your interest. The data I gathered was all by holding the Raspberry Pi and Arduino with my hand at chest level. The Raspberry Pi runs Mathematica and reads the data from the Arduino, since Mathematica can handle data easily, you can program it to upload to DataDrop or you could save it in a file, and later on extract the files to work on your computer. Since both the raspberry pi and the arduino are small devices they could be held by a chest band (or something like the bands that hold your phone securely attached to your arm while you go running). It can be placed anywhere on the body. I chose to place it at chest level because the data would be more universal, meaning that there would be less noise on the data due the particular way a person moves.

(In order to transfer the data and control the devices from my computer I used PuTTY to connect wirelessly to the Raspberry Pi. Also, I needed a WiFi dongle for the Raspberry)

The classify function just tells you what motion the data is showing, that is its main goal. But besides classifying the data into motion categories, the data could also be processed to estimate the energy consumed for each motion based on the intensity and frequency.

If you are interested in replicating the experiment I would be happy to send you the data I gathered, and all the specifications of the devices I used. I encourage you to get more data though, this would make the classify function more precise.

I am open to any suggestions, and if you are interested in making the image processing part of this project work, I think that would make a very interesting product. It could be a program that analyzes movement (energy consumption, velocity) and categorizes it. I can also send some of the code that I started to extract information from the images. Tell me if you are interested in this, we could keep discussing this matter as an addition to the main project.

Lastly, please do tell if you want me to explain something specific about the project. Unfortunately it is not very easy sharing all the data and visualizations through the community but I will try my best. I can also send you the classify function.

Pablo