A Wearable Hybrid Robotic Suit for Self-Actualization and Well-Being

Motivation

Potential sites around the human body

Millions of elderly around the world will be diagnosed with a mobility impairment due to stroke, Parkinson’s disease, or a weak musculoskeletal system. There are over 50 million people in the US living with physical disabilities compromising mobility. We take many daily activities for granted – walking with a spouse or friend, playing with children, shopping in a store, using a bathroom on time and independently – until we have reduced mobility. Most importantly, reduced mobility leads to degenerative fitness and health problems. This proposal will focus on the challenges of improving mobility, due to its potential for high societal impact.

Concept

We propose a wearable hybrid robotic system that assists, enhances, and augments a person in their daily activities around the home and in the workplace in order to improve quality of life, increase productivity, and prolong independent living. Our approach focuses on three key research activities in order to achieve a wearable, hybrid system that works with its user to provide (a) alternate load pathways, (b) reconfigure itself for different activities, and (c) learn alongside its wearer to improve usability. First, we propose a biomechanics-based investigation of routine activities often seen in the workplace and in daily living to identify key opportunities for intervention. Second, we propose a low-cost and highly customizable design approach in which arrays of passive devices may be engaged or disengaged to provide dynamic and low-power support to the user as they transition through various activities. Finally, we propose to pair this wearable device with a machine learning approach called “mutual adaptation” in order to learn about – and reciprocally guide and train – the robotic device for more effective use and symbiosis with the wearer.

Facutly Leads

Dan Aukes

Assistant Professor
Ira A. Fulton Schools of Engineering

Heni Ben Amor

Assistant Professor
Ira A. Fulton Schools of Engineering

Hyunglae Lee

Associate Professor
Ira A. Fulton Schools of Engineering

Tom Sugar

Professor
Ira A. Fulton Schools of Engineering

Wenlong Zhang

Associate Professor
Ira A. Fulton Schools of Engineering

Student Researchers

• Masood Nevisipour, Ph.D., Postdoctoral Research Assistant
• Dongting Li, PhD Researcher
• Geoffrey Clark, PhD Researcher
• Michael Drolet, PhD Researcher
• Xiao Liu, PhD Researcher
• Emiliano Quinones Yumbla, PhD Researcher

Former Students:

• Chien-Wen Pan, PhD Researcher

Resources

Bibliography

• G. Clark, J. Campbell, M. Drolet, H. Ben Amor, “Learning Predictive Models for Ergonomic Control of Prosthetic Devices”, International Conference on Robot Learning (CoRL 2020)
• G. Clark, H. Ben Amor, “Learning Ergonomic Control in Human-RobotSymbiotic Walking”, Transactions on Robotics (TRO) - In Review
• G. Clark, Xiao Liu, H. Ben Amor, “An Environment-aware Predictive ModelingFramework for Human-Robot Symbiotic Walking”, RSS 2021 - In Review
• M. Nevisipour, T. Sugar, H. Lee, “Trunk movement control is critical during obstacle avoidance with a cognitive task,” Gait and Posture - In Review
• M. Nevisipour, T. Sugar, and H. Lee, “Trunk Control in Young Healthy Adults Requires Large Adaptations During and After Obstacle Avoidance with a Cognitive Task,” The 45th Annual Meeting of the American Society of Biomechanics (ASB 2021), August 2021, Atlanta (Virtual)

Talks and Presentations

• G. Clark, J. Campbell, M. Drolet, H. Ben Amor, “Learning Predictive Models for Ergonomic Control of Prosthetic Devices”, International Conference on Robot Learning (CoRL 2020)
• M. Nevisipour, T. Sugar, and H. Lee, “Trunk Control in Young Healthy Adults Requires Large Adaptations During and After Obstacle Avoidance with a Cognitive Task,” The 45th Annual Meeting of the American Society of Biomechanics (ASB 2021), August 2021, Atlanta (Virtual)

Datasets

Coming Soon

Software and Libraries

Coming Soon

Funding Provided by

External Links

• https://global.asu.edu/global-kaiteki-center