SBIR Phase I: Medical Device for Monitoring Respiratory Disease

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be a novel wearable device for unobtrusively detecting changes in the lung health of patients with chronic respiratory disease. This Passive Unobtrusive Lung function Monitor (PULMO) will be the only device that can automatically measure a patient's lung health information without requiring any active engagement from the clinician, and without imposing any obtrusive changes to the patient's daily routine. The PULMO is useful in a broad range of respiratory disease applications, such as lung transplant post-operative care, asthma management and respiratory therapy research, that require continuous, objective and unobtrusive monitoring of lung function change. Since PULMO technology is compatible with low-cost microcontrollers, it has the potential to dominate the spirometry market and respiratory clinical trials space with high-volume production. The spirometry market has an expected CAGR of 9.4 % from 2018 to 2025, reaching a $1.4 billion market value by 2025, while clinical trials expenditures on new respiratory therapy drugs is forecast to reach $1.7 billion in 2025. This Small Business Innovation Research (SBIR) Phase I project addresses the major drawback of the state-of-the-art in continuous monitoring of respiratory disease, which is that it demands daily discipline, effort and proper technique of the patient, resulting in missing, invalid or fabricated data. In this project, a manufactured PULMO test device will be implemented with an intelligent event detection unit combined with a low power microcontroller, and the total average power consumption will be less than 300 microWatts. The measurement error of the PULMO will be within +/- 5 %, which is necessary for detecting changes in lung impairment. The intelligent event detection unit will be implemented as a nonlinear dynamical system in a custom integrated circuit. A gated recurrent neural network that is optimized for embedded low resource systems will be implemented in the low power microcontroller and will be used to detect respiratory disease symptoms. Controlled tests will be performed with a silicone phantom chest to evaluate the measurement accuracy of the PULMO device. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.