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<p style="background:white"><b><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Webi<span style="background:white">nar: Op</span>enSense: Analyzing Motion with Inertial Measurement Unit (IMU) Data</span></b><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><i><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Tuesday, November 16, 2021, at 10:00 AM Pacific Time </span></i><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black"><a href="https://simtk-confluence.stanford.edu:8443/display/OpenSim/OpenSense+-+Kinematics+with+IMU+Data" target="_blank">OpenSense</a> is a new workflow
within <a href="https://opensim.stanford.edu/" target="_blank">OpenSim</a> 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. <a href="https://mobilize.stanford.edu/webinar-opensense-analyzing-motion-with-inertial-measurement-unit-imu-data/" target="_blank">Register
now</a> </span><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><b><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black"> </span></b><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><b><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Call for Abstracts for OpenSim Teaching Symposia at World Congress of Biomechanics </span></b><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><i><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Deadline to Submit: November 15, 2021 </span></i><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">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. <a href="https://www.wcb2022.com/" target="_blank">Learn more</a> <o:p></o:p></span></p>
<p style="background:white"><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black"> </span><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><b><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Teaching Materials to Accompany Biomechanics of Movement Textbook </span></b><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Homework problems and other teaching resources for the textbook <i>Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation </i>can
be found on the <a href="https://biomech.stanford.edu/" target="_blank">textbook companion website</a>. 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. <a href="https://simtk-confluence-homeworks.stanford.edu:8443/display/BMH/Lectures" target="_blank">Download these resources</a> </span><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><b><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black"> </span></b><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><b><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Accelerometry Data from Daily Life for Community-Dwelling Adults and Adults with Stroke </span></b><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">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. <a href="https://simtk.org/projects/referentaccdata" target="_blank">Learn
more and download data</a> </span><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black"> </span><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><b><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Deep Reinforcement Learning for Modeling Human Locomotion Control in Neuromechanical Simulation </span></b><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p style="background:white"><span style="font-size:12.0pt;font-family:"Segoe UI",sans-serif;color:black">Seungmoon Song from Stanford University and colleagues recently published a paper in the <i>Journal of NeuroEngineering and Rehabilitation </i>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
<a href="https://osim-rl.kidzinski.com/">scientific competition</a> 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. <a href="https://link.springer.com/epdf/10.1186/s12984-021-00919-y?sharing_token=A1X1k7GpJeMxvNG5U3fR22_BpE1tBhCbnbw3BuzI2RO_qjhPtrjSP--qE9_E0F66W4adRds_uvFjuhr9Cql-sdZnxSwInZ4BRzNbFWgrRVEN8XDLIsfoZbPCtFSES8pcssSszooxbuFvFRePN4Xr7kQ9ZxSFflHcqPRPUvOic4o%3D" target="_blank">Read
the publication</a> </span><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:9.0pt;color:#A6A6A6">--<o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:9.0pt;color:#767171">Joy P. Ku, PhD</span></b><span style="font-size:9.0pt;color:#767171"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;color:gray">Deputy Director | <a href="https://humanperformance.stanford.edu/">
<span style="color:gray">Wu Tsai Human Performance Alliance at Stanford</span></a>
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;color:gray">Director of Education & Communications |
<a href="https://mobilize.stanford.edu/"><span style="color:gray">Mobilize Center</span></a> &
<a href="https://restore.stanford.edu/"><span style="color:gray">Restore Center</span></a>
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;color:gray">Stanford University<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;color:gray"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;color:gray">650.736.8434 | joyku@stanford.edu<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;color:gray">Supporting open-source biocomputational resources |
</span><span style="font-size:9.0pt;color:gray"><a href="https://opensim.stanford.edu/"><span style="color:gray">OpenSim</span></a></span><span style="font-size:9.0pt;color:gray"> &
<a href="https://simtk.org/"><span style="color:gray">SimTK</span></a><o:p></o:p></span></p>
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