Motion sensing, which studies the changes of people's location and posture, has been applied for various applications, such as location-based services, household appliance control, and motion sensing games. Motion sensing systems can be classified into two categories for sensing purposes: location-related and posture-related. Location-related motion sensing systems detect the instantaneous location or the moving trajectories of human, while posture-related motion sensing approaches sense the posture variation and the human movements. Various techniques have been utilized to achieve motion sensing among which WiFi begins to receive more attention from academia and industry in the past decades due to the wide deployment and low cost of WiFi infrastructure. WiFi has been first ado...[ Read more ]

Motion sensing, which studies the changes of people's location and posture, has been applied for various applications, such as location-based services, household appliance control, and motion sensing games. Motion sensing systems can be classified into two categories for sensing purposes: location-related and posture-related. Location-related motion sensing systems detect the instantaneous location or the moving trajectories of human, while posture-related motion sensing approaches sense the posture variation and the human movements. Various techniques have been utilized to achieve motion sensing among which WiFi begins to receive more attention from academia and industry in the past decades due to the wide deployment and low cost of WiFi infrastructure. WiFi has been first adopted for indoor location-based services and then widely applied for human activity and gesture recognition. In this thesis, we follow this line of research and propose three WiFi-based motion sensing systems to enhance location-related and posture-related motion sensing.

We take advantage of the WiFi physical layer channel state information and propose motion sensing systems for both sensing purposes. For location-related motion sensing, we propose WiShape which can sense the shape of the moving trajectory. WiShape studies the WiFi signal variation pattern of different trajectory shapes and shows a potential in corner shape detection. Then, we pay more attention to posture-related motion sensing and propose two device-free passive sensing systems, namely WiFall and WiWrite. WiFall establishes the relationship between WiFi signal variation and human activities to achieve precise fall detection. Our improvement of WiFall allows it to directly classify other daily activities indoors. We then study human limb posture and propose WiWrite, a device-free finger writing system. WiWrite detects the basic strokes decomposed from upper-case English letters by analyzing WiFi signal variation, and constructs English alphabets and words based on the strokes to achieve text entry.