Webinar: OpenSense: Analyzing Motion with Inertial Measurement Unit (IMU) Data

Tuesday, November 16, 2021, at 10:00 AM Pacific Time   

OpenSense is a new workflow within OpenSim that provides new tools to use IMU data to analyze motion. In this webinar, Carmichael Ong from Stanford University will present an overview of OpenSense and the results of a validation study examining traditional marker-based inverse kinematics versus IMU-based inverse kinematics. In the second part of the webinar, Dr. Ong will teach participants how to use OpenSense with an example using gait data. Register now

Call for Abstracts for OpenSim Teaching Symposia at World Congress of Biomechanics 

Deadline to Submit: November 15, 2021  

Joy Ku from Stanford University and Thomas Uchida from the University of Ottawa will be moderating a session on “Teaching with the OpenSim Neuromuscular Biomechanics Software” at the World Congress of Biomechanics 2022. We invite you to submit abstracts describing how you use OpenSim in your teaching at any level (e.g., high school students, undergraduates, clinicians). When submitting your abstract, select either this session or “Use of Open Access Resources” under the “Biomedical Engineering Education and Outreach” track. Learn more

Teaching Materials to Accompany Biomechanics of Movement Textbook 

Homework problems and other teaching resources for the textbook Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation can be found on the textbook companion website. We have recently added slides for you to use in your lectures based on figures from the textbook. The figures and slides are free to use for teaching and other non-commercial purposes. Slides for Chapters 2 to 5 are now available; the others will be coming soon. Download these resources

Accelerometry Data from Daily Life for Community-Dwelling Adults and Adults with Stroke 

Catherine Lang from Washington University has shared a dataset with accelerometry data from neurologically intact, community-dwelling adults and adults with stroke. This data was collected with Actigraph accelerometers during 1 hour in the lab and 24 hours in the real world. Data from persons with stroke were acquired during baseline assessment, weekly during an intervention, and then post-intervention. Learn more and download data

Deep Reinforcement Learning for Modeling Human Locomotion Control in Neuromechanical Simulation 

Seungmoon Song from Stanford University and colleagues recently published a paper in the Journal of NeuroEngineering and Rehabilitation reviewing the current state of neuromechanical simulation and deep reinforcement learning for modeling the control of human locomotion. Their paper also presents the results of a scientific competition that pushed the limits of using deep reinforcement learning to produce motions, including movements such as walk-to-stand transitions, that had not previously been demonstrated before without using reference motion data. Read the publication

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Joy P. Ku, PhD

Deputy Director | Wu Tsai Human Performance Alliance at Stanford

Director of Education & Communications | Mobilize Center & Restore Center

Stanford University

650.736.8434 | joyku@stanford.edu

Supporting open-source biocomputational resources | OpenSim & SimTK