Personalized Pain Study Platform using Evidence-Based Continuous Learning Tool

Use of mobile health (mHealth) systems has increased with the advancement and proliferation of mobile computing and related technologies. It has improved efficiency and effectiveness of healthcare services. Non-invasive pain level detection from facial images is one of the promising mHealth applications. Effective pain treatment requires regular and continuous pain assessment. Most of the pain research tools are study or disease specific while some are pain (lumbar pain, cancer pain, etc.) and patient group specific (neonatal, adult, woman, etc.). This results in recurrent but potentially avoidable costs such as time, money, and workforce to develop similar services or software research tools for each research study. In this study, we have proposed, designed, and implemented a customizable personalized pain study platform that offers real-time data collection, research participant management, role-based access control, research data anonymization etc. It is also used to investigate pain level detection accuracy using evidence-based continuous learning from the facial expression data, collected from Bangladesh, Nepal and USA, which yielded about 71% classification accuracy.