Application of Sensor Based Analysis of Movement for Quantification of Pain and Discomfort
There is a large body of literature describing the application of sensing technology to study and understand many aspects of human motion. Many clinicians and researchers have reported on the use of sensors to compare movement in normal and pathological states and to quantify therapeutic effect. Such analyses are often complex and expensive. Rapid advances have been made in the development of smaller inexpensive, more sensitive and capable sensors such as Multi-Axis Inertial Measurement Units. These devices sense rotation and acceleration via gyroscopes, accelerometers and magnetometers. This work describes the application of machine learning techniques and a consumer available multi-axis inertial measurement unit to evaluate clinically relevant movement related to understanding pathology related pain and discomfort and the quantification of the therapeutic effect of analgesics and other pharmacological and mechanical therapies.
Advisory Industry Consultant at SAS Institute
Dr. Wolff has over 25 years of experience in the health and life science industries as a scientist and analyst working in the U.S. and Europe. Mark joined SAS in 2005 and is an Advisory Industry Consultant and Chief Health and Life Science Analytics Strategist for the SAS Global IoT Division. Mark’s areas of expertise include the development and application of advanced and predictive analytics in healthcare and life sciences with a particular interest in outcomes and safety. Current work focuses on methods and application of Machine Learning to real time sensor/IoT data in support of outcomes and safety research, visualization and development of intelligent, decision support systems. Prior to joining SAS Mark held a variety of research and leadership positions in academia, government and industry. He holds a Bachelor of Science degree from Loyola College in Maryland, a Master of Science in Entomology and a Doctorate in Toxicology from North Carolina State University.
End-User, Government, Enterprise, Small / Medium Enterprise, OEM
Health Care, Diagnostics, Clinical Decision Support, Machine Learning, Predictive Analytics, Value Based Care, Patient Safety, Outcomes Research
VP / Director, Middle Management, Technical, Business Line Management
Advanced, Intermediate, Beginner
Healthcare, Pharmaceutical / BioTech
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