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Unobtrusive human localization and activity recognition for supporting independent living of the elderly

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Abstract

Indoor localization and activity recognition is a fundamental research topic for a wide range of important applications such as fall detection of elderly people. It usually requires an intelligent environment to successfully infer where and what a person is doing. However, many of the existing techniques on localization and activity recognition rely heavily on people's involvement such as wearing battery-powered sensors, which might not be practical in real-world situations (e.g., people may forget to wear sensors). In this project, we propose a device-free localization and activity recognition approach using passive RFID tags. It is achieved by learning how the Received Signal Strength Indicator (RSSI) from the passive RFID tag array is distributed when a person performs different activities in different locations. After activity patterns are discovered for a particular individual, we will also develop a context-aware, common-sense based activity reasoning engine that assists applications to make appropriate interpretation of detected activities. We believe the proposed system has the potential to better support the independent living of elderly people considering the continuously increased aging population.