2021/12/11 by Chinazunwa Uwaoma, Uwaoma, Chinazunwa
Computer Science · Engineering · Medicine · #Chronic Obstructive Pulmonary Disease (COPD) Research #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #Physical Activity and Health #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2112.09068
arxiv created 2021/12/11 · openalex publication_date 2021/12/11 · arxiv updated 2021/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While physical activity has been described as a primary prevention against chronic diseases, strenuous physical exertion under adverse ambient conditions has also been reported as a major contributor to exacerbation of chronic respiratory conditions. Maintaining a balance by monitoring the type and the level of physical activities of affected individuals, could help in reducing the cost and burden of managing respiratory ailments. This paper explores the potentiality of motion sensors in Smartphones to estimate physical activity thresholds that could trigger symptoms of exercise induced respiratory conditions (EiRCs). The focus is on the extraction of measurements from the embedded motion sensors to determine the activity level and the type of activity that is tolerable to individuals respiratory health. The calculations are based on the correlation between Signal Magnitude Area (SMA) and Energy Expenditure (EE). We also consider the effect of changes in the ambient conditions like temperature and humidity, as contributing factors to respiratory distress during physical exercise. Real time data collected from healthy individuals were used to demonstrate the potentiality of a mobile phone as tool to regulate the level of physical activities of individuals with EiRCs. We describe a practical situation where the experimental outcomes can be applied to promote good respiratory health.