WiFall: Device-free fall detection by wireless networks

The world population is in the midst of a unique and irreversible process of aging. Fall, which is one of the major health threats and obstacles to independent living of elders, will aggravate the global pressure in elders´ health care and injury rescue. Thus, automatic fall detection is highly in need. Current proposed fall detection systems either need hardware installation or disrupt people´s daily life. These limitations make it hard to widely deploy fall detection systems in residential settings. In this work, we analyze the wireless signal propagation model considering human activities influence. We then propose a novel and truly unobtrusive detection method based on the advanced wireless technologies, which we call as WiFall. WiFall employs the time variability and special diversity of Channel State Information (CSI) as the indicator of human activities. As CSI is readily available in prevalent in-use wireless infrastructures, WiFall withdraws the need for hardware modification, environmental setup and worn or taken devices. We implement WiFall on laptops equipped with commercial 802.11n NICs. Two typical indoor scenarios and several layout schemes are examined. As demonstrated by the experimental results, WiFall yielded 87% detection precision with false alarm rate of 18% in average.