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Search Results (874)

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Keywords = rehabilitation robot

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22 pages, 1481 KiB  
Article
Adaptive Impedance Control of a Human–Robotic System Based on Motion Intention Estimation and Output Constraints
by Junjie Ma, Hongjun Chen, Xinglan Liu, Yong Yang and Deqing Huang
Appl. Sci. 2025, 15(3), 1271; https://doi.org/10.3390/app15031271 - 26 Jan 2025
Abstract
The rehabilitation exoskeleton represents a typical human–robot system featuring complex nonlinear dynamics. This paper is devoted to proposing an adaptive impedance control strategy for a rehabilitation exoskelton. The patient’s motion intention is estimated online by the neural network (NN) to cope with the [...] Read more.
The rehabilitation exoskeleton represents a typical human–robot system featuring complex nonlinear dynamics. This paper is devoted to proposing an adaptive impedance control strategy for a rehabilitation exoskelton. The patient’s motion intention is estimated online by the neural network (NN) to cope with the intervention of the patient’s subjective motor awareness in the late stage of rehabilitation training. Due to the differences in impedance parameters for training tasks in individual patients and periods, the least square method was used to learn the impedance parameters of the patient. Considering the uncertainties of the exoskeleton and the safety of rehabilitation training, an adaptive neural network impedance controller with output constraints was designed. The NN was applied to approximate the unknown dynamics and the barrier Lyapunov function was applied to prevent the system from violating the output rules. The feasibility and effectiveness of the proposed strategy were verified by simulation. Full article
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19 pages, 3651 KiB  
Article
Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography
by Yonglin Han, Qing Tao and Xiaodong Zhang
Sensors 2025, 25(3), 719; https://doi.org/10.3390/s25030719 - 24 Jan 2025
Viewed by 377
Abstract
The estimation of multijoint angles is of great significance in the fields of lower limb rehabilitation, motion control, and exoskeleton robotics. Accurate joint angle estimation helps assess joint function, assist in rehabilitation training, and optimize robotic control strategies. However, estimating multijoint angles in [...] Read more.
The estimation of multijoint angles is of great significance in the fields of lower limb rehabilitation, motion control, and exoskeleton robotics. Accurate joint angle estimation helps assess joint function, assist in rehabilitation training, and optimize robotic control strategies. However, estimating multijoint angles in different movement patterns, such as walking, obstacle crossing, squatting, and knee flexion–extension, using surface electromyography (sEMG) signals remains a challenge. In this study, a model is proposed for the continuous motion estimation of multijoint angles in the lower limb (CB-TCN: temporal convolutional network + convolutional block attention module + temporal convolutional network). The model integrates temporal convolutional networks (TCNs) with convolutional block attention modules (CBAMs) to enhance feature extraction and improve prediction accuracy. The model effectively captures temporal features in lower limb movements, while enhancing attention to key features through the attention mechanism of CBAM. To enhance the model’s generalization ability, this study adopts a sliding window data augmentation method to expand the training samples and improve the model’s adaptability to different movement patterns. Through experimental validation on 8 subjects across four typical lower limb movements, walking, obstacle crossing, squatting, and knee flexion–extension, the results show that the CB-TCN model outperforms traditional models in terms of accuracy and robustness. Specifically, the model achieved R2 values of up to 0.9718, RMSE as low as 1.2648°, and NRMSE values as low as 0.05234 for knee angle prediction during walking. These findings indicate that the model combining TCN and CBAM has significant advantages in predicting lower limb joint angles. The proposed approach shows great promise for enhancing lower limb rehabilitation and motion analysis. Full article
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29 pages, 32667 KiB  
Article
An Active Control Method for a Lower Limb Rehabilitation Robot with Human Motion Intention Recognition
by Zhuangqun Song, Peng Zhao, Xueji Wu, Rong Yang and Xueshan Gao
Sensors 2025, 25(3), 713; https://doi.org/10.3390/s25030713 - 24 Jan 2025
Viewed by 383
Abstract
This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a [...] Read more.
This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm. Full article
(This article belongs to the Section Wearables)
19 pages, 8391 KiB  
Article
NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation
by Soroush Zare, Sameh I. Beaber and Ye Sun
Sensors 2025, 25(3), 610; https://doi.org/10.3390/s25030610 - 21 Jan 2025
Viewed by 1143
Abstract
Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to [...] Read more.
Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove’s soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to 85.3%, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient’s attempted movements using pure thinking through a non-intrusive brain–computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings. Full article
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51 pages, 26899 KiB  
Review
Robotic Systems for Hand Rehabilitation—Past, Present and Future
by Bogdan Gherman, Ionut Zima, Calin Vaida, Paul Tucan, Adrian Pisla, Iosif Birlescu, Jose Machado and Doina Pisla
Technologies 2025, 13(1), 37; https://doi.org/10.3390/technologies13010037 - 16 Jan 2025
Viewed by 1530
Abstract
Background: Cerebrovascular accident, commonly known as stroke, Parkinson’s disease, and multiple sclerosis represent significant neurological conditions affecting millions globally. Stroke remains the third leading cause of death worldwide and significantly impacts patients’ hand functionality, making hand rehabilitation crucial for improving quality of life. [...] Read more.
Background: Cerebrovascular accident, commonly known as stroke, Parkinson’s disease, and multiple sclerosis represent significant neurological conditions affecting millions globally. Stroke remains the third leading cause of death worldwide and significantly impacts patients’ hand functionality, making hand rehabilitation crucial for improving quality of life. Methods: A comprehensive literature review was conducted analyzing over 300 papers, and categorizing them based on mechanical design, mobility, and actuation systems. To evaluate each device, a database with 45 distinct criteria was developed to systematically assess their characteristics. Results: The analysis revealed three main categories of devices: rigid exoskeletons, soft exoskeletons, and hybrid devices. Electric actuation represents the most common source of power. The dorsal placement of the mechanism is predominant, followed by glove-based, lateral, and palmar configurations. A correlation between mass and functionality was observed during the analysis; an increase in the number of actuated fingers or in functionality automatically increases the mass of the device. The research shows significant technological evolution with considerable variation in design complexity, with 29.4% of devices using five or more actuators while 24.8% employ one or two actuators. Conclusions: While substantial progress has been made in recent years, several challenges persist, including missing information or incomplete data from source papers and a limited number of clinical studies to evaluate device effectiveness. Significant opportunities remain to improve device functionality, usability, and therapeutic effectiveness, as well as to implement advanced power systems for portable devices. Full article
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18 pages, 11743 KiB  
Article
The Design and Validation of an Open-Palm Data Glove for Precision Finger and Wrist Tracking
by Olivia Hosie, Mats Isaksson, John McCormick, Oren Tirosh and Chrys Hensman
Sensors 2025, 25(2), 367; https://doi.org/10.3390/s25020367 - 9 Jan 2025
Viewed by 478
Abstract
Wearable motion capture gloves enable the precise analysis of hand and finger movements for a variety of uses, including robotic surgery, rehabilitation, and most commonly, virtual augmentation. However, many motion capture gloves restrict natural hand movement with a closed-palm design, including fabric over [...] Read more.
Wearable motion capture gloves enable the precise analysis of hand and finger movements for a variety of uses, including robotic surgery, rehabilitation, and most commonly, virtual augmentation. However, many motion capture gloves restrict natural hand movement with a closed-palm design, including fabric over the palm and fingers. In order to alleviate slippage, improve comfort, reduce sizing issues, and eliminate movement restrictions, this paper presents a new low-cost data glove with an innovative open-palm and finger-free design. The new design improves usability and overall functionality by addressing the limitations of traditional closed-palm designs. It is especially beneficial in capturing movements in fields such as physical therapy and robotic surgery. The new glove incorporates resistive flex sensors (RFSs) at each finger and an inertial measurement unit (IMU) at the wrist joint to measure wrist flexion, extension, ulnar and radial deviation, and rotation. Initially the sensors were tested individually for drift, synchronisation delays, and linearity. The results show a drift of 6.60°/h in the IMU and no drift in the RFSs. There was a 0.06 s delay in the data captured by the IMU compared to the RFSs. The glove’s performance was tested with a collaborate robot testing setup. In static conditions, it was found that the IMU had a worst case error across three trials of 7.01° and a mean absolute error (MAE) averaged over three trials of 4.85°, while RFSs had a worst case error of 3.77° and a MAE of 1.25° averaged over all five RFSs used. There was no clear correlation between measurement error and speed. Overall, the new glove design proved to accurately measure joint angles. Full article
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12 pages, 2155 KiB  
Article
Human–Robot Interactions: A Pilot Study of Psychoaffective and Cognitive Factors to Boost the Acceptance and Usability of Assistive Wearable Devices
by Margherita Bertuccelli, Stefano Tortora, Edoardo Trombin, Liliana Negri, Patrizia Bisiacchi, Emanuele Menegatti and Alessandra Del Felice
Multimodal Technol. Interact. 2025, 9(1), 5; https://doi.org/10.3390/mti9010005 - 9 Jan 2025
Viewed by 421
Abstract
Robotic technology to assist rehabilitation provides practical advantages compared with traditional rehabilitation treatments, but its efficacy is still disputed. This controversial effectiveness is due to different factors, including a lack of guidelines to adapt devices to users’ individual needs. These needs include the [...] Read more.
Robotic technology to assist rehabilitation provides practical advantages compared with traditional rehabilitation treatments, but its efficacy is still disputed. This controversial effectiveness is due to different factors, including a lack of guidelines to adapt devices to users’ individual needs. These needs include the specific clinical conditions of people with disabilities, as well as their psychological and cognitive profiles. This pilot study aims to investigate the relationships between psychological, cognitive, and robot-related factors playing a role in human–robot interaction to promote a human-centric approach in robotic rehabilitation. Ten able-bodied volunteers were assessed for their anxiety, experienced workload, cognitive reserve, and perceived exoskeleton usability before and after a task with a lower-limb exoskeleton (i.e., 10 m path walking for 10 trials). Pre-trial anxiety levels were higher than post-trial ones (p < 0.01). While trait anxiety levels were predictive of the experienced effort (Adjusted-r2 = 0.43, p = 0.02), the state anxiety score was predictive of the perceived overall workload (Adjusted-r2 = 0.45, p = 0.02). High–average cognitive reserve scores were predictive of the perception of exoskeleton usability (Adjusted-r2 = 0.45, p = 0.02). A negative correlation emerged between the workload and the perception of personal identification with the exoskeleton (r = −0.67, p-value = 0.03). This study provides preliminary evidence of the impact of cognitive and psychoaffective factors on the perception of workload and overall device appreciation in exoskeleton training. It also suggests pragmatic measures such as familiarization time to reduce anxiety and end-user selection based on cognitive profiles. These assessments may provide guidance on the personalization of training. Full article
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21 pages, 702 KiB  
Review
The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty
by Luca Andriollo, Aurelio Picchi, Giulio Iademarco, Andrea Fidanza, Loris Perticarini, Stefano Marco Paolo Rossi, Giandomenico Logroscino and Francesco Benazzo
J. Pers. Med. 2025, 15(1), 21; https://doi.org/10.3390/jpm15010021 - 9 Jan 2025
Viewed by 644
Abstract
Total hip arthroplasty (THA) is a widely performed surgical procedure that has evolved significantly due to advancements in artificial intelligence (AI) and robotics. As demand for THA grows, reliable tools are essential to enhance diagnosis, preoperative planning, surgical precision, and postoperative rehabilitation. AI [...] Read more.
Total hip arthroplasty (THA) is a widely performed surgical procedure that has evolved significantly due to advancements in artificial intelligence (AI) and robotics. As demand for THA grows, reliable tools are essential to enhance diagnosis, preoperative planning, surgical precision, and postoperative rehabilitation. AI applications in orthopedic surgery offer innovative solutions, including automated hip osteoarthritis (OA) diagnosis, precise implant positioning, and personalized risk stratification, thereby improving patient outcomes. Deep learning models have transformed OA severity grading and implant identification by automating traditionally manual processes with high accuracy. Additionally, AI-powered systems optimize preoperative planning by predicting the hip joint center and identifying complications using multimodal data. Robotic-assisted THA enhances surgical precision with real-time feedback, reducing complications such as dislocations and leg length discrepancies while accelerating recovery. Despite these advancements, barriers such as cost, accessibility, and the steep learning curve for surgeons hinder widespread adoption. Postoperative rehabilitation benefits from technologies like virtual and augmented reality and telemedicine, which enhance patient engagement and adherence. However, limitations, particularly among elderly populations with lower adaptability to technology, underscore the need for user-friendly platforms. To ensure comprehensiveness, a structured literature search was conducted using PubMed, Scopus, and Web of Science. Keywords included “artificial intelligence”, “machine learning”, “robotics”, and “total hip arthroplasty”. Inclusion criteria emphasized peer-reviewed studies published in English within the last decade focusing on technological advancements and clinical outcomes. This review evaluates AI and robotics’ role in THA, highlighting opportunities and challenges and emphasizing further research and real-world validation to integrate these technologies into clinical practice effectively. Full article
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18 pages, 6840 KiB  
Article
Exploring New Tools in Upper Limb Rehabilitation After Stroke Using an Exoskeletal Aid: A Pilot Randomized Control Study
by Pantelis Syringas, Vassiliki Potsika, Nikolaos Tachos, Athanasios Pardalis, Christoforos Papaioannou, Alexandros Mitsis, Emilios E. Pakos, Orestis N. Zestas, Georgios Papagiannis, Athanasios Triantafyllou, Nikolaos D. Tselikas, Konstantina G. Yiannopoulou, George Papathanasiou, George Georgoudis, Daphne Bakalidou, Maria Kyriakidou, Panagiotis Gkrilias, Ioannis Kakkos, George K. Matsopoulos and Dimitrios I. Fotiadis
Viewed by 721
Abstract
Background/Objectives: Spasticity commonly occurs in individuals after experiencing a stroke, impairing their hand function and limiting activities of daily living (ADLs). In this paper, we introduce an exoskeletal aid, combined with a set of augmented reality (AR) games consisting of the Rehabotics rehabilitation [...] Read more.
Background/Objectives: Spasticity commonly occurs in individuals after experiencing a stroke, impairing their hand function and limiting activities of daily living (ADLs). In this paper, we introduce an exoskeletal aid, combined with a set of augmented reality (AR) games consisting of the Rehabotics rehabilitation solution, designed for individuals with upper limb spasticity following stroke. Methods: Our study, involving 60 post-stroke patients (mean ± SD age: 70.97  ±  4.89 years), demonstrates significant improvements in Ashworth Scale (AS) scores and Box and Block test (BBT) scores when the Rehabotics solution is employed. Results: The intervention group showed slightly greater improvement compared to the control group in terms of the AS (−0.23, with a confidence interval of −0.53 to 0.07) and BBT (1.67, with a confidence interval of 1.18 to 2.16). Additionally, the Rehabotics solution was particularly effective for patients with more severe deficits. Patients with an AS score of 3 showed more substantial improvements, with their AS scores increasing by −1.17 ± 0.39 and BBT scores increasing by −4.83 ± 0.72. Conclusions: These findings underscore the potential of wearable hand robotics in enhancing stroke survivors’ hand rehabilitation, emphasizing the need for further investigations into its broader applications. Full article
(This article belongs to the Special Issue Applications of Digital Technology in Comprehensive Healthcare)
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44 pages, 4022 KiB  
Review
Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review
by Nafizul Alam, Sk Hasan, Gazi Abdullah Mashud and Subodh Bhujel
Actuators 2025, 14(1), 16; https://doi.org/10.3390/act14010016 - 6 Jan 2025
Viewed by 619
Abstract
The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. [...] Read more.
The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot-assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot-assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot-assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human–robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction of customizing existing neural networks for robot-assisted rehabilitation applications. Full article
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9 pages, 723 KiB  
Article
The Effect of Home-Based Robotic Rehabilitation on Individuals with Disabilities in Community Settings: A Pilot Study
by Joonhwan Lee, Eunyoung Lee, Seokjoon Hong, Sunyi Shin and Byungju Ryu
Viewed by 569
Abstract
Background: With South Korea’s growing aging population, the demand for accessible rehabilitation solutions is increasing. Home-based robotic rehabilitation presents a feasible alternative to conventional in-clinic rehabilitation. This study explores the impact of the Rebless robotic rehabilitation device in a home-based setting for people [...] Read more.
Background: With South Korea’s growing aging population, the demand for accessible rehabilitation solutions is increasing. Home-based robotic rehabilitation presents a feasible alternative to conventional in-clinic rehabilitation. This study explores the impact of the Rebless robotic rehabilitation device in a home-based setting for people with physical disabilities and their caregivers. Methods: We prospectively collected data from individuals with brain disorders or physical disabilities living in Dongdaemun-gu, from August 2023 to March 2024. Participants completed an 8-week rehabilitation program using the Rebless robotic device. Assessments were conducted at baseline and after the eight-week program, measuring motor function, caregiver burden, and quality of life. Exercises were performed three times weekly for at least 90 min total. Results: We conducted an intervention with 26 adults with physical or neurological disabilities, of which 20 completed the program. Significant improvements were observed in upper limb function within the elbow exercise group (Fugl–Meyer assessment for upper extremity, p = 0.043) and a reduction in caregiver burden across the total groups (Zarit Burden Interview, p = 0.003). However, no statistically significant changes were found in balance and mobility measures (Berg balance scale, timed up-and-go, 10 m walk test). Conclusions: Home-based robotic rehabilitation demonstrates potential for improving upper limb function and reducing caregiver burden and mental health, proving beneficial to both patients and caregivers. Full article
(This article belongs to the Special Issue Rehabilitation Program for Orthopedic and Neurological Patients)
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41 pages, 1344 KiB  
Article
Robust Position Control of a Knee-Joint Rehabilitation Exoskeleton Using a Linear Matrix Inequalities-Based Design Approach
by Sahar Jenhani, Hassène Gritli and Jyotindra Narayan
Appl. Sci. 2025, 15(1), 404; https://doi.org/10.3390/app15010404 - 4 Jan 2025
Viewed by 1116
Abstract
This study focuses on developing a control methodology for exoskeleton robots designed for lower limb rehabilitation, specifically addressing the needs of elderly individuals and pediatric therapy. The approach centers on implementing an affine state-feedback controller to effectively regulate and stabilize the knee-joint exoskeleton [...] Read more.
This study focuses on developing a control methodology for exoskeleton robots designed for lower limb rehabilitation, specifically addressing the needs of elderly individuals and pediatric therapy. The approach centers on implementing an affine state-feedback controller to effectively regulate and stabilize the knee-joint exoskeleton robot at a desired position. The robot’s dynamics are nonlinear, accounting for unknown parameters, solid and viscous frictions, and external disturbances. To ensure robust stabilization, the Lyapunov approach is utilized to derive a set of Linear Matrix Inequality (LMI) conditions, guaranteeing the stability of the position error. The derivation of these LMI conditions is grounded in a comprehensive theoretical framework that employs advanced mathematical tools, including the matrix inversion lemma, Young’s inequality, the Schur complement, the S-procedure, and specific congruence transformations. Simulation results are presented to validate the proposed LMI conditions, demonstrating the effectiveness of the control strategy in achieving robust and accurate positioning of the lower limb rehabilitation exoskeleton robotic system. Full article
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26 pages, 6569 KiB  
Article
Design of a Wearable Exoskeleton Piano Practice Aid Based on Multi-Domain Mapping and Top-Down Process Model
by Qiujian Xu, Meihui Li, Guoqiang Chen, Xiubo Ren, Dan Yang, Junrui Li, Xinran Yuan, Siqi Liu, Miaomiao Yang, Mufan Chen, Bo Wang, Peng Zhang and Huiguo Ma
Viewed by 635
Abstract
This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy [...] Read more.
This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy and daily activity assistance, challenges remain in performing highly dexterous tasks due to structural complexity and insufficient motion accuracy. To address these issues, we developed a modular division method based on multi-domain mapping and a top-down process model. This method integrates the functional domain, structural domain, and user needs domain, and explores the principles and methods for creating functional construction modules, overcoming the limitations of traditional top-down approaches in design flexibility. By closely combining layout constraints with the design model, this method significantly improves the accuracy and efficiency of module configuration, offering a new path for the development of piano practice assistance devices. The results demonstrate that this device innovatively combines piano practice with rehabilitation training and through the introduction of ontological modeling methods, resolves the challenges of multidimensional needs mapping. Based on five user requirements (P), we calculated the corresponding demand weight (K), making the design more aligned with user needs. The device excels in enhancing motion accuracy, interactivity, and comfort, filling the gap in traditional piano assistance devices in terms of multi-functionality and high adaptability, and offering new ideas for the design and promotion of intelligent assistive devices. Simulation analysis, combined with the motion trajectory of the finger’s proximal joint, calculates that 60° is the maximum bending angle for the aforementioned joint. Physical validation confirms the device’s superior performance in terms of reliability and high-precision motion reproduction, meeting the requirements for piano-assisted training. Through multi-domain mapping, the top-down process model, and modular design, this research effectively breaks through the design flexibility and functional adaptability bottleneck of traditional piano assistance devices while integrating neurological rehabilitation with music education, opening up a new application path for intelligent assistive devices in the fields of rehabilitation medicine and arts education, and providing a solution for cross-disciplinary technology fusion and innovative development. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 2nd Edition)
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12 pages, 1186 KiB  
Article
Cardiac Function and Fatigue During Exoskeleton-Assisted Sit-to-Stand Maneuver and Walking in People with Stroke with Moderate to Severe Gait Disability: A Pilot Cross-Sectional Study
by Raimondas Kubilius, Darius Ruočkus, Vitalija Stonkuvienė, Rugilė Vareikaitė, Rebecca Cardini and Thomas Bowman
Sensors 2025, 25(1), 172; https://doi.org/10.3390/s25010172 - 31 Dec 2024
Viewed by 562
Abstract
Background. Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an [...] Read more.
Background. Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an exoskeleton. Methods. Five PwST were recruited in this pilot cross-sectional study. We observed three experimental conditions: walking without and with the UAN.GO exoskeleton and walking with the UAN.GO combined with the OPTIGO walker. Each condition included five trials related to ADLs such as sitting and walking. Results. No statistically significant difference was found between heart rate and R–R of ECG data while comparing all the observed conditions during each respective trial. The NASA Task Load Index did not show significant differences across all trials, except for a significant difference between Condition 2 and Condition 3 in Trial 4 (p = 0.043). However, walking and sit-to-stand tasks seem to be more challenging according to the NASA-TLX. Only one participant scored over 70 points on the System Usability Scale. The TSQ-WT scores for conditions 2 and 3 were 62 (56.5–72.5) and 70 (66.5–75) points, respectively. Conclusions. This study suggests that UAN.GO exoskeleton could be used for RAGT in PwST with disability without compromising cardiovascular function. Full article
(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)
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20 pages, 3392 KiB  
Article
Impedance Controller Analysis for a Two-Degrees-Of-Freedom Ankle Rehabilitation Machine with Serious Game Interactions
by Oscar I. Cirilo-Piñon, Agustín Barrera-Sánchez, Cesar H. Guzmán-Valdivia, Manuel Adam-Medina, Rafael Campos-Amezcua, Andrés Blanco-Ortega and Arturo Martínez-Mata
Viewed by 383
Abstract
An ankle sprain can be caused by daily activities such as running, walking, or playing sports. In many cases, the patient’s ankle suffers severe or permanent damage that requires rehabilitation to return to its initial state. Thanks to technological advances, robotics has allowed [...] Read more.
An ankle sprain can be caused by daily activities such as running, walking, or playing sports. In many cases, the patient’s ankle suffers severe or permanent damage that requires rehabilitation to return to its initial state. Thanks to technological advances, robotics has allowed for the development of machines that generate precise, efficient, and safe movements. In addition, these machines are manipulated by a specific control depending on the rehabilitation objective. Impedance control is used in ankle rehabilitation machines for active–resistive-type rehabilitation, where the patient participates by exerting a force on the machine repeatedly. Serious games are an example of an activity where the patient can interact with a video game while rehabilitating. Currently, most machines involving impedance control and targeted at serious gaming applications are mechanically composed of one degree of freedom, so the addition of another degree is a novelty. This paper presents simulation results comparing different impedance controls reported in the literature to determine the best option for applying a 2-DOF ankle rehabilitation machine using serious games. The results obtained are presented by comparing them according to the force applied to the rehabilitation machine (emulating the behavior of a patient). From the impedance controllers analyzed for horizontal (abduction/adduction) and vertical (dorsiflexion/plantarflexion) movements in the rehabilitation machine, it was determined that the PD control, which considers some mechanical parameters, presents a better performance. With this controller, fast and smooth angular movements are generated, while the consumption of kinetic energy is kept in a low range, proportional to the applied forces, compared to the other impedance controls analyzed. Full article
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