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

TL;DR

This study demonstrates that thermal sensor arrays can effectively monitor and classify human daily activities, including sleep and daily movements, while preserving privacy and providing precise spatial location data.

Contribution

The paper introduces a TSA-based system for non-obtrusive, privacy-preserving human activity monitoring that distinguishes sleep from daily activities and analyzes spatial distribution.

Findings

Sleep duration averaged 9 hours/day.

Daily activity duration averaged 7 hours/day.

TSA system effectively classifies activities regardless of time of day.

Abstract

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.