• Yan Hao Tan School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
  • Holden Li King Ho School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Keywords: Machine Learning, Polynomial Regression, Photoplethysmography, Autonomous Hospital Bed Pushing, Vitals Monitoring, Nursing


In community-based healthcare, the nursing workforce requires low-skilled nursing automation in the hospital to accelerate talent development towards high-skilled advance practice nurse for community deployment. As precursor, the hospital bed pushing operation for medium-risk patient was hypothesized as a novice nursing task where artificial intelligence automation is possible. The solution framework was embodied by a concept of operation with non-invasive vitals monitoring as priority to study feasibility in addressing patient life-safety requirements. Polynomial regression machine learning of 65 one-hour sets of finger PPG data from a single subject were collected and studied. Convergence of finger PPG to 8th degree polynomial was observed which suggested process feasibility towards establishing patient safe states during autonomous journey. Process reliability ranged between 2% to 95% with long PPG counts as influencing factor for drops in reliability score. Motivation/Background: A predictable non-invasive vitals monitoring was priority to enable autonomous hospital bed pushing framework to address patient life-safety concerns during autonomous journey. Finger PPG is a non-invasive and easy to use method to monitor heart related activities and used to study for convergence and reliability within the framework. Method:65 one-hour sets of finger PPG was recorded from a single male, age 27 subject. The data was processed by polynomial regression machine learning technique to output the degree of polynomial with highest cross validation score mean. Results: Convergence of regressed PPG data to 8th degree for both pre-journey and journey datasets and degree of polynomial matching reliability of 2% to 95% were observed. Conclusions: Convergence of PPG data facilitates the establishment of safe physical states in vitals monitoring, enabling the autonomous hospital bed pushing framework for further development. Reliability remains an area for improvement via medical grade.


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How to Cite
Yan Hao Tan, & King Ho, H. L. (2018). EXPERIMENTAL MACHINE LEARNING OF FINGER PHOTOPLETHYSMOGRAPHY (PPG) FOR AUTONOMOUS HOSPITAL BED PUSHING FRAMEWORK USING POLYNOMIAL REGRESSION . International Journal of Engineering Technologies and Management Research, 5(9), 108-119. https://doi.org/10.29121/ijetmr.v5.i9.2018.295