Measuring Vital Signs and Pain Intensity Level Classification through Image Processing
Authors: 
Jennifer C. Dela Cruz, Glenn V. Magwili, Merlin A. Bautista, Jr., Mollie Ainah G. Becina, Donald Jason Hao Paredes, Hershey Marieh O. TumulakAuthors Info & Claims
Jennifer C. Dela Cruz, Glenn V. Magwili, Merlin A. Bautista, Jr., Mollie Ainah G. Becina, Donald Jason Hao Paredes, Hershey Marieh O. TumulakAuthors Info & ClaimsPages 284 - 288
Abstract
This study developed an all-in-one portable electronic device that measures the four human vital signs and classifies the pain intensity level through image processing. The inputs are gathered from several sensors for the vital signs and from a webcam for the pain intensity level classification. All data is processed by an Arduino Nano board and a Raspberry Pi 3 with the integration of OpenFace and the Prkachin and Solomon Pain Intensity (PSPI) scale for image processing. The output is then displayed on a liquid crystal display (LCD) integrated into the device and could be monitored wirelessly on a smartphone or computer that supports the VNC application. The device is then tested on a group with members aged eighteen (18) years old and above.
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Index Terms
- Measuring Vital Signs and Pain Intensity Level Classification through Image Processing
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Published In
September 2020
350 pages
ISBN:9781450377249
DOI:10.1145/3397391
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Association for Computing Machinery
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Published: 15 September 2020
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ICBET 2020
ICBET 2020: 2020 10th International Conference on Biomedical Engineering and Technology
September 15 - 18, 2020
Tokyo, Japan
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