Video-Based Standoff Health Measurements
Abstract
We addressed two interesting video-based health measurements. First is videobased Heart Rate (HR) estimation, known as video-based Photoplethysmography (PPG) or videoplethysmography (VHR). We adapted an existing video-based HR estimation method to produce more robust and accurate results. Specifically, we removed periodic signals from the recording environment by identifying (and removing) frequency clusters that are present the face region and background. This adaptive passband filter generated more accurate HR estimates and allowed other applied filters to work more effectively. Measuring HR at the presence of motions is one of the most challenging problems in recent VHR studies. We investigated and described the motion effects in VHR in terms of the angle change of the subjects skin surface in relation to the light source. Based on this understanding, we discussed the future work on how we can compensate for the motion artifacts. Another important health information addressed in this thesis is Videosomnography (VSG), a range of video-based methods used to record and assess sleep vs. wake states in humans. Traditional behavioralVSG (B-VSG) labeling requires visual inspection of the video by a trained technician to determine whether a subject is asleep or awake. We proposed an automated VSG sleep detection system (auto-VSG) which employs motion analysis to determine sleep vs. wake states in young children. The analyses revealed that estimates generated from the proposed Long Short-term Memory (LSTM)-based method with long-term temporal dependency are suitable for automated sleep or awake labeling.
Degree
Ph.D.
Advisors
Delp, Purdue University.
Subject Area
Artificial intelligence|Computer science|Electrical engineering|Mathematics|Medicine|Physiology
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