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$1.2M NSF Grant to Develop Objective Pain Assessment Sensing System
The National Science Foundation (NSF) recently awarded a $1.2 million, four-year research grant to a Northeastern-led team to develop a Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS) that improves pain assessment and management, reduces opioid dependency and advances the field of pain management research and patient safety.
Led by Yingzi Lin, associate professor, Mechanical and Industrial Engineering (MIE), and director of the Intelligent Human-Machine Systems Lab, with MIE Professor Sagar Kamarthi (co-PI), the multi-disciplinary project will be conducted in collaboration with the College of Nursing and Health Innovation, the University of Texas at Arlington, and Brigham and Women’s Hospital, Boston.
For Lin, the research project grew out of a highly personal experience: when she was in labor with her first child, her doctor asked her to rate her pain level. “I took a close look at the visual analog scale on the wall and gave him a low score,” says Lin. “I told the doctor, ‘I’m fine,’ but to be honest, I was in pain.”
In the U.S., doctors rely primarily on a patient’s subjective assessment of pain using visual scales to rate pain intensity from 0 to 10. Lin spent a lot of time thinking about this practice as she awaited her son’s birth. “I realized they were measuring heart rate, oxygen level, blood pressure and contractions, all of which is very scientific, but the only way to determine patient pain level was by asking.”
Breakthrough research with societal impact
During the past several years, Lin and her collaborators explored pain assessment and management practices, gathering valuable data through projects at her laboratory and in clinical settings – painstaking work that is finally paying off.
Under the NSF grant, the team will conduct a pilot research study at Brigham and Women’s Hospital, using multimodal cognitive sensors, for which Lin’s research group has gained rich experience from her earlier work. “Our focus will be on making sensors less intrusive to human subjects, which is a huge challenge,” she says. “If a patient is already in pain, putting more devices on them can add more distress.”
Ultimately, the team hopes to develop an automated system for use at the patient’s bedside. The system, which will provide scientific data complementing a patient’s subjective rating, will enable doctors to make more effective treatment decisions, avoid over prescribing pain pills that can lead to addiction or help patients who are actually in severe pain.
Lin emphasizes the societal importance of the project: the U.S. Department of Health and Human Services identified pain management as one of the five key strategies to address the opioid crisis, which has been declared a national emergency. “This problem touches everyone’s life in some way,” she says. “It’s pushing me to work harder with my collaborators to make this research a success.”
Abstract Source: NSF
Few objective pain assessment techniques are currently available for use in clinical settings. Clinicians typically use subjective pain scales for pain assessment and management, which has resulted in suboptimal treatment plans, delayed responses to patient needs, over-prescription of opioids, and drug-seeking behavior among patients. This project will investigate science-based methods to build a robust Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS) and a clinical interface capable of generating objective measurements of pain from multimodal physiological signals and facial expressions. COMPASS will allow objective measurements that can be used to significantly improve pain assessment, pain management strategies, reduce opioid dependency, and advance the field of pain-related research. The educational plan will include activities to engage patient training, K-12 students, minorities and underrepresented groups, as well as general public. These outcomes will also lead to development of a diverse work force needed to support advanced medical technologies and services.
Using advanced biosensing systems, data fusion algorithms and machine learning models, this project will develop a robust, reliable, and accurate pain intensity classification system, COMPASS, for estimating pain intensity experienced by patients in real-time on a 0-10 scale, which is the standard scale used by physicians in clinical settings. In the initial phase of the project, the team will conduct a pilot at Brigham and Women's Hospital to experiment with the different elements for developing the sensing systems and collect data to develop data fusion algorithms and machine learning models. In the later phase of the project, the team will collect an extensive set of data to train and validate the fully implemented COMPASS. Physiological sensor data from electroencephalograph, facial-expression, patient self-reported pain scales, and physician/nurse assessed pain scales will be collected from the subjects as they experience pain modulated by medical therapies that cause patients pain. The project will investigate evidence-based machine learning and feature extraction methods for physiological signals and facial-expression images. This highly interdisciplinary research will make significant contributions to the areas of pain assessment and management, human factors and patient safety.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.