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.

“Fall” and Redemption in the Thought of Martin Heidegger and Jacques Lacan

This dissertation examines and develops Martin Heidegger’s concept of “falling” as a significant historical-philosophical principle. Falling, however, is primarily understood as a concept of the early Heidegger, whereas I argue that Heidegger continues to rely upon it, both explicitly and implicitly, throughout his career. Falling is a description ofphilosophical and Western history, known as metaphysics, and the description of man’s relationship to Being. Thus, falling relates to the most significant streams in Heidegger’s later thought, too, including the truth of Being, the death of God, the gods, the overcoming of metaphysics, and meditative thinking. I then reinterpret the traditional theology of the Fall narrative from Genesis in light of falling as philosophical concept, extending Heidegger’s own “destruction” of Western metaphysics in relation to one of its grounding myths. I move on to demonstrate the significance of a falling understanding in a rereading of the death of God and the end of metaphysics by examining Heidegger’s engagement with Nietzsche. I conclude by incorporating Jacques Lacan’s psychoanalysis as a further extension of Heidegger’s discourse on falling, showing that the subject’s discourse and relationship to the truth of Being is at the core of his constitution and neurosis.