Machine Learning & Stress Prediction

Seeking a reliable way to measure when I was am in a relaxed and focussed frame of mind and when I was feeling stress I looked again at the data I had collected for several sessions. I used the data from the readings when I was getting my tooth drilled and when I was giving a speech as a reliable reading of when I was in a stress state. I also used sessions of high coherence while using Heartmath Pro as a baseline for when I was in a relaxed state. I used this as a training set which I fed into an Artificial Neural Network using a freeware package called WEKA.  

The machine learning algorithm came back with a 98.3% accuracy on the ability to classify whether I was relaxed or stressed. Now that I had an algorithm I could run it on other data to see how it looked. First I ran the data for a meeting I had with a colleague that was very positive. Running the data I was not surprised to see that I was relaxed during the majority of the session. Twice I dropped into stress in a 53 minute meeting. That was consistent with my experience. 

Slide2

Running the algorithm on a second meeting gave me a different outcome. I remember the meeting well and the conversation was not combative, but the topic was more difficult. This result was more of a stress state with a four time periods where I was in Poise. Again this was consistent with my experience of the meeting. 

Slide1

I cut the data in the training set and the meeting data lists into 30 second increments. This is the shortest period of time needed to get a meaningful set of heart rate variability data.  It also allows me to use my previous work in returning to Poise as a comparison. The next step is to run the algorithm on the data from different situations. 

2 Comments

Interesting approach! What’s the input, the frequency spectrum data?

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