Webinar on Clinical Gait Analysis using Smartphone Videos

Wednesday, March 8, 2023, 9:00 AM Pacific Time


We are pleased to announce our upcoming webinar with Ryan Roemmich and Jan Stenum from the Kennedy Krieger Institute and John Hopkins University entitled “Clinical Gait Analysis using Video-Based Pose Estimation: Multiple Perspectives, Clinical Populations, and Measuring Change”. They will present the software they have developed to perform gait analysis in multiple clinical populations using smartphone videos and discuss validation results. The software is available for others to use in their own research, and during the webinar, Dr. Stenum will guide the audience through the analysis workflow to obtain these measures using their software.  Learn more and register | Pre-print

 


Research Summit: Models & Sensors to Measure Real-World Muscle Function & Movement

Sunday, June 18, 2023, Canmore, AB, Canada

ABSTRACT DEADLINE: April 3, 2023


The Restore and Mobilize Centers at Stanford University and C-STAR at Shirley Ryan Ability Lab invite you to a research summit, “Models & Sensors to Measure Real-World Muscle Function & Movement.” Hear about late-breaking research, along with available tools, frameworks, and best practices for applying machine learning and wearable sensing to study real world movement. Submit an abstract to present your research. We encourage you to also submit an abstract and attend the Rocky Mountain Muscle Symposium immediately after. Learn more and register | Call for abstracts



Course on Machine Learning and Sensors in Rehabilitation

Wednesday, May 17, 2023, Virtual


Over recent years, there has been an explosion of interest in wearable sensors, video, and computer vision for healthcare and patient monitoring. In this introductory course co-taught by C-STAR and the Restore Center, we will take a practical approach to understand how machine learning and sensors can be applied in the rehabilitation field, pitfalls to avoid, how to get started and how these tools can create reliable digital biomarkers. The course is eligible for CME credit. Register now