Analysis of Personal Health Monitoring Data for Physical Activity Recognition and Assessment of Energy Expenditure, Mental Load and Stress: Dissertation

Personal health monitoring refers to the long-term health monitoring that is performed in uncontrolled environments instead of a laboratory, for example, at home or by using wearable sensors. The monitoring is done by individuals alone, usually without guidance from health care professionals. Data produced by personal health monitoring (for example, actigraphy, heart rate, etc.) are currently used more in personal wellness monitoring rather than in clinical decisionmaking, because of challenges in the interpretation of the long-term and possibly unreliable data. Automatic analysis of long-term personal health monitoring data could be used for the continuous recognition of changes in individual’s behavior and health status, and to point out which everyday selections have a negative effect on health and which have a positive effect. This can not be achieved by using sparse measurements in controlled environments. In this thesis, data analysis was carried out for the recognition of physical and mental load using data from wearable sensors and other self-measurements. Large, annotated data libraries were collected in real-life or realistic laboratory conditions for the purpose of the development of practical algorithms and the identification of the most information-rich sensors and signal interpretation methods. Time and frequency domain features were computed from raw sensor data for the correlation analysis and the automatic classification of the personal health monitoring data. The decision tree, artificial neural network, K-Nearest Neighbor and a hybrid of a decision tree and artificial neural network classifiers were used. Automatic activity recognition aims at recognizing individual’s activities and postures using data from unobtrusive, wearable sensors. Similarly, the unobtrusive, wearable sensors can be used for the assessment of energy expenditure.