Scrutiny of Mental Depression through Smartphone Sensors Using Machine Learning Approaches

Abstract

In addition to a variety of exceptional sensors, Smartphones now facilitates vigorous open entries in data mining and machine learning to scrutinize the Human Activity Recognition (HAR) system. The follow-up to the treatment of diseases, HAR monitoring system, can be used to recognize mental depression that until now has been overlooked for HAR applications. In this scrutinize, Smartphone sensor data were collected in the 1 Hz frequency from 20 data subjects of different ages. We drove the HAR by using basic machine learning strategies, namely Support Vector Machine, Random Forest, K-Nearest Neighbors, and Artificial Neural Network to recognize physical activities which are associated with mental depression. Random Forest outperformed to recognize daily patterns of activities with 99.80% accuracy of the validation data set. Along with, sensors data was amassed regarding the activities performed over the most recent 14 days continuously from target subjects’ Smartphone. This data was fed to the optimized Random Forest model and quantified the duration of each symptomatic activity of mental depression. Here, a push was connected to figure the risk factor for the probability that an individual has been encountering mental depression. So, a questionnaire was surveyed to collect data from 50 patients who were suffering from mental depression. The questionnaire enquires for the duration of activities related to mental depression. Then, the similarity of these experimental subjects’ activity pattern was measured with those of 50 depressed patients. Finally, data was collected from target subjects’ and applied similarity approach to induce the relation between the target subjects’ and depressed patients. Average similarity value of 90.94% for the depressing subject and 34.99% of the typical subject justifies that this robust system was able to achieve a good performance in terms of measurement of risk factors.