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.
Issues Involved or Addressed
Lab research is divided into multidisciplinary research subteams of graduate and undergraduate students. Each team is led by at least one graduate student mentor who directs the team to assist in their primary research goals. To best ensure meaningful contributions, each VIP member is selected for a specific subteam. As an applicant, we ask that you first review the subteams (listed below) and reach out to the contacts corresponding to your project(s) of interest. Successfully contacting the graduate student mentors before applying to VIP greatly helps us in determining who to accept! When you submit your application, please indicate the subteam of interest in the comment box.
Virtual Reality & Exoskeleton Team
This team will primarily focus on developing the testing platform for a hip exoskeleton to explore if this system can assist a user to evade moving threats. We tackle this through three key research areas.
- Situational Awareness enhancement: our current experiment investigates the influence of AI on human performance in multiple threat scenarios to quantify the benefits of intelligent path planning combined with human intuition. We create virtual reality games to play and evaluate performance metrics.
- Intent recognition: to understand once we have alerted a person, how do we extract their intended movement through wearable sensor in tandem with sensor fusion and machine learning techniques.
- Hip exoskeleton: we have developed a hip exoskeleton system and students will work on a redesign to make the system significantly more lightweight.
Major short-term goals which undergraduate students can tackle include component design, fabrication, and testing; exo UI programming and control, virtual environment experimentation; and system integration/processing for motion capture. We are seeking students who have interest and/or experience in Unity, C#, virtual reality, AI/machine learning, microprocessors/controllers, sensors, mechatronic design, and SolidWorks. Some of the majors we will be recruiting for are ECE, CS, BME, and ME.
Project contact: Aakash Bajpai
Multi-objective Optimization of Torque Assistance (MOTA) Team
This team works to understand how to provide individualized exoskeleton (exo) assistance for various performance measures. Students will be exposed to streamlining and increasing robustness exo mechanical hardware, controllers, and experimentation. With reliable exos, we enhance walking while collecting walking speed, motion capture (kinematics and kinetics of motion), EMG (muscular activation), and metabolics (energetic cost). You will work with various populations (able-bodied younger & older adults) assisting with data collection and analysis. Each student will be assigned to subgroup that specializes in various aspects of the project based on their skills.
We are currently in need of team members experienced/interested in
- Matlab (basic programming, structures, and visualization)
- Solidworks (CAD, FEA, 3D printing, machining)
Project contact: Ben Shafer
Human Balance Augmentation Team
Description: This team aims to understand how humans maintain their balance in unsteady environments and how balance can be augmented using a robotic hip exoskeleton. The team uses our lab’s CAREN system, a treadmill mounted on a Stewart platform, to collect biomechanics information following rapid perturbations that cause a loss of balance. We are using this information to understand the biomechanics/neural control of agile balance recovery and predict disruptions to balance using machine learning. The team is also designing and testing a new 2-degree-of-freedom hip exoskeleton that will be used to study balance augmentation. Students on this team have the opportunity to assist in human subject experiments, analyze biomechanical and neural control data, develop machine learning models to predict loss of balance, design and implement mechatronics, create exoskeleton controllers, and test a robotic exoskeleton. Students who have or would like to gain experience in these areas would be a great fit for this team! Experience with Matlab, mechatronics design/implementation, machine learning, Python, and/or controls are preferred. Students from all disciplines are welcome to apply.
Project Contact: Jennifer Leestma
Robotic Hip Exoskeleton Team
Description:The robotic hip exoskeleton team primarily focuses on implementing intellectual controllers to understand the human intent using during locomotion. The team implements state-of-the-art machine learning algorithms to better understand what the exoskeleton user is trying to do (e.g., ambulation modes, walking speed) and provide natural and seamless assistance. Prospective students joining the team will learn how to utilize different on-board mechanical sensors from the exoskeleton to develop a machine learning model that can be deployed to a real-time robot. Additionally, students will be exposed to learn different engineering skills such as human biomechanics using motion capture system, muscle signal analysis, and measuring human energetic cost. Students will be exposed to an interdisciplinary aspect of the project, such as biomechanical analysis, modeling/simulation software, mechatronic design/fabrication of an actuator, and integrating artificial intelligence for controlling the exoskeleton.
Project contact: Dongho Park
Pediatric Knee Exoskeleton
Description: The goal of this project is to promote/enhance walking rehabilitation for children with walking disabilities. The team will be focusing on human-subject experiments to investigate the effect of gait assistance using a robotic knee exoskeleton. Additionally, the team will investigate developing techniques to understand walking environmental conditions using machine-learning approaches. The data collected from the experiment will include 3-D motion capture, muscle activity, device performance, and user feedback which facilitate biomechanical analysis. Students from BME, ME, and ECE backgrounds are all suitable for the project. Additionally, students with skills and knowledge in machining, LabVIEW, Matlab, circuit design, or biomechanics will be helpful.
Project contact: Dawit Lee
Description: This team will be dedicated to develop and validate different control strategies on a knee and ankle prosthetic device. The main goal involves creating a user independent system to allow individuals with transfemoral amputation to improve their ambulation in common community tasks. We will be collecting data from different sensors which included embedded mechanical sensors (IMU’s and encoders), EMG signals, motion capture, and metabolics on a variety of terrain.
Skills that will be looked but not limited to include strong programming skills (C++, Python, Qt, & ROS). Applicants are encouraged to become Invention Studio or HIVE PI’s. We are looking for highly motivated students specifically from CS.
Project contact: Jairo Maldonado
DOE/ Back Exoskeleton Team
This team is dedicated to investigating how to use back, knee, and ankle exoskeletons to reduce muscle strain and fatigue while performing physically demanding manual labor tasks.
Undergraduate involvement consists of two main components: experimentation and development. Experimentation involves assisting in human-subject experimentation and sensor data analysis, as well as piloting exosuit controllers. Development includes developing exoskeleton controllers, improving machine learning-based human state estimators, and working at a mechatronics level to improve hardware. Undergraduate students are expected to have previous experience in embedded programming, machine learning, mechanical design, mechatronics, and/or biomechanics. Previous experience in Python, Matlab, C++, and/or SolidWorks is also encouraged.
Project contact: Christoph Nuesslein
Methods and Technologies
Academic Majors of Interest
Preferred Interests and Preparation
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.
Meeting Schedule & Location
- Mechanical Engineering