Wearable Graphene Respiratory Sensor for Early Detection and Monitoring of Asthma - Project Summary / Abstract Asthma is a chronic, non-communicable disease that affects 300 million children and adults worldwide. In the United States alone, asthma burdens 26 million citizens. With the rise of global temperatures, climate change is adding to the complexity of asthma occurrences. Changes in environmental conditions can lead to negative downstream effects such as prolonged pollen seasons that can elevate aeroallergens. Increase in aeroallergens are concerning due to its association to allergen-induced asthma. Asthma is a debilitating disease that has no cure, and the severity of the disease varies person to person. Diagnosis and monitoring in children is challenging due to an inability to follow technical instructions to perform lung functioning tests such as spirometry and their inability to communicate symptoms. To alleviate difficulties associated with diagnosis and monitoring of asthma treatments in children, Aquillius Corporation proposes to develop a respiratory sensor capable of real-time continuous monitoring and measuring of pulmonary functions. Utilizing graphene nanotechnology, this innovative device will be cost-effective, robust, and suitable for all ages. Our approach seeks to improve patient monitoring, treatment, and diagnosis of asthma through development of innovative respiratory sensor. To reach this goal, Aquillius will (i) Optimization of respiratory sensor’s function by utilizing a manikin lung simulator for normal and asthma conditions; and (ii) Demonstrate the ability of GNS respiratory sensor to capture pulmonary function data in real time. Upon the successful completion of Phase I feasibility studies, Phase II will focus on development and refinement of the device by incorporating human test subjects with asthma. The end-user interface and software will also be further developed. Overall, the commercialization of the respiratory sensor will greatly improve patient care by measuring a patient’s lung function in real-time, thus helping with both accurate diagnosis and personalized asthma treatments.
