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Nisar H, Annamraju S, Deka SA, Horowitz A, Stipanović DM. Robotic mirror therapy for stroke rehabilitation through virtual activities of daily living. Comput Struct Biotechnol J 2024; 24:126-135. [PMID: 38352631 PMCID: PMC10862404 DOI: 10.1016/j.csbj.2024.01.017] [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: 09/27/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
Mirror therapy is a standard technique of rehabilitation for recovering motor and vision abilities of stroke patients, especially in the case of asymmetric limb function. To enhance traditional mirror therapy, robotic mirror therapy (RMT) has been proposed over the past decade, allowing for assisted bimanual coordination of paretic (affected) and contralateral (healthy) limbs. However, state-of-the-art RMT platforms predominantly target mirrored motions of trajectories, largely limited to 2-D motions. In this paper, an RMT platform is proposed, which can facilitate the patient to practice virtual activities of daily living (ADL) and thus enhance their independence. Two similar (but mirrored) 3D virtual environments are created in which the patients operate robots with both their limbs to complete ADL (such as writing and eating) with the assistance of the therapist. The recovery level of the patient is continuously assessed by monitoring their ability to track assigned trajectories. The patient's robots are programmed to assist the patient in following these trajectories based on this recovery level. In this paper, the framework to dynamically monitor recovery level and accordingly provide assistance is developed along with the nonlinear controller design to ensure position tracking, force control, and stability. Proof-of-concept studies are conducted with both 3D trajectory tracking and ADL. The results demonstrate the potential use of the proposed system to enhance the recovery of the patients.
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Affiliation(s)
- Harris Nisar
- Health Care Engineering Systems Center, University of Illinois Urbana Champaign, 1206 W Clark St, Urbana 61801, IL, USA
| | - Srikar Annamraju
- Coordinated Science Laboratory, University of Illinois Urbana Champaign, 1308 W Main St, Urbana 61801, IL, USA
| | - Shankar A. Deka
- Division of Decision and Control Systems at KTH Royal Institute of Technology, Brinellvägen 8, 114 28 Stockholm, Sweden
| | - Anne Horowitz
- Outpatient Rehabilitation, OSF Healthcare Saint Francis Medical Center, 6501 N Sheridan Rd, Peoria, IL, USA
| | - Dušan M. Stipanović
- Coordinated Science Laboratory, University of Illinois Urbana Champaign, 1308 W Main St, Urbana 61801, IL, USA
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Coser O, Tamantini C, Soda P, Zollo L. AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review. Front Robot AI 2024; 11:1341580. [PMID: 38405325 PMCID: PMC10884274 DOI: 10.3389/frobt.2024.1341580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024] Open
Abstract
Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.
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Affiliation(s)
- Omar Coser
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy
- Unit of Advanced Robotics and Human-Centered Technologies, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Christian Tamantini
- Unit of Advanced Robotics and Human-Centered Technologies, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy
- Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden
| | - Loredana Zollo
- Unit of Advanced Robotics and Human-Centered Technologies, Università Campus Bio-Medico di Roma, Rome, Italy
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Xu J, Huang K, Zhang T, Cao K, Ji A, Xu L, Li Y. A rehabilitation robot control framework with adaptation of training tasks and robotic assistance. Front Bioeng Biotechnol 2023; 11:1244550. [PMID: 37849981 PMCID: PMC10577441 DOI: 10.3389/fbioe.2023.1244550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/29/2023] [Indexed: 10/19/2023] Open
Abstract
Robot-assisted rehabilitation has exhibited great potential to enhance the motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic motion can be regulated through physical human-robot interaction for comfortable support and appropriate assistance level. However, it is hardly possible, especially for patients with severe motor disabilities, to continuously exert force to guide the robot to complete the prescribed training task. Conversely, reduced task difficulty cannot facilitate stimulating patients' potential movement capabilities. Moreover, challenging more difficult tasks with minimal robotic assistance is usually ignored when subjects show improved performance. In this paper, a control framework is proposed to simultaneously adjust both the training task and robotic assistance according to the subjects' performance, which can be estimated from the users' electromyography signals. Concretely, a trajectory deformation algorithm is developed to generate smooth and compliant task motion while responding to pHRI. An assist-as-needed (ANN) controller along with a feedback gain modification algorithm is designed to promote patients' active participation according to individual performance variance on completing the training task. The proposed control framework is validated using a lower extremity rehabilitation robot through experiments. The experimental results demonstrate that the control scheme can optimize the robotic assistance to complete the subject-adaptation training task with high efficiency.
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Affiliation(s)
- Jiajun Xu
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Kaizhen Huang
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Tianyi Zhang
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Kai Cao
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Aihong Ji
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Linsen Xu
- College of Mechanical and Electrical Engineering, Hohai University, Changzhou, China
| | - Youfu Li
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
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Xu J, Xu L, Ji A, Cao K. Learning robotic motion with mirror therapy framework for hemiparesis rehabilitation. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Abstract
Abstract
Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user’s interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals’ baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training.
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Tucan P, Vaida C, Ulinici I, Banica A, Burz A, Pop N, Birlescu I, Gherman B, Plitea N, Antal T, Carbone G, Pisla D. Optimization of the ASPIRE Spherical Parallel Rehabilitation Robot Based on Its Clinical Evaluation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:3281. [PMID: 33810042 PMCID: PMC8004699 DOI: 10.3390/ijerph18063281] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 11/16/2022]
Abstract
The paper presents the design optimization of the ASPIRE spherical parallel robot for shoulder rehabilitation following clinical evaluation and clinicians' feedback. After the development of the robotic structure and the implementation of the control system, ASPIRE was prepared for clinical evaluation. A set of clinical trials was performed on 24 patients with different neurological disorders to obtain the patient and clinician acceptance of the rehabilitation system. During the clinical trials, the behavior of the robotic system was closely monitored and analyzed in order to improve its reliability and overall efficiency. Along with its reliability and efficiency, special attention was given to the safety characteristics during the rehabilitation task.
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Affiliation(s)
- Paul Tucan
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Calin Vaida
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Ionut Ulinici
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Alexandru Banica
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Alin Burz
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Nicoleta Pop
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Iosif Birlescu
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Bogdan Gherman
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Nicolae Plitea
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Tiberiu Antal
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | | | - Doina Pisla
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
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