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Wearable Robotics

Physical assistance from wearable robots has the potential to improve our quality of life. Achieving positive health outcomes requires a multidisciplinary approach to the robot design and evaluation process. In this class, we will introduce some of the biomechanical and mechatronic principles to guide this process. The main topics include compliant actuation methods for exoskeletons, exosuits, and robotic prostheses; dynamic models of the human body and wearable robot; hierarchical control through state variables, finite-state machines, and impedance control; optimization for mechanical design, real-time control, and real-time estimation; and methods for the evaluation of health outcomes. The learning outcomes in this class include:

  1. Model and analyze human movement through kinematic and kinetic principles.

  2. Select electric motors that maximize physical assistance while reducing robot mass.

  3. Formulate the design and control of wearable robots as optimization problems that converge to global solutions or can be solved in real time.

  4. Understand relevant scientific literature and summarize its outcomes.

  5. Understand control methods for lower-limb wearable robots.

Diagram describing the main content of this Wearable Robotics course: Analysis of human movement, generation of movement, optimization in the context of wearable robots, and control of wearable robots.
This wearable robotics course focuses on the analysis of human movement, generation of movement, optimization in the context of wearable robots, and control of wearable robots.

Content and link to notes

Lec. Section Topics Project Article Reading Link to Notes
1   Introduction, nomenclature, and motivation
M1: Find peer advisee
   Link
2
Analysis of Human Movement - define your functional outcome
 Quantifying Movement Winter, David A. “Biomechanical Motor Patterns in Normal Walking.” Journal of Motor Behavior 15, no. 4 (December 1983): 302–30. https://doi.org/10.1080/00222895.1983.10735302. Link
3 Kinematics of Sit to Stand - OpenSim, IMUs, and Optical Motion Capture System   Link
4 Inverse Dynamics
M2: Define the project and 1 to 3 aims
 Roebroeck, M.E., C.A.M. Doorenbosch, J. Harlaar, R. Jacobs, and G.J. Lankhorst. “Biomechanics and Muscular Activity during Sit-to-Stand Transfer.” Clinical Biomechanics 9, no. 4 (July 1994): 235–44. https://doi.org/10.1016/0268-0033(94)90004-3. Link
5 Kinetics of Sit to Stand - OpenSim, Force Plates, Instrumented Insoles, and Load Cells   Link
6 Muscle Force Optimization Zajac, F. E. “Muscle and Tendon: Properties, Models, Scaling, and Application to Biomechanics and Motor Control.” Critical Reviews in Biomedical Engineering 17, no. 4 (1989): 359–411. Link
7 Muscle Coordination during Sit to Stand - OpenSim, Muscle Optimization, and Electromyography   Link
8
Generation of movement
 Design Principles: the effect of distal mass and repetitive loading. Brushed DC motors Browning, Raymond C., Jesse R. Modica, Rodger Kram, and Ambarish Goswami. “The Effects of Adding Mass to the Legs on the Energetics and Biomechanics of Walking.” Medicine & Science in Sports & Exercise 39, no. 3 (March 2007): 515–25. https://doi.org/10.1249/mss.0b013e31802b3562. Link
9 Selection of brushed DC motors - Simulink/Simscape   Link
10 Permanent magnet synchronous motors
M3: Deliverable related to the analysis of human movement
 Lee, Ung Hee, Chen-Wen Pan, and Elliott J. Rouse. “Empirical Characterization of a High-Performance Exterior-Rotor Type Brushless DC Motor and Drive.” In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 8018–25. Macau, China: IEEE, 2019. https://doi.org/10.1109/IROS40897.2019.8967626. Link
11 Permanent magnet synchronous motors - Simulink/Simscape   Link
12 IROS23 - The role of series and parallel elasticity (Video lecture) Bolivar-Nieto, Edgar A., Gray C. Thomas, Elliott Rouse, and Robert D. Gregg. “Convex Optimization for Spring Design in Series Elastic Actuators: From Theory to Practice.” In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9327–32. Prague, Czech Republic: IEEE, 2021. https://doi.org/10.1109/IROS51168.2021.9636427. Link
13 IROS23 - The role of series and parallel elasticity - Simulink/Simscape (At home experiment)   Link
14 Transmission of mechanical power, modeling of winding temperature, and heat losses Grosu, Svetlana, Laura De Rijcke, Victor Grosu, Joost Geeroms, Bram Vanderboght, Dirk Lefeber, and Carlos Rodriguez-Guerrero. “Driving Robotic Exoskeletons Using Cable-Based Transmissions: A Qualitative Analysis and Overview.” Applied Mechanics Reviews 70, no. 6 (November 1, 2018): 060801. https://doi.org/10.1115/1.4042399. Link
15
Optimization in the context of wearable robots
 Convex Optimization (review pre-requisites video) Introduction - Boyd, Stephen P., and Lieven Vandenberghe. Convex Optimization. Cambridge, UK ; New York: Cambridge University Press, 2004. Link
16 Is this a convex or non-convex optimization program?   Link
17 Robust Optimization - The Usefulness of the Lagrange Dual Preface - Ben-Tal, A., Laurent El Ghaoui, and A. S. Nemirovskiĭ. Robust Optimization. Princeton Series in Applied Mathematics. Princeton: Princeton University Press, 2009. Link
18 Practice robust counterparts   Link
19 Optimization in Real-Time
M4: Deliverable related to the generation of movement
 Mattingley, John, and Stephen Boyd. “Real-Time Convex Optimization in Signal Processing.” IEEE Signal Processing Magazine 27, no. 3 (May 2010): 50–61. https://doi.org/10.1109/MSP.2010.936020. Link
20 Kalman filter - Generation of Trajectories - Inverse Kinematics   Link
21
Control of wearable robots
 Control methods for wearable robots: Impedance, Admitance, State Machinces, and Phase Variables Tucker, Michael R, Jeremy Olivier, Anna Pagel, Hannes Bleuler, Mohamed Bouri, Olivier Lambercy, José del R Millán, Robert Riener, Heike Vallery, and Roger Gassert. “Control Strategies for Active Lower Extremity Prosthetics and Orthotics: A Review.” Journal of NeuroEngineering and Rehabilitation 12, no. 1 (2015): 1. https://doi.org/10.1186/1743-0003-12-1. Link
22 Phase variable control of a knee exoskeleton to support sit-to-stand motion and walking   Link
23 Analysis of Human Movement II Walking M5: Deliverable related to the control of wearable robots Hof, At L. “The ‘Extrapolated Center of Mass’ Concept Suggests a Simple Control of Balance in Walking.” Human Movement Science 27, no. 1 (February 2008): 112–25. https://doi.org/10.1016/j.humov.2007.08.003. Link
26
Case studies
 Exam   Azocar, Alejandro F., Luke M. Mooney, Jean-François Duval, Ann M. Simon, Levi J. Hargrove, and Elliott J. Rouse. “Design and Clinical Implementation of an Open-Source Bionic Leg.” Nature Biomedical Engineering 4, no. 10 (October 2020): 941–53. https://doi.org/10.1038/s41551-020-00619-3.  
27 Summary through Case Studies: The Utah Leg and a Passive Exoskeleton that Reduces Metabolic Energy - (Invited Speaker: Ray Browning, Ph.D., CEO & Co-Founder at BIOMOTUM, Inc.)   Collins, Steven H., M. Bruce Wiggin, and Gregory S. Sawicki. “Reducing the Energy Cost of Human Walking Using an Unpowered Exoskeleton.” Nature 522, no. 7555 (June 2015): 212–15. https://doi.org/10.1038/nature14288.  
28   Class project presentations (Invited Speaker: Gwen Bryan, Ph.D., Research Scientist at IHMC)      

*Note: this class was first offered in 2023. The expected class content on the table may be different as the content in the course notes.

Acknowledgements

This course was possible thanks to David Kelly, student of our Ph.D. program in Aerospace and Mechanical engineering. David served as Teaching Assistant in the Fall 2023.

The content of this course follows the material from the book Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation. Cambridge, Massachusetts: The MIT Press, 2020 by Uchida, Thomas K., Scott Delp, and David B. Delp (link to content).

The organization and content of this course was inspired by the course BIOMEDE 646 (MECHENG 646) (ROB 646). Locomotion Mechanics and Design/Control of Wearable Robotic Systems from Elliott Rouse at the University of Michigan (link to his Neurobionics Lab).