Figure 2. Overview of the Regression and Classification Techniques.

(a) Wearable ECG and SCG (only showing one axis of the signal for simplicity) signals were synchronized with breath-by-breath data from CPX computer. R-peaks of the ECG signal were detected and the SCG signals were segmented into heartbeats using corresponding R-peaks. 10 heartbeat frames from the SCG signals were averaged to get ensemble averaged heartbeats corresponds to one VO₂ value from breath-by-breath data from CPX and features were extracted from the averaged heartbeats. The features were fed into a Random Forest regressor as estimators to estimate VO₂. Estimated VO₂ was compared to actual VO₂ to see the estimation accuracy. (b) The features from SCG and ECG were fed into a support vector machine (SVM) classifier with RBF kernel to estimate the clinical state of a subject and it was compared to the actual clinical state derived from CPX.