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Pervasive sensing technologies are promising for increasing one-person households (OPH), where the sensors monitor and assist the resident to maintain healthy life rhythm. Towards the practical use, the recognition of activities of daily living (ADL) is an important step. Many studies of the ADL recognition have been conducted so far, for real-life and human-centric applications such as eldercare and healthcare. However, most existing methods have limitations in deployment cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this paper presents a new indoor ADL recognition system especially for OPH. To minimize the deployment cost as well as the intrusions to the user and the house, we exploit an IoT-based environment-sensing device, called Autonomous Sensor Box (SensorBox). Just placed in the house, SensorBox autonomously measures seven kinds of environment attributes, and uploads them to a cloud server. We apply machine-learning techniques to the collected data, and predicts seven kinds of ADLs. We conduct an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADL recognition with more than 90%, by carefully developing the features of environment attributes.

Analyzing Indoor Environment Sensing Data for Recognizing ADLs of One Person Household