UC Irvine

In the healthcare sector, there is a strong demand for accurate objective pain assessment as a key for effective pain management. Real-time and accurate objective pain assessment help caregivers and hospital staffs decide the proper dosage of pain medication to be provided to a patient in a timely manner. The state-of-the-art automatic and objective pain assessment techniques in the literature can be classified into two main categories: physiological-based and behavioral-based. The first-class monitors the changes in patients' physiological data such as heart rate (HR), heart rate variability (HRV), Electrocardiography (ECG), Electromyography (EMG), Photoplethysmography (PPG) to identify autonomic nervous system reactions to pain, while the second class utilizes behavioral reactions to pain such as techniques using computer vision-based techniques by extracting features from patients' head poses and facial expressions. Recent pain monitoring systems have recently gained attention on multi-modality meaning that they deploy a combination of both approaches to improve pain monitoring accuracy. Although such complex models are highly accurate in pain monitoring, they are more computationally intensive imposing feasibility limitations to implement them on wearable devices in terms of energy efficiency (battery life) as well as computation latency. A smart and self-aware system capable of adaptively making a decision at run-time in response to the changes in pain level and context can minimize energy consumption by dynamically offloading tasks to the gateway devices at the edge layer. For this reason, in this work, a self-aware system is proposed for the continuous assessment of pain intensity at the edge layer. Using the BioVid heat pain dataset, this approach demonstrates a promising reduction in terms of energy consumption with a negligible accuracy loss compared with its non-adaptive counterpart.