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.

The Use of Humor in Therapy : Research-Based Training Recommendations

Despite its essential and universal nature, humor has historically received limited attention from the behavioral sciences, particularly as compared to other affective experiences like anger and sadness. Some authors (e.g., Bell & Malhi, 2009; Provine, 2000a; Roeckelein, 2002) suggest that this is because researchers have traditionally failed to "take humor seriously" and, according to O'Connell (cited in Roeckelein, 2002), have too often pursued its study in a piecemeal manner lacking scientific rigor, resulting in "no comprehensive network of facts about the development and purposes of humor in human existence" (p. 1). Roeckelein (2002) found not a single mention of humor, laughter, wit, comedy, or theories relating to these topics in introductory psychology textbooks published between 1930 and 1996.While research interest in the area has grown, especially over the last decade, it remains an elusive and nebulous topic, more likely to be examined in specialty psychology texts (e.g., social psychology and child development) than general ones (Martin, 2007; Roeckelein, 2002). Organizations (e.g., The International Society for Humor Studies; The Association for the Advancement of Therapeutic Humor), journals (e.g., Humor: International Journal of Humor Research) and internet phenomena such as "The Humor Project" (www.humorproiect.com) have made great strides in integrating information about humor from discreet fields such as the arts and humanities, biological and social sciences, education, and business management. Still, the therapeutic potential of humor remains a relatively young subject of serious scientific inquiry (Marci, Moran, & Orr, 2004; Sala, Krupat, & Roter, 2002). While humor does make appearances in self-help books and publications addressing clinical applications, these sources are much ...

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.