Facial Pain Expression Recognition in Real-Time Videos

Abstract

Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%–55% and an improved processing time of 20%–25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%–100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC‐McMaster Shoulder Pain Database as well.

Incorporation of “Artificial Intelligence” for Objective Pain Assessment: A Comprehensive Review

“…Several studies developed novel models for pain recognition with ML by analyzing facial expressions. They were able to automatically detect pain successfully with relatively high accuracy in more than 95% of the subjects [ 33 – 36 ]. Other studies used the AI-based approach to analyze clinical notes and patients’ records with pain assessment information to identify components related to pain classifications and severity [ 37 ].…”

“…Other studies used the AI-based approach to analyze clinical notes and patients’ records with pain assessment information to identify components related to pain classifications and severity [ 37 ]. Results of a recent review provide evidence that machine learning, data mining, and natural language processing can improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively [ 33 – 36 ]. These promising results may help in the creation of new pain assessment instruments with human language technology.…”

Pain Management in Pediatric Burns: A Review of the Science behind It

“…Te use of innovative tools and devices has improved the accuracy and objectivity of pain assessment in children. For example, the use of facial expression recognition software and wearable sensors has allowed for realtime monitoring and analysis of pain-related behaviors and physiological responses in children [63,64]. Tese technological advancements provide valuable insights into the child's pain experience, allowing healthcare providers to tailor pain management strategies more efectively.…”

Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system

“…The potential of artificial intelligence (AI)-based facial expression analysis using a facial expression recognition system (FERS) to identify emotions, pain, and nonverbal information among persons with psychiatric disorders has been documented [6][7][8][9]. FERS successfully predicted 8 basic mood phenotypes using more than 1,000,000 facial images collected from the internet, i.e., disgust, fear, sadness, anger, happiness, surprise, neutral, and contempt [9][10][11].…”

“…Although deficient facial expressions were common presentations of persons with neurodegenerative disorder, the enhanced facial responses to pain in PwD provided opportunities for FERS to identify somatic discomforts [8,10,12]. The advanced development of AI technology and deep learning programs enables FERS to identify facial expressions and their changes over time from video streams, creating opportunities to develop the automatic detection of BPSDs to improve the quality of dementia care [7][8][9][10]13]. Evidence suggests that BPSDs are often related to suboptimal management of physical pain, but pain is not the only aggravating factor that precipitates or aggravates BPSDs in PwD [14].…”