These finalists were invited to present in the final round of the Wireless Innovation Project™:
PathVis is a smartphone-based detection platform that is able to quantitatively measure the level of a disease pathogen in environmental and patient samples, providing health organizations with real-time and locational data on disease prevalence so that healthcare resources can be more efficiently targeted to areas of need.
Augmented Reality for Pedestrian Safety (SafetyAR)
University of Alabama at Birmingham
Child pedestrian safety represents a global public health challenge. Building from our existing technology to deliver virtual reality environments to teach safe street-crossing, we propose technological refinement and then evaluation of mobile augmented reality delivered via smartphone to teach children to cross streets in their community. Potential reach is global.
ENVenture is building an open source mobile app productivity tool called ENVision for cash-based merchants in developing countries who primarily sell fast moving consumer goods; our application enables merchants to capture inventory, input sales, and forecast orders, with real-time data visualization, running offline and on 2G networks.
Illegal wildlife trafficking is leading to the extirpation of numerous species. WIPER is a novel wireless, low-power, acoustic shot detector that can be integrated into existing location tracking collars commonly used with elephants and other mammals across Africa. WIPER can disrupt poaching by automatically notifying authorities when shots are fired.
University of California: Berkeley
We help reach the “last mile” of mobile connectivity by bringing first-time cell coverage to isolated communities with our community-owned, low-cost Community Cellular Networks.
DreamSave Innovation is a solution for the millions of unbanked people who are members of village savings groups to access digital financial services through scalable and sustainable mobile application.
Health Aware Personal Assistant
Health Aware Personal Assistant is enables users to efficiently “search” their collected mobile health data and issue “queries” on their data, similar to the way health care providers interact with patients.
SUNY University at Buffalo
ASD Scan is a mobile app for autism screening, objective and widely accessible, which captures gaze distributions using smartphone camera and biofeedback data using wearable sensors. The app then performs data analysis in the cloud server and provides a quantitative score for children at risk.