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I think you could use a Support Vector Machine (SVM). It can easily handle 9 dimensions. Train your SVM with positive and negative samples (stressed / not stressed). You can then classify your live data every second, it's very fast. By averaging the results over time you should be able to filter out false classifications. But that depends of course on the quality of your data.