Michael Potter

Background

About Michael Potter

I am passionate about developing sensor fusion and other physics-based methods and algorithms that utilize sensor data to quantify performance in broad contexts including human performance and robotics. I have nearly 8 years of experience formulating, coding, and validating various physics-based state estimation algorithms (e.g, Kalman filters), especially for applications in human biomechanics. My work has focused primarily on developing methods and algorithms that utilize raw data from wearable sensors (e.g, inertial measurement units or IMUs) to quantify human performance in sport and health. As a primary example, I developed a novel Kalman filter based sensor fusion method that estimates human lower-limb joint kinematics from raw inertial sensor data obtained from an array of seven body-worn IMUs. Notably, the method has many advantages over similar existing methods that make it suitable for future application in broader environments (e.g, at home, on the field) and activities. My primary interests include-sensor fusion -state estimation -wearables -elite athlete and warfighter performance -human biomechanics -dynamics -engineering education -rehabilitation and injury prevention -human-robot interaction I am always looking for opportunities to connect and work with others within industry and academia in any of these areas. I am also passionate about teaching. As an educator, my primary areas of expertise are in mechanics and signal processing. I teach many classes in these areas including Design of Mechanisms, System Dynamics and Controls, and Engineering Graphics.