An ensemble approach to activity recognition based on binary sensor readings
Research on activity recognition provides a wide range of ubiquitous computing applications. Once activities are recognized, computers can use this information to provide people with suitable services. In the past decade, many classification algorithms have been applied to activity recognition. However, most of them were based on the use of inertial measurement sensors, such as tri-axial accelerometers and gyroscopes. There is still room for investigating the application of binary sensors in activity recognition. Simple binary sensors such as contact switches and motion detectors are commonly used in home security systems. In addition to low cost and ease of installation, the use of binary sensors is the least invasive approach from a privacy perspective. In this study, we developed an ensemble approach to human activity recognition based on binary sensor data. We verified our ensemble model with a publicly accessible activity dataset. The preliminary results showed that the proposed approach is stable and effective in recognizing activities of daily living (ADLs) in a smart home setting.
