Carl Hartung

Evaluation of an Android-based mhealth system for population surveillance in developing countries

OBJECTIVE In parts of the developing world traditionally modeled healthcare systems do not adequately meet the needs of the populace. This can be due to imbalances in both supply and demand--there may be a lack of sufficient healthcare and the population most at need may be unable or unwilling to take advantage of it. Home-based care has emerged as a possible mechanism to bring healthcare to the populace in a cost-effective, useful manner. This study describes the development, implementation, and evaluation of a mobile device-based system to support such services. MATERIALS AND METHODS Mobile phones were utilized and a structured survey was implemented to be administered by community health workers using Open Data Kit. This system was used to support screening efforts for a population of two million persons in western Kenya. RESULTS Users of the system felt it was easy to use and facilitated their work. The system was also more cost effective than pen and paper alternatives. DISCUSSION This implementation is one of the largest applications of a system utilizing handheld devices for performing clinical care during home visits in a resource-constrained environment. Because the data were immediately available electronically, initial reports could be performed and important trends in data could thus be detected. This allowed adjustments to the programme to be made sooner than might have otherwise been possible. CONCLUSION A viable, cost-effective solution at scale has been developed and implemented for collecting electronic data during household visits in a resource-constrained setting.

BALANCE: towards a usable pervasive wellness application with accurate activity inference

Technology offers the potential to objectively monitor peoples eating and activity behaviors and encourage healthier lifestyles. BALANCE is a mobile phone-based system for long term wellness management. The BALANCE system automatically detects the users caloric expenditure via sensor data from a Mobile Sensing Platform unit worn on the hip. Users manually enter information on foods eaten via an interface on an N95 mobile phone. Initial validation experiments measuring oxygen consumption during treadmill walking and jogging show that the systems estimate of caloric output is within 87% of the actual value. Future work will refine and continue to evaluate the systems efficacy and develop more robust data input and activity inference methods.

Reminding About Tagged Objects Using Passive RFIDs

People often misplace objects they care about. We present a system that generates reminders about objects left behind by tagging those objects with passive RFID tags. Readers positioned in the environment frequented by users read tags and broadcast the tags’ IDs over a short-range wireless medium. A user’s personal server collects the read events in real-time and processes them to determine if a reminder is warranted or not. The reminders are delivered to a wristwatch-sized device through a combination of text messages and audible beeps. We believe this leads to a practical and scalable approach in terms of system architecture and user experience as well as being more amenable to maintaining user privacy than previous approaches. We present results that demonstrate that current RFID tag technology is appropriate for this application when integrated with calendar information.

Proximity interactions between wireless sensors and their application

Many applications in ubiquitous computing rely on knowing where people and objects are relative to each other. By placing small wireless sensors on people, at specific locations, and on or in a wide variety of everyday objects we can collect these proximate relationships and deduce much about a persons or an objects context. This paper investigates the practical issues of recording these proximity interactions using RF wireless sensors and explores the benefits of collecting/mining proximity data and how user context and usage habits can be inferred for use by proactive applications. We describe some of the issues we faced in collecting usable proximity data from RF wireless sensors. Specifically, we discuss some of the ranging experiments we conducted, our approach to utilizing the limited local data store, and how we implemented a low-overhead time synchronization scheme. We present initial results from one of the applications we are targeting: a proactive reminding system that informs users when they leave important items behind.

Mobile video for patient education: the midwives' perspective

The study presented in this paper demonstrates how nurse midwives used video on mobile phones to support patient education in a maternal and child health project in rural India. The main goals of the study were to understand how the technology impacted the workflow of the nurses and to assess the acceptability of the use of video during patient encounters. The study was based on interviews of the midwives, observation of patient visits, and an analysis of logs from the mobile devices. The overall results were positive; the midwives accepted use of mobile video as part of the workflow for postnatal care examinations. Using video changed the process of patient education, in some cases making it a more focused activity. The use of video also led to midwife multitasking, which was enabled by the technology. The study suggests that the midwives felt that their authority was enhanced by the use of video.

A new generation of open source data collection tools

Organizations in low income regions need tools for collecting and reporting on data. Existing paper-based approaches are often slow and incomplete when compared to data collection tools on mobile devices. In this demonstration, we present the authors Android-based system in addition to broader work by the OpenRosa Consortium - a community dedicated to building open source, standards-based tools for mobile data collection, aggregation, analysis, and reporting.