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Zhang Y, Zhao W, Wan C, Wu X, Huang J, Wang X, Huang G, Ding W, Chen Y, Yang J, Su B, Xu Y, Zhou Z, Zhang X, Miao F, Li J, Li Y. Exoskeleton rehabilitation robot training for balance and lower limb function in sub-acute stroke patients: a pilot, randomized controlled trial. J Neuroeng Rehabil 2024; 21:98. [PMID: 38851703 PMCID: PMC11162020 DOI: 10.1186/s12984-024-01391-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/30/2024] [Indexed: 06/10/2024] Open
Abstract
PURPOSE This pilot study aimed to investigate the effects of REX exoskeleton rehabilitation robot training on the balance and lower limb function in patients with sub-acute stroke. METHODS This was a pilot, single-blind, randomized controlled trial. Twenty-four patients with sub-acute stroke (with the course of disease ranging from 3 weeks to 3 months) were randomized into two groups, including a robot group and a control group. Patients in control group received upright bed rehabilitation (n = 12) and those in robot group received exoskeleton rehabilitation robot training (n = 12). The frequency of training in both groups was once a day (60 min each) for 5 days a week for a total of 4 weeks. Besides, the two groups were evaluated before, 2 weeks after and 4 weeks after the intervention, respectively. The primary assessment index was the Berg Balance Scale (BBS), whereas the secondary assessment indexes included the Fugl-Meyer Lower Extremity Motor Function Scale (FMA-LE), the Posture Assessment Scale for Stroke Patients (PASS), the Activities of Daily Living Scale (Modified Barthel Index, MBI), the Tecnobody Balance Tester, and lower extremity muscle surface electromyography (sEMG). RESULTS The robot group showed significant improvements (P < 0.05) in the primary efficacy index BBS, as well as the secondary efficacy indexes PASS, FMA-LE, MBI, Tecnobody Balance Tester, and sEMG of the lower limb muscles. Besides, there were a significant differences in BBS, PASS, static eye-opening area or dynamic stability limit evaluation indexes between the robotic and control groups (P < 0.05). CONCLUSIONS This is the first study to investigate the effectiveness of the REX exoskeleton rehabilitation robot in the rehabilitation of patients with stroke. According to our results, the REX exoskeleton rehabilitation robot demonstrated superior potential efficacy in promoting the early recovery of balance and motor functions in patients with sub-acute stroke. Future large-scale randomized controlled studies and follow-up assessments are needed to validate the current findings. CLINICAL TRIALS REGISTRATION URL: https://www.chictr.org.cn/index.html.Unique identifier: ChiCTR2300068398.
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Affiliation(s)
- Yuting Zhang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weiwei Zhao
- Wuxi Central Rehabilitation Hospital, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Chunli Wan
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xixi Wu
- Nanjing Medical University, Nanjing, China
| | | | - Xue Wang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guilan Huang
- Wuxi Central Rehabilitation Hospital, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Wenjuan Ding
- Wuxi Central Rehabilitation Hospital, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Yating Chen
- Wuxi Central Rehabilitation Hospital, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Jinyu Yang
- Wuxi Central Rehabilitation Hospital, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Bin Su
- Wuxi Central Rehabilitation Hospital, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Yi Xu
- Wuxi MaxRex Robotic Exoskeleton Limited, Wuxi, China
| | - Zhengguo Zhou
- Wuxi MaxRex Robotic Exoskeleton Limited, Wuxi, China
| | - Xuting Zhang
- Wuxi MaxRex Robotic Exoskeleton Limited, Wuxi, China
| | - Fengdong Miao
- Wuxi MaxRex Robotic Exoskeleton Limited, Wuxi, China
| | - Jianan Li
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yongqiang Li
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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2
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Luo S, Jiang M, Zhang S, Zhu J, Yu S, Dominguez Silva I, Wang T, Rouse E, Zhou B, Yuk H, Zhou X, Su H. Experiment-free exoskeleton assistance via learning in simulation. Nature 2024; 630:353-359. [PMID: 38867127 DOI: 10.1038/s41586-024-07382-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/03/2024] [Indexed: 06/14/2024]
Abstract
Exoskeletons have enormous potential to improve human locomotive performance1-3. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws2. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals.
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Affiliation(s)
- Shuzhen Luo
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
- Department of Mechanical Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
| | - Menghan Jiang
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Sainan Zhang
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Junxi Zhu
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Shuangyue Yu
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Israel Dominguez Silva
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Tian Wang
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Elliott Rouse
- Neurobionics Lab, Department of Robotics, Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Bolei Zhou
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Hyunwoo Yuk
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Xianlian Zhou
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Hao Su
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
- Joint NCSU/UNC Department of Biomedical Engineering, North Carolina State University, Raleigh, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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3
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Voloshina AS. Robotic exoskeleton adapts to its wearer through simulated training. Nature 2024; 630:311-312. [PMID: 38867125 DOI: 10.1038/d41586-024-01506-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
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4
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Gao RZ, Lee PS, Ravi A, Ren CL, Dickerson CR, Tung JY. Hybrid Soft-Rigid Active Prosthetics Laboratory Exercise for Hands-On Biomechanical and Biomedical Engineering Education. J Biomech Eng 2024; 146:051007. [PMID: 38456810 DOI: 10.1115/1.4065008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/26/2024] [Indexed: 03/09/2024]
Abstract
This paper introduces a hands-on laboratory exercise focused on assembling and testing a hybrid soft-rigid active finger prosthetic for biomechanical and biomedical engineering (BME) education. This hands-on laboratory activity focuses on the design of a myoelectric finger prosthesis, integrating mechanical, electrical, sensor (i.e., inertial measurement units (IMUs), electromyography (EMG)), pneumatics, and embedded software concepts. We expose students to a hybrid soft-rigid robotic system, offering a flexible, modifiable lab activity that can be tailored to instructors' needs and curriculum requirements. All necessary files are made available in an open-access format for implementation. Off-the-shelf components are all purchasable through global vendors (e.g., DigiKey Electronics, McMaster-Carr, Amazon), costing approximately USD 100 per kit, largely with reusable elements. We piloted this lab with 40 undergraduate engineering students in a neural and rehabilitation engineering upper year elective course, receiving excellent positive feedback. Rooted in real-world applications, the lab is an engaging pedagogical platform, as students are eager to learn about systems with tangible impacts. Extensions to the lab, such as follow-up clinical (e.g., prosthetist) and/or technical (e.g., user-device interface design) discussion, are a natural means to deepen and promote interdisciplinary hands-on learning experiences. In conclusion, the lab session provides an engaging journey through the lifecycle of the prosthetic finger research and design process, spanning conceptualization and creation to the final assembly and testing phases.
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Affiliation(s)
- Run Ze Gao
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave W., E5-3008, Waterloo, ON N2L 3G1, Canada
- University of Waterloo
| | - Peter S Lee
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave W., E5-3008, Waterloo, ON N2L 3G1, Canada
- University of Waterloo
| | - Aravind Ravi
- Department of Systems Design Engineering, University of Waterloo, 200 University Ave W., E7-3443, Waterloo, ON N2L 3G1, Canada
- University of Waterloo
| | - Carolyn L Ren
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave W., E3-4105, Waterloo, ON N2L 3G1, Canada
| | - Clark R Dickerson
- Department of Kinesiology and Health Sciences, University of Waterloo, 200 University Ave W., EXP 2684, Waterloo, ON N2L 3G1, Canada
| | - James Y Tung
- Department of Systems Design Engineering, University of Waterloo, 200 University Ave W., E7-3428, Waterloo, ON N2L 3G1, Canada
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5
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Li M, Zhang B, Liu L, Tan X, Li N, Zhao X. Balance recovery for lower limb exoskeleton in standing posture based on orbit energy analysis. Front Bioeng Biotechnol 2024; 12:1389243. [PMID: 38742206 PMCID: PMC11089179 DOI: 10.3389/fbioe.2024.1389243] [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: 02/21/2024] [Accepted: 04/08/2024] [Indexed: 05/16/2024] Open
Abstract
Introduction: The need for effective balance control in lower limb rehabilitation exoskeletons is critical for ensuring stability and safety during rehabilitation training. Current research into specialized balance recovery strategies is limited, highlighting a gap in biomechanics-inspired control methods. Methods: We introduce a new metric called "Orbit Energy" (OE), which assesses the balance state of the human-exoskeleton system based on the dynamics of the overall center of mass. Our control framework utilizes OE to choose appropriate balance recovery strategies, including torque controls at the ankle and hip joints. Results: The efficacy of our control algorithm was confirmed through Matlab Simulink simulations, which analyzed the recovery of balance under various disturbance forces and conditions. Further validation came from physical experiments with human subjects wearing the exoskeleton, where a significant reduction in muscle activation was observed during balance maintenance under external disturbances. Discussion: Our findings underscore the potential of biomechanics-inspired metrics like OE in enhancing exoskeleton functionality for rehabilitation purposes. The introduction of such metrics could lead to more targeted and effective balance recovery strategies, ultimately improving the safety and stability of exoskeleton use in rehabilitation settings.
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Affiliation(s)
- Mengze Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- Research Center for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, China
| | - Bi Zhang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Ligang Liu
- BYD Auto Industry Company Limited, Shenzhen, China
| | - Xiaowei Tan
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Ning Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Xingang Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
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6
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Molinaro DD, Kang I, Young AJ. Estimating human joint moments unifies exoskeleton control, reducing user effort. Sci Robot 2024; 9:eadi8852. [PMID: 38507475 DOI: 10.1126/scirobotics.adi8852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.
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Affiliation(s)
- Dean D Molinaro
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Inseung Kang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Aaron J Young
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
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7
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Wang H, Ding Q, Luo Y, Wu Z, Yu J, Chen H, Zhou Y, Zhang H, Tao K, Chen X, Fu J, Wu J. High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309868. [PMID: 38095146 DOI: 10.1002/adma.202309868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/03/2023] [Indexed: 12/22/2023]
Abstract
Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.
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Affiliation(s)
- Hao Wang
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Qiongling Ding
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Yibing Luo
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Zixuan Wu
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Jiahao Yu
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Huizhi Chen
- Guangdong Provincial Key Laboratory of Research and Development of Natural Drugs and School of Pharmacy, Guangdong Medical University, Dongguan, 523808, P. R. China
- The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523808, P. R. China
| | - Yubin Zhou
- Guangdong Provincial Key Laboratory of Research and Development of Natural Drugs and School of Pharmacy, Guangdong Medical University, Dongguan, 523808, P. R. China
- The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523808, P. R. China
| | - He Zhang
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, National Engineering Research Center of Novel Equipment for Polymer Processing, Key Laboratory of Polymer Processing Engineering (SCUT) Ministry of Education, South China University of Technology, Guangzhou, 510641, P. R. China
| | - Kai Tao
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiaoliang Chen
- Micro- and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Jun Fu
- School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou, 510275, China
| | - Jin Wu
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, National Engineering Research Center of Novel Equipment for Polymer Processing, Key Laboratory of Polymer Processing Engineering (SCUT) Ministry of Education, South China University of Technology, Guangzhou, 510641, P. R. China
- State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, 610065, People's Republic of China
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8
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Park J, Lee Y, Cho S, Choe A, Yeom J, Ro YG, Kim J, Kang DH, Lee S, Ko H. Soft Sensors and Actuators for Wearable Human-Machine Interfaces. Chem Rev 2024; 124:1464-1534. [PMID: 38314694 DOI: 10.1021/acs.chemrev.3c00356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.
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Affiliation(s)
- Jonghwa Park
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Youngoh Lee
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Seungse Cho
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Ayoung Choe
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Jeonghee Yeom
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Yun Goo Ro
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Jinyoung Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Dong-Hee Kang
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Seungjae Lee
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Hyunhyub Ko
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
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Xia H, Zhang Y, Rajabi N, Taleb F, Yang Q, Kragic D, Li Z. Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement. Nat Commun 2024; 15:1760. [PMID: 38409128 PMCID: PMC10897332 DOI: 10.1038/s41467-024-46249-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/19/2024] [Indexed: 02/28/2024] Open
Abstract
Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.
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Affiliation(s)
- Haisheng Xia
- School of Mechanical Engineering, Tongji University, Shanghai, 201804, China
- Translational Research Center, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University, Shanghai, 201619, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230026, China
| | - Yuchong Zhang
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Nona Rajabi
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Farzaneh Taleb
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Qunting Yang
- Department of Automation, University of Science and Technology of China, Hefei, 230026, China
| | - Danica Kragic
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Zhijun Li
- School of Mechanical Engineering, Tongji University, Shanghai, 201804, China.
- Translational Research Center, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University, Shanghai, 201619, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230026, China.
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10
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Kim K, Yang H, Lee J, Lee WG. Metaverse Wearables for Immersive Digital Healthcare: A Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303234. [PMID: 37740417 PMCID: PMC10625124 DOI: 10.1002/advs.202303234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/15/2023] [Indexed: 09/24/2023]
Abstract
The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.
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Affiliation(s)
- Kisoo Kim
- Intelligent Optical Module Research CenterKorea Photonics Technology Institute (KOPTI)Gwangju61007Republic of Korea
| | - Hyosill Yang
- Department of NursingCollege of Nursing ScienceKyung Hee UniversitySeoul02447Republic of Korea
| | - Jihun Lee
- Department of Mechanical EngineeringCollege of EngineeringKyung Hee UniversityYongin17104Republic of Korea
| | - Won Gu Lee
- Department of Mechanical EngineeringCollege of EngineeringKyung Hee UniversityYongin17104Republic of Korea
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11
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Fraldi M, Palumbo S, Cutolo A, Carotenuto AR, Bigoni D. Bimodal buckling governs human fingers' luxation. Proc Natl Acad Sci U S A 2023; 120:e2311637120. [PMID: 37871221 PMCID: PMC10622902 DOI: 10.1073/pnas.2311637120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 09/25/2023] [Indexed: 10/25/2023] Open
Abstract
Equilibrium bifurcation in natural systems can sometimes be explained as a route to stress shielding for preventing failure. Although compressive buckling has been known for a long time, its less-intuitive tensile counterpart was only recently discovered and yet never identified in living structures or organisms. Through the analysis of an unprecedented all-in-one paradigm of elastic instability, it is theoretically and experimentally shown that coexistence of two curvatures in human finger joints is the result of an optimal design by nature that exploits both compressive and tensile buckling for inducing luxation in case of traumas, so realizing a unique mechanism for protecting tissues and preventing more severe damage under extreme loads. Our findings might pave the way to conceive complex architectured and bio-inspired materials, as well as next generation artificial joint prostheses and robotic arms for bio-engineering and healthcare applications.
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Affiliation(s)
- Massimiliano Fraldi
- Department of Structures for Engineering and Architecture, University of Napoli “Federico II”, Napoli80125, Italia
- Laboratory of Integrated Mechanics and Imaging for Testing and Simulation, University of Napoli “Federico II”, Napoli80125, Italia
| | - Stefania Palumbo
- Department of Structures for Engineering and Architecture, University of Napoli “Federico II”, Napoli80125, Italia
- Laboratory of Integrated Mechanics and Imaging for Testing and Simulation, University of Napoli “Federico II”, Napoli80125, Italia
| | - Arsenio Cutolo
- Department of Structures for Engineering and Architecture, University of Napoli “Federico II”, Napoli80125, Italia
- Laboratory of Integrated Mechanics and Imaging for Testing and Simulation, University of Napoli “Federico II”, Napoli80125, Italia
| | - Angelo Rosario Carotenuto
- Department of Structures for Engineering and Architecture, University of Napoli “Federico II”, Napoli80125, Italia
- Laboratory of Integrated Mechanics and Imaging for Testing and Simulation, University of Napoli “Federico II”, Napoli80125, Italia
| | - Davide Bigoni
- Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento38123, Italia
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12
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Hu X, Tan T, Wang B, Yan Z. A reprogrammable mechanical metamaterial with origami functional-group transformation and ring reconfiguration. Nat Commun 2023; 14:6709. [PMID: 37872137 PMCID: PMC10593812 DOI: 10.1038/s41467-023-42323-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 10/06/2023] [Indexed: 10/25/2023] Open
Abstract
Recent advancements in reprogrammable metamaterials have enabled the development of intelligent matters with variable special properties in situ. These metamaterials employ intra-element physical reconfiguration and inter-element structural transformation. However, existing mono-characteristic homo-element mechanical metamaterials have limited reprogramming functions. Here, we introduce a reprogrammable mechanical metamaterial composed of origami elements with heterogeneous mechanical properties, which achieves various mechanical behavior patterns by functional group transformations and ring reconfigurations. Through the anisotropic assembly of two heterogeneous elements into a functional group, we enable mechanical behavior switching between positive and negative stiffness. The resulting polygonal ring exhibits rotational deformation, zero Poisson's ratio stretching/compression deformation, and negative Poisson's ratio auxetic deformation. Arranging these rings periodically yields homogeneous metamaterials. The reconfiguration of quadrilateral rings allows for continuous fine-tunability of the mechanical response and negative Poisson's ratio. This mechanical metamaterial could provide a versatile material platform for reprogrammable mechanical computing, multi-purpose robots, transformable vehicles and architectures at different scales.
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Affiliation(s)
- Xinyu Hu
- State Key Laboratory of Ocean Engineering, Department of Mechanics, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China
| | - Ting Tan
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China
| | - Benlong Wang
- State Key Laboratory of Ocean Engineering, Department of Mechanics, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China
| | - Zhimiao Yan
- State Key Laboratory of Ocean Engineering, Department of Mechanics, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China.
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13
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Zhang X, Tricomi E, Missiroli F, Lotti N, Ma X, Masia L. Improving Walking Assistance Efficiency in Real-World Scenarios with Soft Exosuits Using Locomotion Mode Detection. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941239 DOI: 10.1109/icorr58425.2023.10304773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
The use of portable and lightweight wearable assistive devices can improve wearer locomotion efficiency by reducing the metabolic cost of walking. To achieve this goal, assistive technologies must adapt to different locomotion modes to optimize walking assistance. In this work, we developed a novel control strategy for an underactuated soft exosuit featuring a single actuator to assist bilateral hip flexion, which utilized inertial measurement units (IMUs) to discriminate between three different locomotion modes: walking up/down stairs or on level ground. Walking assistance was adjusted in real-time to maximize the assistance provided to the user. In order to preliminary test the effectiveness of this control strategy, four healthy subjects performed a walking task with the exosuit disabled (Exo Off) and enabled (Exo On). Results showed that the kinematics-based IMU classification strategy achieved an overall accuracy exceeding 95% across the three-movement patterns. Subjects were able to save an average of 10.1% on walking energy expenditure with assistance from the wearable device. This work contributes to the development of compact, high-performance lower limb assistive technologies and their development in practical applications.
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14
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Chen YN, Wu YN, Yang BS. The neuromuscular control for lower limb exoskeleton- a 50-year perspective. J Biomech 2023; 158:111738. [PMID: 37562276 DOI: 10.1016/j.jbiomech.2023.111738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 07/19/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023]
Abstract
Historically, impaired lower limb function has resulted in heavy health burden and large economic loss in society. Although experts from various fields have put large amounts of effort into overcoming this challenge, there is still not a single standard treatment that can completely restore the lost limb function. During the past half century, with the advancing understanding of human biomechanics and engineering technologies, exoskeletons have achieved certain degrees of success in assisting and rehabilitating patients with loss of limb function, and therefore has been spotlighted in both the medical and engineering fields. In this article, we review the development milestones of lower limb exoskeletons as well as the neuromuscular interactions between the device and wearer throughout the past 50 years. Fifty years ago, the lower-limb exoskeletons just started to be devised. We review several prototypes and present their designs in terms of structure, sensor and control systems. Subsequently, we introduce the development milestones of modern lower limb exoskeletons and discuss the pros and cons of these differentiated devices. In addition, we summarize current important neuromuscular control systems and sensors; and discuss current evidence demonstrating how the exoskeletons may affect neuromuscular control of wearers. In conclusion, based on our review, we point out the possible future direction of combining multiple current technologies to build lower limb exoskeletons that can serve multiple aims.
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Affiliation(s)
- Yu-Ning Chen
- Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Taiwan; Biomechanics and Medical Application Laboratory, National Yang Ming Chiao Tung University; Division of Neurosurgery, Department of Surgery, National Taiwan University Hospital Hsin-Chu Branch, Taiwan
| | - Yi-Ning Wu
- Department of Physical Therapy and Kinesiology, University of Massachusetts Lowell, MA, USA; The New England Robotics Validation and Experimentation Center, University of Massachusetts Lowell, MA, USA
| | - Bing-Shiang Yang
- Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Taiwan; Biomechanics and Medical Application Laboratory, National Yang Ming Chiao Tung University; Mechanical and Mechatronics Systems Research Laboratories, Industrial Technology Research Institute, Taiwan; Taiwanese Society of Biomechanics, Taiwan.
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15
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Lee J, Akbas T, Sulzer J. Hip and Knee Joint Kinematics Predict Quadriceps Hyperreflexia in People with Post-stroke Stiff-Knee Gait. Ann Biomed Eng 2023; 51:1965-1974. [PMID: 37133540 PMCID: PMC11003447 DOI: 10.1007/s10439-023-03217-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/20/2023] [Indexed: 05/04/2023]
Abstract
Wearable assistive technology for the lower extremities has shown great promise towards improving gait function in people with neuromuscular injuries. But common secondary impairments, such as hypersensitive stretch reflexes or hyperreflexia, have been often neglected. Incorporation of biomechanics into the control loop could improve individualization and avoid hyperreflexia. However, adding hyperreflexia prediction to the control loop would require expensive or complex measurement of muscle fiber characteristics. In this study, we explore a clinically accessible biomechanical predictor set that can accurately predict rectus femoris (RF) reaction after knee flexion assistance in pre-swing by a powered orthosis. We examined a total of 14 gait parameters based on gait kinematic, kinetic, and simulated muscle-tendon states from 8 post-stroke individuals with Stiff-Knee gait (SKG) wearing a knee exoskeleton robot. We independently performed both parametric and non-parametric variable selection approaches using machine learning regression techniques. Both models revealed the same four kinematic variables relevant to knee and hip joint motions were sufficient to effectively predict RF hyperreflexia. These results suggest that control of knee and hip kinematics may be a more practical method of incorporating quadriceps hyperreflexia into the exoskeleton control loop than the more complex acquisition of muscle fiber properties.
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Affiliation(s)
- Jeonghwan Lee
- Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
| | | | - James Sulzer
- Department of Physical Medicine and Rehabilitation, MetroHealth Medical Center and Case Western Reserve University, Cleveland, OH, USA.
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16
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Hybart R, Villancio-Wolter KS, Ferris DP. Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors. PeerJ 2023; 11:e15775. [PMID: 37525661 PMCID: PMC10387233 DOI: 10.7717/peerj.15775] [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: 04/13/2023] [Accepted: 06/29/2023] [Indexed: 08/02/2023] Open
Abstract
Lower limb robotic exoskeletons are often studied in the context of steady state treadmill walking in a laboratory environment. However, the end goal for exoskeletons is to be used in real world, complex environments. To reach the point that exoskeletons are openly adopted into our everyday lives, we need to understand how the human and robot interact outside of a laboratory. Metabolic cost is often viewed as a gold standard metric for measuring exoskeleton performance but is rarely used to evaluate performance at non steady state walking outside of a laboratory. In this study, we tested the effects of robotic ankle exoskeletons under proportional myoelectric control on the cost of transport of walking both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would lead to a lower cost of transport compared to walking without them both on a treadmill and outside. We saw no significant increases or decreases in cost of transport or exoskeleton mechanics when walking with the exoskeletons compared to walking without them both on a treadmill and outside. We saw a strong negative correlation between walking speed and cost of transport when walking with and without the exoskeletons. In the future, research should consider how performing more difficult tasks, such as incline and loaded walking, affects the cost of transport while walking with and without robotic ankle exoskeletons. The value of this study to the literature is that it emphasizes the importance of both hardware dynamics and controller design towards reducing metabolic cost of transport with robotic ankle exoskeletons. When comparing our results to other studies using the same hardware with different controllers or very similar controllers with different hardware, there are a wide range of outcomes as to metabolic benefit.
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Affiliation(s)
- Rachel Hybart
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - K. Siena Villancio-Wolter
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Daniel Perry Ferris
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
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17
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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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18
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Swaminathan K, Porciuncula F, Park S, Kannan H, Erard J, Wendel N, Baker T, Ellis TD, Awad LN, Walsh CJ. Ankle-targeted exosuit resistance increases paretic propulsion in people post-stroke. J Neuroeng Rehabil 2023; 20:85. [PMID: 37391851 PMCID: PMC10314463 DOI: 10.1186/s12984-023-01204-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/15/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND Individualized, targeted, and intense training is the hallmark of successful gait rehabilitation in people post-stroke. Specifically, increasing use of the impaired ankle to increase propulsion during the stance phase of gait has been linked to higher walking speeds and symmetry. Conventional progressive resistance training is one method used for individualized and intense rehabilitation, but often fails to target paretic ankle plantarflexion during walking. Wearable assistive robots have successfully assisted ankle-specific mechanisms to increase paretic propulsion in people post-stroke, suggesting their potential to provide targeted resistance to increase propulsion, but this application remains underexamined in this population. This work investigates the effects of targeted stance-phase plantarflexion resistance training with a soft ankle exosuit on propulsion mechanics in people post-stroke. METHODS We conducted this study in nine individuals with chronic stroke and tested the effects of three resistive force magnitudes on peak paretic propulsion, ankle torque, and ankle power while participants walked on a treadmill at their comfortable walking speeds. For each force magnitude, participants walked for 1 min while the exosuit was inactive, 2 min with active resistance, and 1 min with the exosuit inactive, in sequence. We evaluated changes in gait biomechanics during the active resistance and post-resistance sections relative to the initial inactive section. RESULTS Walking with active resistance increased paretic propulsion by more than the minimal detectable change of 0.8 %body weight at all tested force magnitudes, with an average increase of 1.29 ± 0.37 %body weight at the highest force magnitude. This improvement corresponded to changes of 0.13 ± 0.03 N m kg- 1 in peak biological ankle torque and 0.26 ± 0.04 W kg- 1 in peak biological ankle power. Upon removal of resistance, propulsion changes persisted for 30 seconds with an improvement of 1.49 ± 0.58 %body weight after the highest resistance level and without compensatory involvement of the unresisted joints or limb. CONCLUSIONS Targeted exosuit-applied functional resistance of paretic ankle plantarflexors can elicit the latent propulsion reserve in people post-stroke. After-effects observed in propulsion highlight the potential for learning and restoration of propulsion mechanics. Thus, this exosuit-based resistive approach may offer new opportunities for individualized and progressive gait rehabilitation.
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Affiliation(s)
- Krithika Swaminathan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Franchino Porciuncula
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
- Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, 02215, USA
| | - Sungwoo Park
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Harini Kannan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Julien Erard
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Nicholas Wendel
- Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, 02215, USA
| | - Teresa Baker
- Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, 02215, USA
| | - Terry D Ellis
- Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, 02215, USA
| | - Louis N Awad
- Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, 02215, USA
| | - Conor J Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA.
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19
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Kolaghassi R, Marcelli G, Sirlantzis K. Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:5687. [PMID: 37420852 DOI: 10.3390/s23125687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 07/09/2023]
Abstract
Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5 to 1.85 m/s. Four FCNNs (a generalised-speed model, a low-speed model, a high-speed model, and a low-high-speed model) are evaluated to assess their predictive performance on gait speeds included in the training speed range and on speeds that have been excluded from it. The evaluation involves short-term (one-step-ahead) predictions and long-term (200-time-step) recursive predictions. The results show that the performance of the low- and high-speed models, measured using the mean absolute error (MAE), decreased by approximately 43.7% to 90.7% when tested on the excluded speeds. Meanwhile, when tested on the excluded medium speeds, the performance of the low-high-speed model improved by 2.8% for short-term predictions and 9.8% for long-term predictions. These findings suggest that FCNNs are capable of interpolating to speeds within the maximum and minimum training speed ranges, even if not explicitly trained on those speeds. However, their predictive performance decreases for gaits at speeds beyond or below the maximum and minimum training speed ranges.
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Affiliation(s)
- Rania Kolaghassi
- School of Engineering, University of Kent, Canterbury CT2 7NT, UK
| | | | - Konstantinos Sirlantzis
- School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK
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20
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Nakano H, Matsugi A, Ito T, Oku K, Sakita M. Editorial: Pushing the limits of motor function recovery in rehabilitation: Basic to applied research based on neuroscience. Front Hum Neurosci 2023; 17:1160632. [PMID: 36908714 PMCID: PMC9996318 DOI: 10.3389/fnhum.2023.1160632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023] Open
Affiliation(s)
- Hideki Nakano
- Graduate School of Health Sciences, Kyoto Tachibana University, Kyoto, Japan
| | - Akiyoshi Matsugi
- Faculty of Rehabilitation, Shijonawate Gakuen University, Daito, Japan
| | - Tomotaka Ito
- Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Japan
| | - Kosuke Oku
- Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Japan
| | - Masahiro Sakita
- Graduate School of Health Sciences, Kyoto Tachibana University, Kyoto, Japan
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