Feasibility of An Online Survey Examining the Physical Activity Patterns Among South Asian Adults Residing In the United States

Background: Physical inactivity is a major risk factor for cardiovascular disease and diabetes among South Asians (SAs) - Bangladeshi, Bhutanese, Indian, Maldivian, Nepali, Pakistani, and Sri Lankan. Methods: An online survey was used to determine the feasibility of examining physical activity (PA) levels of SAs living in the US. The Social Ecological Model was the theoretical basis for identifying individual-level, social environmental, and physical environmental factors that impact PA. Results: Ethnicity, intention, self-efficacy, and perceived health benefits of PA were significantly associated with being physically active. Facilitators to PA included achieving improved health; while barriers were lack of time to exercise, unfamiliarity with PA, and nonexistent gender-specific PA facilities. Conclusions: This study showed that online surveys can be a promising tool for data collection among SAs. Health promotion programs should include education on the benefits of PA, and provide culturally sensitive facilities that support PA, especially for SA women.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.