Research in wireless sensor networks has been very successful in creating academic testbeds and short term real deployments for many application areas such as home health care, saving energy in buildings, infrastructure monitoring, agriculture, and various environmental science applications. However, significant new problems arise for long term deployments in uncontrolled environments. It is also important to note that most of these applications perform activity recognition. Yet, these activity recognition solutions are not always robust enough for long term deployments. Consequently, the goal of this work is to develop robust and reliable activity recognition for in-home deployments that address the realism of long term deployments. To accomplish this goal requires new research results in: obtaining labeled ground truth for training activity recognition systems, recognizing overlapping activities, detection of activities that occur across rooms of a home, handling missing sensor events and sensor failures, addressing the issues of multiple person homes and visitors, and handling the evolution of human behaviors. These solutions must be combined in a holistic manner. In addition, the utility of activity recognition often depends on recognizing anomalies from typical human behaviors. Anomaly detection can also suffer from the realisms of long term deployments and, therefore, is also addressed. The basic research approach includes employing data mining, machine learning, and other techniques in robust ways that account for realisms in long term deployments. Demonstration of the utility of the solutions spans from lab experiments to realistic long term deployments for 9 months or longer.
The broad significance of this work occurs because developing robust activity recognition schemes for wireless sensor networks that operate for long time periods implies improvement in applications such as home health care and saving energy in homes and buildings. Home health care can save lives, provide improved life style and greater independence for the elderly and chronically ill, lower medical costs, and via longitudinal studies, increase understanding of the causes of diseases. Energy is a scarce resource and improved activity recognition can be used to perform control actions that save energy. This energy savings can save money and lower the impact of global warming.
