Medical Students' Exposure to and Attitudes about the Pharmaceutical Industry: A Systematic Review

A systematic review of published studies reveals that undergraduate medical students may experience substantial exposure to pharmaceutical marketing, and that this contact may be associated with positive attitudes about marketing

Behaviometrics for Multiple Residents in a Smart Environment

Smart homes and ambient intelligence show great promise in the fields of medical monitoring, energy efficiency and ubiquitous computing applications. Their ability to adapt and react to the people relying on them positions these systems to be invaluable tools for our aging populations. This work introduces and explores solutions for issues surrounding real world multiple inhabitant smart home situations. Dealing with multiple residents without requiring wireless tracking devices, while paying heed to privacy concerns, is a difficult proposition at best.The Center for Advanced Studies in Adaptive Systems research group has developed and tested a number of novel technologies to address the issues of multiple inhabitants within a smart home context using inexpensive, low profile, privacy sensitive sensors. These smart home implementations, when combined with artificial intelligence tools, are designed to provide localization, tracking, and identification through behaviometric approaches that are useful and deployable in real world situations. They have been evaluated using unscripted living spaces with multiple residents, and their capabilities explored as a means of benefiting other modeling tools, such as detecting the Activities of Daily Living.Given the complex nature and diverse needs of smart home technologies, the tools presented here are by no means definitive solutions to handling multiple resident smart environment situations. However, they do provide a strong working base for the continued development of smart environments with demonstrable benefits on real-world implementations

Behaviometrics for multiple residents in a smart environment

Smart homes and ambient intelligence show great promise in the fields of medical monitoring, energy efficiency and ubiquitous computing applications. Their ability to adapt and react to the people relying on them positions these systems to be invaluable tools for our aging populations. This work introduces and explores solutions for issues surrounding real world multiple inhabitant smart home situations. Dealing with multiple residents without requiring wireless tracking devices, while paying heed to privacy concerns, is a difficult proposition at best. The Center for Advanced Studies in Adaptive Systems research group has developed and tested a number of novel technologies to address the issues of multiple inhabitants within a smart home context using inexpensive, low profile, privacy sensitive sensors. These smart home implementations, when combined with artificial intelligence tools, are designed to provide localization, tracking, and identification through behaviometric approaches that are useful and deployable in real world situations. They have been evaluated using unscripted living spaces with multiple residents, and their capabilities explored as a means of benefiting other modeling tools, such as detecting the Activities of Daily Living. Given the complex nature and diverse needs of smart home technologies, the tools presented here are by no means definitive solutions to handling multiple resident smart environment situations. However, they do provide a strong working base for the continued development of smart environments with demonstrable benefits on real-world implementations

Bayesian updating for individual tracking in smart homes

School of Electrical Engineering and Computer Science, Washington State UniversityCrandall, A. S., and Cook, D. J. (2010, March 26). Bayesian updating for individual tracking in smart homes. Poster presented at the Washington State University Academic Showcase, Pullman, WA

Exploring Smart Home Sensor Placement Algorithms

With the continued success of research into using smart homes for elder care applications, there is a commiserate drive to build tools to assist in the deployment of these smart technologies in the real world. This deployment process has numerous open issues, including knowing where to place the sensors for effective monitoring in the home. Until the smart home engineering community has a strong grasp on installing sensors in any home, it will have trouble providing successful commercial offerings based upon smart home technologies

SkiMon: A Wireless Body Area Network for Monitoring Ski Flex and Motion during Skiing Sports

Monitoring and gathering data on sporting activities holds significant promise for athletes, equipment developers, and physical fitness clinicians. Wireless Body Area Networks are being used in sporting environments as a means of gathering data, providing feedback, and helping to gain understanding of athletic activities. Applying WBANs to skiing situations, which have higher vibration, velocities, and damp environments than many other sports, can open up opportunities to understand the dynamics of skiing equipment behaviors, skiing routes on mountains, and how individuals react when skiing. To support these outcomes, a prototype WBAN-style off the shelf component system called SkiMon was proposed, implemented, and tested. The SkiMon system uses inexpensive ESP8266, Raspberry Pi, and sensor devices to gather high quality motion and location tracking data on skiers in real-world skiing conditions. By using IEEE 802.11b/g/n wireless networks, SkiMon is able to sample data at a minimum of 50 Hz, which is enough to model most ski vibration behaviors. These data results are shown to reflect ground truth 3D maps and the acceleration data comports with earlier works on ski vibration testing. Overall, a WBAN-based commodity components solution shows promise as a high quality sensor platform for tracking and modeling skiing activities