From Fall 2019 to Spring 2020, undergraduate student researchers, Nate Addonizio and Lexi Nehls, assisted with the Idiopathic Toe-Walking in children project. They had hands-on experience of assisting both the clinical team (Dr. Marybeth Grant-Beuttler and DPTs) and engineering team (Dr. Rahul Soangra, Christopher Hoang, and Michael Shiraishi) as well as interacting with the pediatric subjects. Their in-lab research was cut short due to COVID-19 but they continued remotely by analyzing the data and reviewing research articles.

Nate, who also works as a part-time PTA, was particularly interested in the Balance Manager tests since it was a device used in his clinic for adults primarily. This device assessed the children’s balance and limits of stability by providing targets for them to achieve. Using the subject’s height, the center of mass (COM) is extrapolated and compared to center of pressure (COP) travel, determining the subject’s limits of stability. Under the mentoring of Christopher and Michael, Nate used a K-means clustering algorithm to classify the percentage of subjects reaching the theoretical limit of stability by inputting COM and COP data. He presented his findings in a 15 minute oral presentation at Chapman’s Student Symposium 2020.

Lexi, who was accepted as intern at CHOC’s Medical Intelligence and Innovations Institute (MI3), was interested in the data collected from wearable devices. The subjects would wear an inertial measurement unit (IMU), Xsens, and perform various functional tests ranging from squatting to normal/toe walking. Using motion capture as ground truth, she identified intervals when the subject lifts heels/toes as well as heel/toe strikes and cross referenced with the acceleration signals from the ankles and torso IMUs. Lexi quantified the amplitude and frequency of subjects toe walking and heel walking to classify the different walks based on wearable sensors. She presented her findings in a poster presentation at Chapman’s Student Symposium 2020.

K-means clustering of center of pressure (green) and center of mass (red).

Best heel strike walking motion capture with EMG and angles.