Daily Living Activity Recognition through Time Series Analysis of Data Obtained with ECHONET Lite Appliances and Motion Sensors

Abstract

Recognition of daily living activities is important to realize advanced services that increase QoL (Quality of Life) of residents by providing context-aware appliance control, health monitoring and so on. Our previous work already achieved recognition of nine types of daily living activities with an accuracy of 68% by applying Random Forest machine learning algorithm to the data collected with ECHONET Lite appliances and motion sensors in a home. ECHONET Lite is a communication protocol for the control and the sensor network of networked appliances used in a home and has been standardized as ISO/IEC-4-3. Many appliance manufacturers are developing ECHONET Lite appliances and introducing them into the market. In addition, motion sensors are already widespread and attached to some appliances such as lighting devices and IH cooking heaters so that they are automatically turned off when no one exists nearby. In this paper, we propose a new method of daily living activity recognition by introducing time series data analysis with LSTM of Deep Learning. We collected data with ECHONET Lite appliances and motion sensors attached to the appliances over 12 days in our smart home facility where four participants spent usual daily life for three days each. As a result of time series analysis of the collected data with LSTM, we achieved the recognition accuracy of 85% for 9 different activities.