University-Community Partnership For Water Technology Deployment And Co-Innovation: A Decade Of Engagement

The Ateneo de Manila University (Philippines), through its Ateneo Innovation Center (AIC), integrated existing simple technologies into one system – the Water-Electricity-Lighting System (WELS) – to respond to the need for potable water, lighting and communication. WELS is a portable clean water system with provision for lighting and cellular phone charging. It can be connected to a rainwater harvesting facility. Ten years of WELS deployments revealed its flexibility for customization in order to address varied water needs, especially for disaster response. Review of documentations done on past installation experiences highlights the value of engagement between university-based technology providers and community-recipients. This engagement leads to technology improvement and sustainability through co-innovation and contributes to community resilience and education through hands-on training. This paper narrates a decade of deployment experiences and presents the process of community involvement. We present a model of engaging the stakeholders that brings mutual benefit to both university and community through this partnership

Water-Electricity-Light System: Technology Innovations

This paper presents the design of the Water-Electricity-Light System (WELS) that is an integration of technologies composed of rain catcher, mechanical filter and UV irradiation, solar panel, charge converter, LED light, inverter and car battery. We traced back its development from a bulky and expensive system that was meant to generate drinking water into a more innovative water cleaning system that integrated lighting and cellphone charging. We tracked the improvements applied to the system to make the design more efficient yet simple enough to be replicated and customized in order to address varied needs. We shared the alterations made to the system components based on installation experiences in different contexts. We also explored ways to lower its cost and to make its power storage more durable. Initial results are shared in this paper. Having seen its usefulness and realized its successful implementation on the ground, we are proposing the pre-positioning of WELS to promote disaster resilience in a community level. We based this assertion on the review of all documentations done and feedback gathered from our ten-year experience of more than 140 WELS installations all over the Philippines

Design and Development of Electronic Sensor and Monitoring System of Smart Low-cost Phototherapy Light System for Non-Invasive Monitoring and Treatment of Neonatal Jaundice

This paper showcases our previous and continuously improving development at Ateneo Innovation Center (AIC) and partners in designing and further enhancing the existing Low-cost Phototherapy Light System (LPLS) and Improved Low-cost Phototherapy Light System (ILPLS) to the new Smart Low-cost Phototherapy Light System (Smart LPLS) with non-invasive jaundice monitoring for newborns with Neonatal Jaundice (NNJ). Developing this tool will help determine the intensity of yellowish color in infants and can monitor NNJ in a non-invasive way. The system is envisioned to be integrated with Mobile or Near Cloud as part of Smart Nursing Station together with other hospital equipment for monitoring, collection, and management of medical records and services. Its solar-power features for off-grid and remote deployments were also explored. This contribution is an extension of the Intelligent Sensors and Monitoring System for Low-cost Phototherapy Light for Jaundice Treatment that was presented in the International Symposium on Multimedia and Communication Technology (ISMAC) in 2019

Intelligent Sensors and Monitoring System for Low-Cost Phototherapy Light for Jaundice Treatment.

A prototype of a low-cost phototherapy light system (LPLS) was deployed by the Ateneo Innovation Center (AIC) at a public hospital in Metro Manila, Philippines. It underwent clinical investigation for two years under the supervision of licensed physicians in a public tertiary hospital. This paper presents the process of upgrading the LPLS in order to enhance capabilities and improve efficiency yet remain affordable. The following features were added: (1) a visual and auditory monitoring system in order to remotely oversee the infant from the nurse station; (2) an automation system that stores data about the device\u27s light intensity and bulb temperature and records ambient humidity; (3) an alarm system that activates the warning lights if sensor readings are in critical level and if the bulbs need to be replaced; and (4) a time setting to manually set the time of operation and automatically turn-off the device as programmed The upgrades increased the system\u27s cost but it remained cheaper than the ones commercially available. For deployment in remote or off-grid hospitals, the system was equipped with a solar-powering provision

Design and Development of A-vent: A Low-Cost Ventilator with Cost-Effective Mobile Cloud Caching and Embedded Machine Learning

We designed and developed a low-cost mechanical ventilator prototype that meets the government\u27s minimum viable standards. We substituted alternative off-the-shelf food-grade for the medical-grade parts and improvised some components for our prototype. We cleaned the air from the oxygen tanks and compressors before going to the lung test bag. We designed a solar-powered battery system that can run electronic components for a fail-safe operation. We demonstrated how the AIC Near Cloud system can store air flow rate and air pressure data which were generated during the prototype\u27s operation. We used Embedded Machine Learning in sensors and data processing by using flow and pressure sensors to provide accumulated data that can be utilized in training the machine learning software. The patient-ventilator asynchrony detection model was tested using data generated from the emulated ventilator waveform events that mimic the patient-ventilator asynchrony. A different compression pattern was applied to the test lung and results showed the training, validation, and model testing that yielded 98.7%, 99.1%, and 97.18 percent accuracy, respectively. Having demonstrated that the Tiny ML can be trained to detect anomalies from several data points, we realized the feasibility of detecting ventilator patient vibration anomaly, and unusual acoustic signatures, among others, for future works