Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition

Frontiers in Neurorobotics, Journal Year: 2023, Volume and Issue: 17

Published: July 3, 2023

Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support balance for assist them in performing training tasks, which can effectively improve their quality life during the later stages recovery. have become hot topic therapy research. This review introduces traditional assessment methods, explores possibility lower combining sensors electrophysiological signals to assess survivors' objectively, summarizes standard human-robot coupling models recent years, critically adaptive control based on motion intent recognition robots. provides new design ideas future combination with assessment, assistance, treatment, control, making process more objective addressing shortage therapists some extent. Finally, article discusses current limitations proposes research directions.

Language: Английский

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Lower-Limb Exoskeleton With Compliant Actuators: Human Cooperative Control

IEEE Transactions on Medical Robotics and Bionics, Journal Year: 2023, Volume and Issue: 5(3), P. 717 - 729

Published: June 30, 2023

Active exoskeletons for the lower extremities are increasingly being used in rehabilitation therapy. One of key areas research developing these assistive devices is ensuring safe human-machine interaction, which requires both a mechanical system and an effective control framework. Therefore, we present novel human cooperative framework with variable stiffness actuators to assist users during swing stance phases walking other motion sequences such as sit-to-stand. The estimates user's joint torques using Unscented Kalman Filter (UKF) inverse kinematics, respectively. Using Lower-Limb Exoskeleton Serial Elastic Actuators (L2Exo-SE) example, approach was validated its applicability compliant actuators. validation results reveal reduction average torque gait by 63.6%-78.4% hip 40.8%-50.2% knee compared non-assisted walking. Furthermore, introduce automatic selection serial elasticity actuator (VSA) based on active torque. variation increases physical human-robot interaction phase while maintaining high bandwidth phase.

Language: Английский

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A categorization of medical robots by their applications

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 12

Published: Jan. 1, 2025

Language: Английский

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Human Lower Limb Motion Intention Recognition for Exoskeletons: A Review

IEEE Sensors Journal, Journal Year: 2023, Volume and Issue: 23(24), P. 30007 - 30036

Published: Nov. 6, 2023

Human motion intention (HMI) has increasingly gained concerns in lower limb exoskeletons (LLEs). HMI recognition (HMIR) is the precondition for realizing active compliance control LLEs. Accurate and efficient of will benefit LLEs achieving natural effective human–robot interaction (HRI) improving wearing comfort level. A systematic review HMIR great significance developing However, there no literature comprehensively describing development roadmap human (HLLMIR) so far. In order to have a comprehensive understanding HLLMIR explore current research status trend LLEs, this article provides First, mechanism are fully illustrated, tasks pertaining motions (LLMs) elaborated on. Next, intention-related sensing signals with different sources dissected detail, including bioelectric electroencephalography (EEG) electromyogram (EMG), biomechanical signals, multisource fusion. The methods thoroughly addressed analyzed, categorized as model-based, such musculoskeletal model model-free method involving heuristic rule-based, conventional machine learning (ML)-based, deep (DL)-based. Finally, an overall discussion on tasks, methods, performance assessments given, thus, challenges summarized prospected.

Language: Английский

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Biosignal-integrated robotic systems with emerging trends in visual interfaces: A systematic review

Biophysics Reviews, Journal Year: 2024, Volume and Issue: 5(1)

Published: Feb. 21, 2024

Human–machine interfaces (HMI) are currently a trendy and rapidly expanding area of research. Interestingly, the human user does not readily observe interface between humans machines. Instead, interactions machine electrical signals from user's body obscured by complex control algorithms. The result is effectively one-way street, wherein data only transmitted to machine. Thus, gap remains in literature: how can information be conveyed enable mutual understanding machines? Here, this paper reviews recent advancements biosignal-integrated wearable robotics, with particular emphasis on “visualization”—the presentation relevant data, statistics, visual feedback user. This review article covers various interest, such as electroencephalograms electromyograms, explores novel sensor architectures key materials. Recent developments robotics examined mechanical design perspectives. Additionally, we discuss current visualization methods outline field's future direction. While much HMI field focuses biomedical healthcare applications, rehabilitation spinal cord injury stroke patients, also less common applications manufacturing, defense, other domains.

Language: Английский

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Recursive PID-NT Estimation-Based Second-Order SMC Strategy for Knee Exoskeleton Robots: A Focus on Uncertainty Mitigation

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1455 - 1455

Published: April 3, 2025

This study introduces a modified second-order super-twisting sliding mode control algorithm designed to enhance the precision and robustness of knee exoskeleton robots by incorporating advanced uncertainty mitigation techniques. The key contribution this research is development an efficient estimation mechanism capable accurately identifying model parameter uncertainties patients’ unwanted action torques disturbance within finite time horizon, thereby improving overall system performance. proposed framework ensures smooth precise signal dynamics while effectively suppressing chattering effects, common drawback in conventional methodologies. theoretical foundation rigorously established through formulation PID non-singular terminal variable, which stability phase comprehensive Lyapunov-based analysis assuming that upper bound its derivative are known reaching phase, collectively guarantee system’s reliability. Through simulations, efficacy evaluated ability track diverse desired angles, demonstrate against disturbances, such as those caused patient’s foot reaction, handle 20% parameters. Additionally, effectiveness assessed three individuals with varying Notably, controller gains remain consistent across all scenarios. constitutes significant advancement domain control, offering more reliable methodology for addressing uncertainties.

Language: Английский

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Model-free based adaptive BackStepping-Super Twisting-RBF neural network control with α-variable for 10 DOF lower limb exoskeleton

International Journal of Intelligent Robotics and Applications, Journal Year: 2024, Volume and Issue: 8(1), P. 122 - 148

Published: Feb. 25, 2024

Language: Английский

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Adaptive patient-cooperative compliant control of lower limb rehabilitation robot

Biomimetic Intelligence and Robotics, Journal Year: 2024, Volume and Issue: 4(2), P. 100155 - 100155

Published: March 16, 2024

With the increase in number of stroke patients, there is a growing demand for rehabilitation training. Robot-assisted training expected to play crucial role meeting this demand. To ensure safety and comfort patients during training, it important have patient-cooperative compliant control system robots. In order enhance motion compliance hierarchical adaptive strategy that includes patient-passive exercise proposed. A low-level backstepping position controller selected accurate tracking desired trajectory. At high-level, an admittance employed plan trajectory based on interaction force between patient robot. The results patient-robot cooperation experiment robot show significant improvement trajectory, with decrease 76.45% dimensionless squared jerk (DSJ) 15.38% normalized root mean square deviation (NRMSD) when using controller. proposed effectively enhances movements, thereby ensuring

Language: Английский

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Integration of Virtual Reality-Enhanced Motor Imagery and Brain-Computer Interface for a Lower-Limb Rehabilitation Exoskeleton Robot

Actuators, Journal Year: 2024, Volume and Issue: 13(7), P. 244 - 244

Published: June 28, 2024

In this study, we integrated virtual reality (VR) goggles and a motor imagery (MI) brain-computer interface (BCI) algorithm with lower-limb rehabilitation exoskeleton robot (LLRER) system. The MI-BCI system was the VR to identify intention classification enhanced immersive experience of subjects during data collection. VR-enhanced electroencephalography (EEG) model seated subject directly applied LLRER wearer. experimental results showed that had positive effect on accuracy MI-BCI. best were obtained in position wearing VR, but cannot be triggers LLRER. There number confounding factors needed overcome. This study proposes cumulative distribution function (CDF) auto-leveling method can apply standing exoskeletons. an 75.35% open-loop test LLRER, correctly triggering action closed-loop gait 74%. Preliminary findings regarding development activated by presented.

Language: Английский

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Assistive Force Cycle Prediction Method for Lower Limb Exoskeleton Based on Independent Attitude Processing Sensor

IEEE Sensors Journal, Journal Year: 2024, Volume and Issue: 24(7), P. 9562 - 9572

Published: Feb. 14, 2024

In order to make the exoskeleton suitable for wearers, it is necessary provide fitting assistive force, wherein force cycle an important parameter generating curve. The always wants match gait and needs be determined at beginning of each gait. Due unknown current cycle, often defined as previous or average multiple cycles. However, this strategy not accurate. For reason, prediction method based on long short-term memory (LSTM) network with independent attitude processing (IAP) sensors proposed in article. First, IAP are utilized collect process information generate input features. Meanwhile, a variable window detecting timing calculating cycle. Then, features corresponding cycles transferred LSTM predictive model. Finally, predicted four modes (uniform walking, upstairs, uphill). validation experiment, mean square error (MSE) selected evaluation index model effect. Compared other traditional calculation method, MSEs all lower than 0.1 kinds gaits. accuracy by can reach 97.23%.

Language: Английский

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