Opensim-announcement

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opensim-announcement@lists.simtk.org

November 2021

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Join team at Stanford University to help develop and disseminate tools for the biomechanics
by Scott L Delp 08 Nov '21

08 Nov '21

Dear Friends - I am the PI of two national centers funded by the National Institutes of Health: the Mobilize Center<https://mobilize.stanford.edu/> and the Restore Center<https://restore.stanford.edu/>. We are seeking to recruit a highly motivated Scientific Program Manager to oversee the scientific outreach programs for these two centers and accelerate the impact of mobile technologies and computational methods on physical rehabilitation and biomechanics research. This position requires a PhD in engineering, biology, computer science, or a related field, as well as excellent oral and written communication skills and analytical capabilities. Please feel free to apply or forward this announcement to any one who might be interested. Apply now: Research Engineer in School of Engineering, Stanford, California, United States<https://careersearch.stanford.edu/jobs/research-engineer-14873?et=18GbDCjiX> Best wishes - Scott Scott L. Delp James H. Clark Professor Schools of Engineering and Medicine Stanford University https://med.stanford.edu/profiles/scott-delp

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OpenSense webinar, OpenSim session at WCB, new teaching materials and data shared, and more
by Joy P. Ku 03 Nov '21

03 Nov '21

Webinar: OpenSense: Analyzing Motion with Inertial Measurement Unit (IMU) Data Tuesday, November 16, 2021, at 10:00 AM Pacific Time OpenSense<https://simtk-confluence.stanford.edu:8443/display/OpenSim/OpenSense+-+Kine…> is a new workflow within OpenSim<https://opensim.stanford.edu/> 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<https://mobilize.stanford.edu/webinar-opensense-analyzing-motion-with-inert…> 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<https://www.wcb2022.com/> 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<https://biomech.stanford.edu/>. 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<https://simtk-confluence-homeworks.stanford.edu:8443/display/BMH/Lectures> 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<https://simtk.org/projects/referentaccdata> 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<https://osim-rl.kidzinski.com/> 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<https://link.springer.com/epdf/10.1186/s12984-021-00919-y?sharing_token=A1X…> -- Joy P. Ku, PhD Deputy Director | Wu Tsai Human Performance Alliance at Stanford<https://humanperformance.stanford.edu/> Director of Education & Communications | Mobilize Center<https://mobilize.stanford.edu/> & Restore Center<https://restore.stanford.edu/> Stanford University 650.736.8434 | joyku(a)stanford.edu Supporting open-source biocomputational resources | OpenSim<https://opensim.stanford.edu/> & SimTK<https://simtk.org/>

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