Identifying Human Indoor Daily Life Behavior employing Thermal Sensor Arrays (TSAs)

Daily activity monitoring systems used in households provide vital information for health status, particularly with aging residents. Multiple approaches have been introduced to achieve such goals, typically obtrusive and non-obtrusive. Amongst the obtrusive approaches are the wearable devices, and among the non-obtrusive approaches are the movement detection systems, including motion sensors and thermal sensor arrays (TSAs). TSA systems are advantageous when preserving a person's privacy and picking his precise spatial location. In this study, human daily living activities were monitored day and night, constructing the corresponding activity time series and spatial probability distribution and employing a TSA system. The monitored activities are classified into two categories: sleeping and daily activity. Results showed the possibility of distinguishing between classes regardless of day and night. The obtained sleep activity duration was compared with previous research using the same raw data. Results showed that the duration of sleep activity, on average, was 9 hours/day, and daily life activity was 7 hours/day. The person's spatial probability distribution was determined using the bivariate distribution for the monitored location. In conclusion, the results showed that sleeping activity was dominant. Our study showed that TSAs were the optimum choice when monitoring human activity. Our proposed approach tackled limitations encountered by previous human activity monitoring systems, such as preserving human privacy while knowing his precise spatial location.

Monitoring Indoor Activity of Daily Living Using Thermal Imaging: A Case Study

Monitoring indoor activities of daily living (ADLs) of a person is neither an easy nor an accurate process. It is subjected to dependency on sensor type, power supply stability, and connectivity stability without mentioning artifacts introduced by the person himself. Multiple challenges have to be overcome in this field, such as; monitoring the precise spatial location of the person, and estimating vital signs like an individuals average temperature. Privacy is another domain of the problem to be thought of with care. Identifying the persons posture without a camera is another challenge. Posture identification assists in the persons fall detection. Thermal imaging could be a proper solution for most of the mentioned challenges. It provides monitoring both the persons average temperature and spatial location while maintaining privacy. In this research, we propose an IoT system for monitoring an indoor ADL using thermal sensor array (TSA). Three classes of ADLs are introduced, which are daily activity, sleeping activity and no-activity respectively. Estimating person average temperature using TSAs is introduced as well in this paper. Results have shown that the three activity classes can be identified as well as the persons average temperature during day and night. The persons spatial location can be determined while his/her privacy is maintained as well.