From The Theme
HUMAN MACHINE INTERACTION AND SENSING

WHAT IF?
What if we could develop a Smart Home Care Network that would enable the elderly to live independently while enjoying the assurance of timely access to caregivers?

WHAT WE SET OUT TO DO
We set out to develop a self-organizing, distributed network of image sensors that would improve smart homes and home care. Sensors would monitor the status and body gesture of the persons under care for detection of abnormality.

WHAT WE FOUND
Our research led to the development of a layered and collaborative architecture for gesture recognition in a multi-camera network. Our approach is based on a fusion between data from a single camera, and collaborative decisions among multiple networked cameras.

LEARN MORE
Aghajan, H., Wu, C. “Layered and Collaborative Gesture Analysis in Multi-Camera Networks” in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, Vol. IV, pp 1377-1380

PEOPLE BEHIND THE PROJECT
Andrea Goldsmith is the Stephen Harris professor in the School of Engineering and professor of Electrical Engineering at Stanford University. Her research goal is to develop novel techniques, protocols, and designs for future wireless systems and networks. Her specific research areas include the design and capacity analysis of wireless systems and networks, multiple-antenna wireless networks, cognitive radios, sensor and networks, cross-layer wireless network design, and applications of communications and signal processing to health and neuroscience.

Hamid Aghajan is director of Stanford’s Wireless Sensor Networks Lab and A I R (Ambient Intelligence Research) Lab, which he established in 2003 and 2009, respectively. Focus of research in his group is on methods and applications of Ambient Intelligence with an emphasis on behavior modeling based on activity monitoring. Specific research topics include adaptive automation in smart homes, detection of anomaly or shift in behavior in elderly care, improving user’s well-being in home and office through personalized recommendations, occupancy modeling of smart buildings for resource efficiency, and avatar-based social interactions.