Prediction of Chronic Pain Onset in Patients Experiencing Tonic Pain: A Survey

Abstract

Pain is caused by particular nerve fibers that transmit impulses to the brain, where they are interpreted. Pain has a multidimensional nature, making it hard to decipher, because of its subjectivity. Chronic or recurring pain is a common impairment among the elderly, significantly bringing down their quality of life. Assessment and treatment of pain are critical challenges in providing interventions for a variety of illnesses, especially because their origins are closely related to deep rooted issues. There is a rising interest in finding objective, nonverbal methods to quantify pain in people who are unable to self-report discomfort, such as those suffering from dementia or those in a minimally conscious state. Though, self and subjective reports are the common means of measuring the impact of pain and assessing its intensity, studies suggest that there are multiple sensory means through which a cumulative and concise data on occurrence of pain (tonic or phasic) can be retrieved, such as, through HRV (Heart Rate Variation), EEG/ECG/EMG readings and health performance metrics based on everyday activities. Our project aims to utilize the space of sensory technologies, with a focus on EEG data and IoT to conduct pain assessments with the help of machine learning techniques and explore IR therapy as well as electrical stimulation in subjects that experience chronic pain. Our intervention in pain assessment looks to combine subjective reporting and sensor based crucial data points on occurrence of pain to derive a reliable method of pain reporting.