iHurt

Intelligent and Automatic Pain Assessment Tool Employing Behavioral and Physiologic Indicators

Intelligent and Automatic Pain Assessment Tool Employing Behavioral and Physiologic Indicators Contact Person:

Amir M. Rahmani

Other PIs/Investigators/PhD students:

Kai Zheng
Nikil Dutt
Ariana Nelson
Pasi Liljeberg
Sanna Salanera
G.P. Li

Partners:
TYKS Hospital and UCI Medical Center Funding Agency:

Academy of Finland

Project Summary:

Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage. It is a subjective sensation and patients’ self-report is considered the most reliable indicator of pain. However, assessment of pain is particularly difficult when the ability of the patient to communicate is limited or impossible e.g. during critical illness, under sedation and anesthesia or for infants. The objective of this project is to benefit from the offered features of the IoT and sensor networks to provide an automatic tool which can detect and assess pain employing behavioral and physiologic indicators such as facial muscle activity, heart rate, blood pressure, and breathing rate. The aim of this project is to develop a system based on the Internet of Things to detect and assess pain in a reliable and objective way by enabling the pain diagnoses in the case when the patient is unable to communicate and express the pain sensations.

Publications:

– Emad Kasaeyan Naeini, Mingzhe Jiang, Elise Syrjälä, Michael-David Calderon, Riitta Mieronkoski, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana Nelson, Amir M Rahmani, “A Prospective Study Evaluating a Pain Assessment Tool in Postoperative Environment: A Protocol for Algorithm Testing and Enhancement,” JMIR Research Protocols Jorunal, 2020.
– Mingzhe Jiang, Riitta Mieronkoski, Elise Syrjälä, Arman Anzanpour, Virpi Terävä, Amir M. Rahmani, Sanna Salanterä, Riku Aantaa, Nora Hagelberg, and Pasi Liljeberg, “Acute pain intensity monitoring with the classification of multiple physiological parameters,” Springer – -Journal of Clinical Monitoring and Computing (Springer-JCMC), 2018.
– Geng Yang, Mingzhe Jiang, Wei Ouyang, Guangchao Ji, Amir M. Rahmani, Pasi Liljeberg, and Hannu Tenhunen, “IoT-based Remote Pain Monitoring System: from Device to Cloud Platform,” IEEE Journal of Biomedical and Health Informatics (IEEE-JBHI), 2017.
– Emad Kasaeyan, Sina Shahhosseini, Ajan Subramanian, Tingjue Yin, Amir M. Rahmani, and Nikil Dutt, “An Edge-Assisted and Smart System for Real-Time Pain Monitoring,” IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE’19), 2019, USA.
– Mingzhe Jiang, Riitta Mieronkoski, Amir M. Rahmani, Nora Hagelberg, Sanna Salanterä, and Pasi Liljeberg, “Ultra-Short-Term Analysis of Heart Rate Variability for Real-time Acute Pain Monitoring with Wearable Electronics,” IEEE International Conference on Bioinformatics and Biomedicine (BIBM’17), 2017, USA.