Otomatik insan ruh sağlığı asistanı: Pasif sensör verisinden stres tanıma çalışması

Stress level among people is rising through years and passive sensing data from mobile phones or other ubiquitous devices have started to found its place in applications of mental health observation. With the ultimate goal of creating an automatic human mental health assistant that helps people to have a better mental condition, a step is taken by creating a stress recognition model. In previous works, the researchers have found correlations between sensor data and mental health conditions and attempted to predict the stress level of the user. Due to there is no direct link between any sensor data with mental health, Machine Learning algorithms are employed to uncover relations with multiple sensors and mental well-being. The utilized machine learning algorithms for prediction work with non-sequence data hence the researchers need to extract features that represent historical sensor data with instant features. However, extracted features cannot completely represent a sequence of time data. Within the scope of this study, we showed that LSTM, CNN and CNN-LSTM algorithms which accept sequences of data as input and reaches exceptional performances in different applications can also work in passive mobile phone sensor data to predict human mental stress. The performance of the model on StudentLife dataset which includes passive mobile sensing data of college students has 62.83% accuracy on 460 test instances by training with 800 instances with LSTM model. Diversity and size of the data are very small and the data-hungry LSTM model could not generalize on adapted features with the small sample size. Although we did not adapt complex features, the results are promising and encourage us to improve data size and continue to research on this topic.