Clinical Progress and Optimization of Information Processing in Artificial Visual Prostheses.

Visual prostheses, used to assist in restoring functional vision to the visually impaired, convert captured external images into corresponding electrical stimulation patterns that are stimulated by implanted microelectrodes to induce phosphenes and eventually visual perception. Detecting and providing useful visual information to the prosthesis wearer under limited artificial vision has been an important concern in the field of visual prosthesis. Along with the development of prosthetic device design and stimulus encoding methods, researchers have explored the possibility of the application of computer vision by simulating visual perception under prosthetic vision. Effective image processing in computer vision is performed to optimize artificial visual information and improve the ability to restore various important visual functions in implant recipients, allowing them to better achieve their daily demands. This paper first reviews the recent clinical implantation of different types of visual prostheses, summarizes the artificial visual perception of implant recipients, and especially focuses on its irregularities, such as dropout and distorted phosphenes. Then, the important aspects of computer vision in the optimization of visual information processing are reviewed, and the possibilities and shortcomings of these solutions are discussed. Ultimately, the development direction and emphasis issues for improving the performance of visual prosthesis devices are summarized.

Subjective well-being of the elderly in Xi Cheng District, Beijing.

BACKGROUND: In 2010 the Beijing Municipal Government promulgated a policy aimed at improving the quality of life and subjective well-being of elderly residents that included a component focused on mental health. AIM: Identify factors associated with subjective well-being in a representative sample of elderly residents of Xi Cheng District in Beijing. METHODS: This cross-sectional study administered a self-completion survey to a stratified random sample of 2342 residents of Xi Cheng District who were 60 to 80 years of age. The level of well-being was assessed using a validated Chinese version of the Memorial University of Newfoundland Scale of Happiness (MUNSH). Detailed socioeconomic variables were obtained using a questionnaire developed by the authors. Social support, anxiety, and depression were assessed using validated Chinese versions of the Social Support Rating Scale (SSRS), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). RESULTS: Among the 2342 respondents, 1616 (69.0%) had a total MUNSH score of 32 or above, indicating a high level of happiness; 423 (18.1%) has a total SSRS score 32 or below, indicating poor social support; 201 (8.6%) had a total SDS score of 53 or above, indicating significant depression; and 126 (5.3%) had a total SAS score of 50 or above, indicating significant anxiety. In the multivariate regression analysis the self-reported level of depression was the most important factor related to well-being. Anxiety, social support, income level, the quality of family relationships, the ability to self-regulate emotions, and regular exercise were also significantly related to well-being; but gender, marital status, age and educational level were not associated with well-being. CONCLUSION: Among elderly urban residents in Beijing, self-reports of poor subjective well-being are closely associated with self-reports of depressive and anxiety symptoms and also associated with social factors such as social support, income level and family relationships. Prospective studies are needed to identify the causal relationships of these variables and, based on the findings, to develop targeted interventions aimed at improving the quality of life and well-being of elderly community members.