The participant data was then used with a set of machine algorithms that included linear regression (LR), simple linear regression (SLR), Gaussian processes (GP), epsilon-support vector regression (E-SVR) and nu-support vector regression (N-SVR). Models were trained using 10-fold cross validation.
“To be specific, we randomly selected nine-tenths of the dataset as the training data and the remaining data as the testing data, and then we repeated the process ten times for every model,” the authors wrote. “This method can avoid problems such as overfitting or selection bias to some degree.”
Overall, GP achieved the best score with a correlation of 0.78.
“The correlation coefficient value of 0.78 indicates a high positive correlation, which is relatively rare according to previous studies,” the authors explained. “This result is consistent with an earlier study showing that sleep is associated with gait and demonstrates that gait patterns can reveal sleep quality quite well. More importantly, a real-time prediction of human health status (sleep quality) scores can be implemented by using Kinect.”