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Earlier Interventions, Happier Outcomes

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University of Denver

Chadd Clary, Casey Meyers, Bradley Davidson, and Mohammad Mahoor

News  •

What if there was a better way to decide whether the time is right for knee surgery? Similar to the technology found in your cell phone, an inertial measurement unit (IMU) could be the key to more helpful information for those making these decisions and assist in optimizing surgery timing.

Chadd Clary, Casey Myers, Bradley Davidson, and Mohammad Mahoor

An IMU is an electronic device that measures and reports a body's accelerations, angular velocities, and orientations. When worn by people, IMUs could measure movement quality before, during, and after a diagnosis or surgical intervention.

On their own, IMUs do not have enough information or high enough fidelity to characterize a patient’s movement quality. That’s where machine learning, which is good at identifying patterns in data and associating those patterns, comes in. IMU sensor data, combined with supplementary gait lab measurements, could be used by machine learning techniques, to quantify a person’s ability to walk smoothly or navigate stairs in real time.

Together with KIHA, Chadd Clary of the Center for Orthopaedic Biomechanics (COB) and assistant professor of mechanical and materials engineering (RSECS), is researching how this approach could be crucial for those considering surgeries like hip or knee replacement. Patients would wear sensors to get a base-line idea of how they move, the quality of that movement, and how it after surgery. It could also be used to monitor progress during rehabilitation.

This research has potential implications in the field of neurodegenerative diseases as well. Movement quality is a key indicator for diagnosing disease. Chadd has seen firsthand the early signs and effects of Parkinson’s disease on a family member's body movement several years before an official diagnosis.

Currently, Chadd is focusing his research on osteoarthritis of joints where cartilage wears away, leaving bone on bone contact and with it, pain and inflammation. Understanding what those characteristic changes are will help build an algorithm that will sort patients into categories such as: healthy, mildly osteoarthritic, or ready for a total knee/hip replacement. Thus, assisting with both diagnosis and optimizing surgery timing.

Getting this research and helpful information out to those who need it is important. Chadd and his group are partnering with associate professors Melissa Akaka and Ali Besharat of DU's Consumer Insights and Business Innovation Center (CiBiC) to research the consumer side of the project. Together, the hope is to understand how and what interventions this technology can provide to help people make these decisions and live healthier, happier lives.

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