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.

Development of a psychological assessment battery to measure client beliefs about the effectiveness of psychotherapy

The author attempted to develop a brief scale to measure clients' beliefs about the effectiveness of psychotherapy. The study is an early pilot study to determine if the scale can predict therapy outcomes. While the scale did differ significantly between clients who were active in therapy and those who were not, higher scores on the instrument were not indicative of greater involvement. Possibilities for future research to refine the instrument are discussed.

Opportunities and Challenges for the Widespread Implementation of Battery Powered Electric Transit Buses

The need for more sustainable public transportation choices drives innovation and provides opportunity for improvement in options. Transit buses provide many advantages for efficient transportation and electric drive vehicles are anticipated to play an increasing role in future transportation systems. A lifecycle cost analysis of battery electric transit buses indicates rate structures and demand charges do not currently have a large impact on lifecycle cost for small fleets of battery electric buses. As fleets grow, policies and rate structures will need to adjust to avoid becoming a barrier to adoption. Battery electric transit buses are now being developed which promise to address the primary issues of high life cycle cost, low reliability, range, and flexibility.