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1. DEVELOPMENT AND PILOT STUDY OF AN INTEGRATED SENSOR SYSTEM TO MEASURE FUEL CONSUMPTION AND COOKSTOVE USE

   Ventrella, Jennifer; MacCarty, Nordica (January 2018, Proceedings of the ... ASME Design Engineering Technical Conferences)

   Accurate, accessible methods for monitoring and evaluation of improved cookstoves are necessary to optimize designs, quantify impacts, and ensure programmatic success. Despite recent advances in cookstove monitoring technologies, there are no existing devices that autonomously measure fuel use in a household over time and this important metric continues to rely on in-person visits to conduct measurements by hand. To address this need, researchers at Oregon State University and Waltech Systems have developed the Fuel, Usage, and Emissions Logger (FUEL), an integrated sensor platform that quantifies fuel consumption and cookstove use by monitoring the mass of the household’s fuel supply with a load cell and the cookstove body temperature with a thermocouple. Following a proof-of-concept study of five prototypes in Honduras, a pilot study of one hundred prototypes was conducted in the Apac District of northern Uganda for one month. The results were used to evaluate user engagement with the system, verify technical performance, and develop algorithms to quantify fuel consumption and stove usage over time. Due to external hardware malfunctions, 31% of the deployed FUEL sensors did not record data. However, results from the remaining 69% of sensors indicated that 82% of households used the sensor consistently for a cumulative 2188 days. Preliminary results report an average daily fuel consumption of 6.3 ± 1.9 kg across households. Detailed analysis algorithms are still under development. With higher quality external hardware, it is expected that FUEL will perform as anticipated, providing long-term, quantitative data on cookstove adoption, fuel consumption, and emissions. 

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2. DEVELOPMENT AND PILOT STUDY OF AN INTEGRATED SENSOR SYSTEM TO MEASURE FUEL CONSUMPTION AND COOKSTOVE USE

   Ventrella, J; MacCarty, N (January 2018, Proceedings of the ... ASME Design Engineering Technical Conferences)

   Accurate, accessible methods for monitoring and evaluation of improved cookstoves are necessary to optimize designs, quantify impacts, and ensure programmatic success. Despite recent advances in cookstove monitoring technologies, there are no existing devices that autonomously measure fuel use in a household over time and this important metric continues to rely on in-person visits to conduct measurements by hand. To address this need, researchers at Oregon State University and Waltech Systems have developed the Fuel, Usage, and Emissions Logger (FUEL), an integrated sensor platform that quantifies fuel consumption and cookstove use by monitoring the mass of the household’s fuel supply with a load cell and the cookstove body temperature with a thermocouple. Following a proof-of-concept study of five prototypes in Honduras, a pilot study of one hundred prototypes was conducted in the Apac District of northern Uganda for one month. The results were used to evaluate user engagement with the system, verify technical performance, and develop algorithms to quantify fuel consumption and stove usage over time. Due to external hardware malfunctions, 31% of the deployed FUEL sensors did not record data. However, results from the remaining 69% of sensors indicated that 82% of households used the sensor consistently for a cumulative 2188 days. Preliminary results report an average daily fuel consumption of 6.3 ± 1.9 kg across households. Detailed analysis algorithms are still under development. With higher quality external hardware, it is expected that FUEL will perform as anticipated, providing long-term, quantitative data on cookstove adoption, fuel consumption, and emissions. 

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3. A Mixed-Method Approach: Design of a Novel Sensor System to Measure Cookstove Usage and Fuel Consumption

   Ventrella, Jennifer; MacCarty, Nordica; Zhang, Shaozeng (January 2018, IEEE Global Humanitarian Technical Conference)

   Abstract— Stakeholders in the improved cookstove sector have called for better monitoring tools to quantify the adoption and technical performance of these devices. To meet this need, researchers at Oregon State University developed the Fuel, Usage and Emissions Logger (FUEL), an integrated logging load cell and temperature sensor that measures household fuel consumption and cookstove usage. This paper outlines the design process of the FUEL system using a mixed-method ethnographic approach that combines qualitative and quantitative data to inform each step in the design process. The ethnographic methods used for field studies in Guatemala, Honduras, and Uganda included participant observation, semi-structured interviews, surveys, and focal follow. Triangulation of these results were used to validate or invalidate design hypotheses. Findings highlight general challenges faced during the design process of humanitarian technologies, including product adaptability, symbolism of “everyday” objects, and pro-innovation bias. Overall, results contribute towards a framework of ethnographic design research for humanitarian technologies. 

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4. Survey based behavior and impact assessment A case study of improved cookstove adoption in rural Honduras

   Pakravan, Mohammad; Laughlin, Katie; MacCarty, Nordica (January 2018, IEEE Global Humanitarian Technical Conference)

   Nearly 40% of the world’s population relies on inefficient burning of biomass using traditional stoves and open fires for their household energy demands. Use of traditional methods contributes to global anthropogenic climate change, and has been attributed to at least 4 million premature deaths every year. In addition, increasing population in middle- and low- income countries pushes the demand for firewood to unsustainable harvest rates leading to deforestation. To address such challenges, many international organizations have worked to provide improved cookstoves for these communities. This study applies surveys incorporating research methods from social sciences focusing on the role of users to evaluate the impacts of these technologies in the field, as well as to understand what motivates consumers to change their traditional practices. By conducting surveys before and two months after dissemination of 390 improved cookstoves in Copan Ruinas, Honduras, this research evaluated the impact of the improved cookstoves for the users and their evaluations of the presented technology. Results suggest that approximately 85% of the households used the improved cookstove as their primary stove. The top three reasons for households to continue using the stove were reported as reductions in smoke emissions, firewood consumption, and time to cook food. For 80% of the households, the level of effort required to cook with the improved cookstove was reported as significantly less than traditional stove. Future work should include sensor-based monitoring and long-term follow up to verify findings and examine impact over time. 

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5. Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

   Randy Ardywibowo, Guang Zhao (April 2019, Proceedings of Machine Learning Research)

   Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustainable monitoring. This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring. To address this challenge in the context of activity monitoring, we have designed an adaptive activity monitoring framework. We first propose a switching Gaussian process to model the observed sensor signals emitting from the underlying activity states. To efficiently compute the Gaussian process model likelihood and quantify the context prediction uncertainty, we propose a block circulant embedding technique and use Fast Fourier Transforms (FFT) for inference. By computing the Bayesian loss function tailored to switching Gaussian processes, an adaptive monitoring procedure is developed to select features from available sensors that optimize the trade-off between sensor power consumption and the prediction performance quantified by state prediction entropy. We demonstrate the effectiveness of our framework on the popular benchmark of UCI Human Activity Recognition using Smartphones. 

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