Vitals are Vital! Visual Vital Metric Detection for Multiple Subjects

Vitals are vital! Patient vitals provide critical information essential to the successful diagnosis and management of patients in every healthcare settings, particularly in the acute-care setting. Heart-rate(HR), and to a greater degree heart rate variability, as well as overall patient mobility can indicate the presence of severe disease states. Practical and technical constraints currently limit monitoring in healthcare settings to select (eg. admitted) patients, leaving numerous others unmonitored and potentially at risk. Wed like to use Computer vision to provide a non-invasive solution to this acute problem. To tackle this problem, we extended Haar cascade detectors and SIFT based tracking and face recognition for a robust continuous face detection and assignment. Applying independent component analysis (ICA) based blind signal decomposition we were able to accurately extract heart-rate from video. Additionally, we proposed a method based on multi-scale spatial-temporal correlation in motion field to recognize illness related activities (eg. coughing). The HR detection error is 1± 2bpm and action recognition (coughing) is 90%. This work could be used to triage patients in an unmonitored, possibly acute, healthcare setting. Further improvement of the algorithms and implementation is in progress.