AIRS in the AIR

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  张添威

  AIRS 智能机器人中心副研究员

  执行主席

  嘉宾介绍

  个人简介
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  Emel Demircan

  加州州立大学长滩分校副教授

  Human Movement Understanding for Intelligent Robots and Systems

  嘉宾介绍

  Dr. Emel Demircan is an Associate Professor at the Departments of Mechanical and Aerospace Engineering and Biomedical Engineering at California State University, Long Beach. Dr. Demircan obtained her Ph.D in Mechanical Engineering from Stanford University in 2012. She was a postdoctoral scholar at Stanford from 2012 to 2014 and a visiting assistant professor at the University of Tokyo from 2014 to 2015. She was also a part-time scientist at Lucile Salter Packard Children's Hospital Gait Analysis Lab at Stanford University. Dr. Demircan's research focuses on the application of dynamics and

  control theory for the simulation and analysis of biomechanical and robotic systems. Her research interests include cyber-physical systems, rehabilitation robotics, sports biomechanics, natural motion generation in humanoid robotics, and human motion synthesis. Dr. Demircan is an OpenSim Fellow; and the founder & co-chair of the IEEE RAS Technical Committee on "Human Movement Understanding." She is actively collaborating with clinical, athletic, and industrial partners; and she is involved in professional and educational activities within the IEEE Robotics Society where she serves as the VP of Conference Operations.

  个人简介

  Human motor performance is a key area of investigation in biomechanics, robotics, and machine learning. Understanding human neuromuscular control is important to synthesize prosthetic motions and ensure safe human-robot interaction. Building controllable biomechanical models through modeling and algorithmic tools from both robotics and biomechanics increases our scientific understanding of musculoskeletal mechanics and control. The resulting models can consequently help quantifying the characteristics of a subject’s motion and in designing effective treatments, like predictive simulations and motion training. My objective is to explore how neural control dictates motor performance in humans by developing a portable, soft, cyberphysical system and a computational framework - which incorporates real-time roboticsbased control, AI-based perception and learning, and OpenSim’s musculoskeletal models. In this talk, I will present the modeling, control, and simulation components of this new framework with two examples on human manipulation and locomotion skills. The presented framework has promise to advance the field of rehabilitation robotics by deepening our scientific understanding of human motor performance dictated by musculoskeletal physics and neural control. Automated and real-time motion improvement and retraining, facilitated with such frameworks, promise to transform the neuromuscular health, longevity, and independence of millions of people, utilizing a cost effective approach.

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  Emiko Uchiyama

  东京大学助理教授

  Fall Prevention of the Elderly from Surveying Their Perceptional and Physical Abilities

  嘉宾介绍

  Emiko Uchiyama is an Assistant Professor at Department of Mechanical Engineering, School of Engineering, University of Tokyo. She was born in Tokyo, Japan, in 1990. She received the B.S and M.S degrees from the University of Tokyo, Japan, in engineering in 2014 and 2016, Ph.D degree from the University of Tokyo, Japan, in 2019. She was JSPS research fellow from 2018 to 2020, Project Assistant Professor at the University of Tokyo from 2019 to 2020, Assistant Professor at Tokyo Tech from 2020 to 2022. Her field of research includes human understandings, rehabilitation robotics, biomechanics, and cognitive science. She is a member of IEEE, Robotics Society of Japan.

  个人简介

  In the robotics and AI field, monitoring the elderly lives and detecting risky motions for their safety have been major topics. However, the speaker has focused on modeling the elderly’s perception and motion changes so that robots and AI systems can understand the status of the elderly more precisely. The speaker has been involving work about fall prevention topic for over 5 years. The work includes various fields of research such as interview research to the patients who have experienced femoral neck fractures due to falls, biomechanics research, and informatics research. The speaker has constructed a research idea from interviews with the people concerned of falls. Our work has proposed a modeling method of the depth perception, which is one of the stumble risk factors. In the talk, the works about modeling depth perception and its effects on manipulating legs during approaching an object will be introduced. Why the speaker focused on the perceptional abilities of the elderly and how it is modeled through integrating results of cognitive tasks and motion capture trials will be mainly explained.

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