Use of accelerometers for automatic regional chest movement recognition during tidal breathing in healthy subjects

Abstract Recognition of breathing patterns helps clinicians to understand acute and chronic adaptations during exercise and pathological conditions. Wearable technologies combined with a proper data analysis provide a low cost option to monitor chest and abdominal wall movements. Here we set out to determine the feasibility of using accelerometry and machine learning to detect chest-abdominal wall movement patterns during tidal breathing. Furthermore, we determined the accelerometer positions included in the clusters, considering principal component domains. Eleven healthy participants (age: 21 ± 0.2 y, BMI: 23.4 ± 0.7 kg/m 2 , FEV 1 : 4.1 ± 0.3 L, VO 2 : 4.6 ± 0.2 mL/min kg) were included in this cross-sectional study. Spirometry and ergospirometry assessments were performed with participants seated with 13 accelerometers placed over the thorax. Data collection lasted 10  min. Following signal pre-processing, principal components and clustering analyses were performed. The Euclidean distances in respect to centroids were compared between the clusters ( p p