Review on Biologic Information Extraction Based on Computer Technology

With the growing development of computer technology and sensor technology, artificial intelligence has been applied to a variety of scenarios, providing solutions to problems in different industries. It is a new dimension in the research of machine vision artificial intelligence to perceive and recognize human biological information, such as physiological state, emotion and biological identity, which contributes to making auxiliary judgment and decision. Based on the extraction of human body-related biological information, this paper aims to review on three aspects which are image-based physiological signal acquisition, sleep information extraction and image-based emotion recognition. With respect to image-based physiological signal acquisition, blind source separation and Eulerian video magnification technology are two more commonly used methods for non-contact video to extract pulse information. Based on the principle of photo plethysmography (PPG), there are also many other methods such as signal weighting analysis and supervised learning algorithm, which are gradually applied to non-contact video acquisition of pulse-related physiological signals. In aspect of sleep information extraction, sleep quality can be analyzed through heart rate and breathing signals. When it comes to image-based emotion recognition, there is still some room for improvement in the accuracy of parameter extraction.

[1]  A. B. Hertzman.  Photoelectric Plethysmography of the Fingers and Toes in Man , 1937 .

[2]  P. Ekman.  An argument for basic emotions , 1992 .

[3]  Marc Garbey,et al.  Interacting with human physiology , 2007, Comput. Vis. Image Underst..

[4]  Dario Floreano,et al.  Sleep and Wake Classification With ECG and Respiratory Effort Signals , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[5]  Rosalind W. Picard,et al.  Non-contact, automated cardiac pulse measurements using video imaging and blind source separation , 2022 .

[6]  Daniel McDuff,et al.  Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam , 2011, IEEE Transactions on Biomedical Engineering.

[7]  John R. Shambroom,et al.  Validation of an automated wireless system to monitor sleep in healthy adults , 2012, Journal of sleep research.

[8]  Sazali Yaacob,et al.  DETECTION OF HUMAN STRESS USING SHORT-TERM ECG AND HRV SIGNALS , 2013 .

[9]  Luca Citi,et al.  A nonlinear heartbeat dynamics model approach for personalized emotion recognition , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Jeen-Shing Wang,et al.  A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition , 2013, Neurocomputing.

[11]  Roy Kalawsky,et al.  Noncontact imaging photoplethysmography to effectively access pulse rate variability , 2012, Journal of biomedical optics.

[12]  Marwin Ko.  Applications of long range dependence characterization in thermal imaging & heart rate variability , 2015 .

[13]  E. P. Tomasini,et al.  The non-contact measure of the heart rate variability by laser Doppler vibrometry: comparison with electrocardiography , 2016 .

[14]  Shintaro Izumi,et al.  Non-contact Instantaneous Heart Rate Monitoring Using Microwave Doppler Sensor and Time-Frequency Domain Analysis , 2016, 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE).

[15]  A. Goshvarpour,et al.  Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots , 2017, Australasian Physical & Engineering Sciences in Medicine.