Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Wrestling With Tradition: Japanese Activities at Amache, a World War II Incarceration Facility

I employ archaeological analyses, archival research, and oral histories to investigate traditional Japanese practices that were performed at Amache, a World War II Japanese American incarceration facility. I argue that these inter-generational practices helped to bridge a cultural gap that existed between several generations of Japanese Americans. For many incarcerated Japanese Americans, their first exposure to many traditional activities occurred during incarceration. The resulting social environment incorporated aspects of Japanese, Japanese American, and mainstream American influences, all of which were adapted to conditions during incarceration. Similarly, archaeological analyses allow for the investigation of traditional practice features. These provide evidence regarding the significance of the adapted landscape at Amache. Evidence of these practices suggests Amache internees had both a strong desire to maintain and celebrate these aspects of their Japanese heritage but they also incorporated non-traditional elements that reflected the unique living conditions during incarceration. Incarceration, I argue, created an environment in which a unique internee consciousness was formed in which the use of traditional practices was a focal point. The physical remains of traditional practices allow archaeologists to determine aspects of this newly formed consciousness that are not readily apparent in historical documentation.