Robotic Human Augmentation
GOALS: The EPIC (Exoskeleton and Prosthesis Intelligent Controls) Lab research areas include automation & mechatronics and bioengineering with a focus on the control of powered robotic prostheses and exoskeletons to assist human movement. We implement biological signal processing, intent recognition, and control systems based on EMG and mechanical sensors to improve human-machine capabilities. Our primary goal is to use robotic augmentation technology to restore human movement to individuals with mobility disability.
METHODS & TECHNOLOGIES: Control Systems, Mechatronics, Machine Learning, Signal Processing, Data Analysis, Embedded Programming, Human Subject Testing, Mechanical Design, Human Biomechanics, Maching.
RESEARCH/DESIGN ISSUES: Our research focuses on optimizing the human machine interface with robotic prostheses and exoskeletons. Much of this involves learning control methods to allow a human user to efficiently operate and work in sync with an assistive device. This involves extracting user intent through sensor fusion and machine learning algorithms along with other states such as gait phase, locomotion mode, and walking speed. We then optimize robotic walking profiles to assist the human user. We focus our application with human user tests on individuals with walking disability such as amputees, stroke subjects, elderly subjects, and others.
MEETING TIME: Thurs, 8:00-8:50
ADVISORS: Aaron Young (ME)
PARTNERS & SPONSORS: Department of Defense
MAJORS, PREPARATION AND INTERESTS:
As the research is an interdisciplinary research, wide range of skills and experiences will be highly applicable to the study. Some of the core skills will include mechanical design, machining, mechatronics, embedded and MATLAB programming, and signal processing. Below are examples of specific skills that would be related to each major.
ME - Background/interest in mechanical design and machining for fast prototyping. Training in Montgomery machining mall for CNC, mill, lathe, 3D printer is recommended.
EE/CompE - Background/interest in mechatronics and electrical circuitry design. Knowledge in circuit design and signal processing is recommended
CS - Background/interest in software development for embedded programming. Knowledge/interest in language (C, Labview, MATLAB, python) is recommended.
BME - Background/interest in clinical testing for device evaluation. Knowledge/interest in human biomechanics and design of biomedical robotic devices/controllers as human and biological data processing is recommended.
CONTACT: Professor Aaron Young, firstname.lastname@example.org
AWARDS: Won first and third place in 2018 VIP Innovation Competition's Robotics Track