In this work, a low-cost, off-the-shelf load cell is installed on a typical hospital bed and implemented to measure the longitudinal ballistocardiogram (BCG) in order to evaluate its utility for successful contactless monitoring of heart and respiration rates. The major focus is placed on the beat-to-beat heart rate monitoring task, for which an unsupervised machine learning algorithm is employed, while its performance is compared to an electrocardiogram (ECG) signal that serves as a reference. The algorithm is a modified version of a previously published one, which had successfully detected 49.2% of recorded heartbeats. However, the presented system was tested with seven volunteers and four different lying positions, and obtained an improved overall detection rate of 83.9%.
Keywords: ballistocardiography; clustering; contactless monitoring; heart rate; load cell; pattern recognition; unsupervised machine learning; vital signs monitoring.