Understanding Mastectomy from a Self Psychology Perspective: The Breast as a Self Object

For many women, if not all, breasts are an important component of bodyself-image; a woman may love them or dislike them, but she is rarely neutral" (Young, 2003, p.152). Breast cancer may be one of the oldest forms of cancer known to humans (American Cancer Society, 2010), and in 2008 in the United States over 182,000 women and almost 2,000 men were diagnosed with some form of breast cancer (American Cancer Society, 2008). In that same year 40,480 women and 450 men died from the disease. While any type of cancer diagnosis can instill a fear of mortality and incapacitation in the recipient, breast cancer holds a special meaning for women because of the significance placed on the breast both personally and societally. Removal of the breast tissue and muscle, or mastectomy, remains one of the primary forms of treatment for this disease. The breast plays an important role in a woman's identity, and the loss of one or both breasts due to breast cancer can have a monumental impact on her sense of self. A mastectomy affectsnot only a woman's relationship with herself, but with her family, friends, and society. It changes her outlook on life, her perception of her roles in the world, and her interest in interacting with others. Exploring these issues is important to understanding how doctors, nurses, mental health professionals, family members and support networks can best assist patients in coping with their illness. This paper attempts to understand the psychological issues and injuriesassociated with mastectomy through the lens of Self Psychology. It postulates that the breast itself is a selfobject for most women, and that its loss results in the fragmentation of the self. I will focus particularly on women between the ages of 25 and 40 years of age, in the marital and parental phases of developmental (Wolf, 1988), as the effect of a mastectomy on body image, sexuality, and genderbased roles such as motherhood has been shown to differ according to the age of the patient, with younger patients experiencing more distress (Ashing-Giwa et al, 2004).

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.