Resilient Activities Tracking in a Smart Home using Ultrasonic Sensors

There have been many proposed smart home systems that utilize ultrasonic, pressure or passive infrared sensors to predict a resident's activities in a single occupant house, such as sitting, eating, cooking, walking and lying down. However, these systems require extensive training and manual labeling of the resident's activities before they can be used in the resident's house-making the systems inflexible and expensive. This paper proposes a more realistic system that relies on machine learning techniques to identify a resident's activities based on the ultrasonic sensors' readings and on/off state without the need to manually label the resident's prior activities. The system is trained on a single resident, then tested with the same occupant and with different people. Our evaluation shows that not only can our system accurately predict a resident's activities when it is trained on the resident's previous activities, but our system can also accurately predict the resident's activities even when it is trained on someone else's activities. This research enables this smart home system to be widely adopted in people's houses with minimal training.