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Yip M, Salcudean S, Goldberg K, Althoefer K, Menciassi A, Opfermann JD, Krieger A, Swaminathan K, Walsh CJ, Huang HH, Lee IC. Artificial intelligence meets medical robotics. Science 2023; 381:141-146. [PMID: 37440630 DOI: 10.1126/science.adj3312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023]
Abstract
Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.
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Affiliation(s)
- Michael Yip
- Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Septimiu Salcudean
- Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Ken Goldberg
- Department of Industrial Engineering and Operations Research and Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Kaspar Althoefer
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Arianna Menciassi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, Pisa, Italy
| | - Justin D Opfermann
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Axel Krieger
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Krithika Swaminathan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Conor J Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - He Helen Huang
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - I-Chieh Lee
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Gehlhar R, Tucker M, Young AJ, Ames AD. A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses. ANNUAL REVIEWS IN CONTROL 2023; 55:142-164. [PMID: 37635763 PMCID: PMC10449377 DOI: 10.1016/j.arcontrol.2023.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Lower-limb prostheses aim to restore ambulatory function for individuals with lower-limb amputations. While the design of lower-limb prostheses is important, this paper focuses on the complementary challenge - the control of lower-limb prostheses. Specifically, we focus on powered prostheses, a subset of lower-limb prostheses, which utilize actuators to inject mechanical power into the walking gait of a human user. In this paper, we present a review of existing control strategies for lower-limb powered prostheses, including the control objectives, sensing capabilities, and control methodologies. We separate the various control methods into three main tiers of prosthesis control: high-level control for task and gait phase estimation, mid-level control for desired torque computation (both with and without the use of reference trajectories), and low-level control for enforcing the computed torque commands on the prosthesis. In particular, we focus on the high- and mid-level control approaches in this review. Additionally, we outline existing methods for customizing the prosthetic behavior for individual human users. Finally, we conclude with a discussion on future research directions for powered lower-limb prostheses based on the potential of current control methods and open problems in the field.
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Affiliation(s)
- Rachel Gehlhar
- Department of Mechanical and Civil Engineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, 91125, CA, USA
| | - Maegan Tucker
- Department of Mechanical and Civil Engineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, 91125, CA, USA
| | - Aaron J Young
- Woodruff School of Mechanical Engineering, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
- Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Aaron D Ames
- Department of Mechanical and Civil Engineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, 91125, CA, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, 1200 E. California Blvd., Pasadena, 91125, CA, USA
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Reznick E, Welker CG, Gregg RD. Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 3:211-217. [PMID: 36819935 PMCID: PMC9928215 DOI: 10.1109/ojemb.2023.3234431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/23/2022] [Accepted: 12/29/2022] [Indexed: 06/15/2024] Open
Abstract
Goal: Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved conditions. Methods: Kinematic individuality-how one person's joint angles differ from the group-is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes, or whether a mode-specific prediction (based on a single task for each mode) is significantly more effective. Results: Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, modal individualization improved the fit in 81% of trials, improving the fit on average by 4.3[Formula: see text] across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. Conclusions: For walking and running, kinematic individuality can be easily generalized within mode, but the trends are mixed on stairs depending on joint.
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Affiliation(s)
- Emma Reznick
- Department of RoboticsUniversity of MichiganAnn ArborMI48109USA
| | - Cara Gonzalez Welker
- Department of Mechanical EngineeringUniversity of Colorado BoulderBoulderCO80309USA
| | - Robert D. Gregg
- Department of RoboticsUniversity of MichiganAnn ArborMI48109USA
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Fylstra BL, Lee IC, Li M, Lewek MD, Huang H. Human-prosthesis cooperation: combining adaptive prosthesis control with visual feedback guided gait. J Neuroeng Rehabil 2022; 19:140. [PMID: 36517814 PMCID: PMC9753428 DOI: 10.1186/s12984-022-01118-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Personalizing prosthesis control is often structured as human-in-the-loop optimization. However, gait performance is influenced by both human control and intelligent prosthesis control. Hence, we need to consider both human and prosthesis control, and their cooperation, to achieve desired gait patterns. In this study, we developed a novel paradigm that engages human gait control via user-fed visual feedback (FB) of stance time to cooperate with automatic prosthesis control tuning. Three initial questions were studied: (1) does user control of gait timing (via visual FB) help the prosthesis tuning algorithm to converge faster? (2) in turn, does the prosthesis control influence the user's ability to reach and maintain the target stance time defined by the feedback? and (3) does the prosthesis control parameters tuned with extended stance time on prosthesis side allow the user to maintain this potentially beneficial behavior even after feedback is removed (short- and long-term retention)? METHODS A reinforcement learning algorithm was used to achieve prosthesis control to meet normative knee kinematics in walking. A visual FB system cued the user to control prosthesis-side stance time to facilitate the prosthesis tuning goal. Seven individuals without amputation (AB) and four individuals with transfemoral amputation (TFA) walked with a powered knee prosthesis on a treadmill. Participants completed prosthesis auto-tuning with three visual feedback conditions: no FB, self-selected stance time FB (SS FB), and increased stance time FB (Inc FB). The retention of FB effects was studied by comparing the gait performance across three different prosthesis controls, tuned with different visual FB. RESULTS (1) Human control of gait timing reduced the tuning duration in individuals without amputation, but not for individuals with TFA. (2) The change of prosthesis control did not influence users' ability to reach and maintain the visual FB goal. (3) All participants increased their prosthesis-side stance time with the feedback and maintain it right after feedback was removed. However, in the post-test, the prosthesis control parameters tuned with visual FB only supported a few participants with longer stance time and better stance time symmetry. CONCLUSIONS The study provides novel insights on human-prosthesis interaction when cooperating in walking, which may guide the future successful adoption of this paradigm in prosthesis control personalization or human-in-the-loop optimization to improve the prosthesis user's gait performance.
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Affiliation(s)
- Bretta L. Fylstra
- grid.40803.3f0000 0001 2173 6074Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695 USA ,grid.10698.360000000122483208Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - I-Chieh Lee
- grid.40803.3f0000 0001 2173 6074Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695 USA ,grid.10698.360000000122483208Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Minhan Li
- grid.40803.3f0000 0001 2173 6074Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695 USA ,grid.10698.360000000122483208Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Michael D. Lewek
- grid.10698.360000000122483208Division of Physical Therapy, UNC Chapel Hill, Chapel Hill, NC 27599 USA
| | - He Huang
- grid.40803.3f0000 0001 2173 6074Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695 USA ,grid.10698.360000000122483208Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
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Liu W, Zhong J, Wu R, Fylstra BL, Si J, Huang HH. Inferring Human-Robot Performance Objectives During Locomotion Using Inverse Reinforcement Learning and Inverse Optimal Control. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3143579] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Nasr A, Hashemi A, McPhee J. Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation. ROBOTICS 2022; 11:20. [PMID: 35910714 PMCID: PMC8989382 DOI: 10.3390/robotics11010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 01/26/2022] [Indexed: 11/16/2022] Open
Abstract
The closed-loop human–robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot’s dynamics and external load compared to the human limb free movement. The human–robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human–robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.
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Affiliation(s)
- Ali Nasr
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (A.H.); (J.M.)
| | - Arash Hashemi
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (A.H.); (J.M.)
| | - John McPhee
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (A.H.); (J.M.)
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Liu W, Wu R, Si J, Huang H. A New Robotic Knee Impedance Control Parameter Optimization Method Facilitated by Inverse Reinforcement Learning. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3194326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Wentao Liu
- UNC/NCSU Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA
| | - Ruofan Wu
- School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA
| | - Jennie Si
- School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA
| | - He Huang
- UNC/NCSU Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA
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Deshpande AD. Novel Biomedical Technologies: Rehabilitation Robotics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2022. [DOI: 10.1016/j.cobme.2022.100371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Hashemi A, McPhee J. Assistive Sliding Mode Control of a Rehabilitation Robot with Automatic Weight Adjustment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4891-4896. [PMID: 34892305 DOI: 10.1109/embc46164.2021.9631110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There are approximately 13 million new stroke cases worldwide each year. Research has shown that robotics can provide practical and efficient solutions for expediting post-stroke patient recovery. This simulation study aimed to design a sliding mode controller (SMC) for an end-effector-based rehabilitation robot. A genetic algorithm (GA) was designed for automatic controller weight adjustment. The optimal weights were obtained by minimizing a cost function comprising the end-effector position error, robot input, robot input-rate, and patient input. To promote safe tuner optimization, a model of the human arm was incorporated to generate the human joint torque. A computed-torque proportional derivative controller (CTPD) was designed for the human arm to approximate the central nervous system (CNS) motor control. This controller was adjusted to simulate rehabilitation effects and patient adaptation. The tuner was optimized for a trajectory tracking task with an assistive high-level control scheme. The simulation results showed lower cost compared to seven manual weight settings. The optimal weights provided good tracking performance and suitable robot inputs. This research provides a framework to conduct various simulations before testing our controller on human subjects. The preliminary results of this study will be used as the starting point for online adaptive controller tuning, which will be examined in our future research.
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Nalam V, Huang HH. Empowering prosthesis users with a hip exoskeleton. Nat Med 2021; 27:1677-1678. [PMID: 34635853 DOI: 10.1038/s41591-021-01529-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Varun Nalam
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA.,Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - He Helen Huang
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA. .,Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Hu D, Wang S, Li B, Liu H, He J. Spinal Cord Injury-Induced Changes in Encoding and Decoding of Bipedal Walking by Motor Cortical Ensembles. Brain Sci 2021; 11:brainsci11091193. [PMID: 34573213 PMCID: PMC8469283 DOI: 10.3390/brainsci11091193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 11/24/2022] Open
Abstract
Recent studies have shown that motor recovery following spinal cord injury (SCI) is task-specific. However, most consequential conclusions about locomotor functional recovery from SCI have been derived from quadrupedal locomotion paradigms. In this study, two monkeys were trained to perform a bipedal walking task, mimicking human walking, before and after T8 spinal cord hemisection. Importantly, there is no pharmacological therapy with nerve growth factor for monkeys after SCI; thus, in this study, the changes that occurred in the brain were spontaneous. The impairment of locomotion on the ipsilateral side was more severe than that on the contralateral side. We used information theory to analyze single-cell activity from the left primary motor cortex (M1), and results show that neuronal populations in the unilateral primary motor cortex gradually conveyed more information about the bilateral hindlimb muscle activities during the training of bipedal walking after SCI. We further demonstrated that, after SCI, progressively expanded information from the neuronal population reconstructed more accurate control of muscle activity. These results suggest that, after SCI, the unilateral primary motor cortex could gradually regain control of bilateral coordination and motor recovery and in turn enhance the performance of brain–machine interfaces.
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Affiliation(s)
- Dingyin Hu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100081, China;
- Correspondence:
| | - Shirong Wang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100081, China;
| | - Bo Li
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
| | - Honghao Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
| | - Jiping He
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100081, China;
- Center for Neural Interface Design, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 86287, USA
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