Exoskeleton Research Articles

The role of electroencephalography in epilepsy research-From seizures to interictal activity and comorbidities.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and othercomorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

A Systematic Review of Locomotion Assistance Exoskeletons: Prototype Development and Technical Challenges

Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in exoskeleton technology published since 2020, with particular emphasis on the development of structural designs for lower-limb exoskeletons employed in locomotion assistance. We employed a systematic literature review methodology, categorizing the included studies into three main types: rigid exoskeleton, soft exoskeleton, and tethered platform. The current development status of robotic exoskeletons is analyzed based on publication year, system weight, target assistive joints, and main effects. Furthermore, we examine the factors driving these advancements and their implications for the field. The key challenges and opportunities that may influence the future development of exoskeleton technologies are also highlighted in this review.

Effectiveness of Powered Hand Exoskeleton on Upper Extremity Function in People with Chronic Stroke

Impairment of upper limb function is common after a stroke and is closely linked to decreased functional independence in activities of daily living. Robot-assisted training has been used in clinical settings to improve hand function in stroke patients; however, many existing devices are costly and require specialized training to operate. This study aimed to propose a novel powered hand exoskeleton (EO) and verify its effectiveness on upper extremity function in people with chronic stroke. Thirty participants were randomly assigned to either the experimental group or the control group. Each participant underwent 30 min interventions twice a week for 8 weeks. The experimental group received 15 min of conventional therapy followed by 15 min of training with the powered hand EO, while the control group received 30 min of conventional therapy. The primary outcome measures included the Fugl-Meyer Assessment for upper extremity function (FMA-UE), the Box and Block Test (BBT), and handgrip dynamometer. Assessments were conducted at baseline and then at 4-week intervals throughout the 8-week period. Results showed that, after the 8-week intervention, the average changes in FMA-UE scores for the experimental group were significantly greater than those for the control group (p < 0.01). A clear upward trend in both FMA-UE and BBT scores was observed in the EO group. Statistical analysis revealed significant improvements in the overall, proximal, and distal components of the FMA-UE scores (all p < 0.01) and in BBT scores (both p < 0.05) in the EO group compared to the control group at 4 and 8 weeks, respectively. However, no significant differences in grip strength were observed between the groups at either time point. Our findings suggest that the proposed powered hand EO is both feasible and safe for training the impaired hand in stroke survivors. Given the characteristics of the device, it has potential for use in hand rehabilitation aimed at regaining upper extremity function.

An elbow passive exoskeleton with controllable assistance: Design and experimental.

A passive exoskeleton is a wearable robotic device that is worn on the exterior of the user's body to provide physical support and facilitate movement. Existing elbow passive exoskeletons have limitations in their assistance capabilities and range of applications. In this paper, we propose a controllable elbow passive exoskeleton (CEPE) to address these limitations. The CEPE features a ratchet-based self-energy storage mechanism (RSSM) and a Candan gravity compensation mechanism (CGCM). The CGCM counteracts gravitational forces, while the RSSM stores and releases motion energy. This paper establishes a mathematical model for the RSSM, outlines design specifications for both the RSSM and CEPE, and analyzes the influence of design parameters on the power assistance performance. Three experiments were conducted to validate the feasibility of CEPE, including static strength testing, power assistance without load, and power assistance with load. Results show that, without loading, the CEPE provides compensation effects on elbow joint torque of 68.8%, 93.8%, and 70.7% at shoulder joint angles of 0°, 30°, and 60°, respectively. With a 5kg loading, adjusting the shoulder joint angle from 30° to 60° results in an increase in the decrease of the average absolute torque directly acting on the elbow joint, from 86% to 91.2%. The adjustable RSSM enables the CEPE to operate in four different modes, expanding its potential applications.

Robotic Ankle Exoskeleton and Limb Angle Biofeedback for Assisting Stroke Gait: A Feasibility Study

Robotic Ankle Exoskeleton and Limb Angle Biofeedback for Assisting Stroke Gait: A Feasibility Study

Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography

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.

Exoskeleton Robot Training in Two Patients with an Electrical Burn and Septic Arthritis: A Case Report.

Septic arthritis (SA) are rare in patients with burns, but delayed treatment can result in irreversible joint destruction. Early diagnosis and immediate treatment are necessary to prevent joint destruction. Robot training in patients with musculoskeletal diseases and burns, can improve joint range of motion (ROM), muscle strength, and lower extremity function. The aim of this report is to present robot training utility in patient with lower extremity electrical burns and associated SA. Rebless® (H-ROBOTICS, KOREA) for ROM and strength training, can operate in passive or active modes in knee or ankle flexion and extension. Rebless® works by providing visual feedback on angles during flexion and extension training. Two participants, diagnosed with SA after burns, and unable to walk before training because of joint pain, limited ROM, and muscle weakness, underwent 30min of robot training using Rebless® with 30min conventional therapy, 5 days a week for 8 weeks. After training, the gait function, muscle strength, and pain scores of the participants improved without adverse effects on joint ROM. This report is the first to demonstrate that robot training has a positive effect on gait function, pain, muscle strength with no soft tissue contractures or other complications in a patient with burn injury and SA.

Machine Learning Models for Assistance from Soft Robotic Elbow Exoskeleton to Reduce Musculoskeletal Disorders

Musculoskeletal disorders are very common injuries among occupational and healthcare workers. These injuries are preventable in many scenarios using exoskeleton-based assistive technology. Soft robotics initiates an evolution in exoskeleton devices due to their safe human interactions, ergonomic design, and adaptive characteristics. Despite their enormous advantages, it is a challenging task to model and control soft robotic devices due to their inherent nonlinearity and hysteresis. Learning-based approaches are becoming more popular to overcome these limitations. This work proposes an approach to estimate the pressure input for a pneumatically actuated soft robotic elbow exoskeleton to assist occupational workers to avoid musculoskeletal disorders. An elbow exoskeleton design made up of modular pneumatic soft actuators is discussed, which helps to flex/extend an elbow joint. Machine learning (ML) approaches are used to develop a relationship between the air pressure, the bending angle of the elbow, and the percentage of the weight of the arm to be assisted by the exoskeleton. The most popular and widely used regression-based ML approaches are applied and compared in terms of accuracy and computation cost. Further, a modified KNN (K-Nearest Neighbor) approach is proposed, which outperforms the accuracy of other approaches.

Simulation Based Analysis and Design of Passive Back Exoskeleton for Squat Lifting

Abstract Back pain is the most common musculoskeletal disorder that manual material handling laborers face as they lift heavy objects. To overcome the back pain problem, an assistive device (Exoskeleton) can be worn during lifting. Through musculoskeletal simulation, this work investigates how the passive back exoskeleton affects the spinal muscle forces and spinal joint reaction loads. Such simulation findings can provide insights to improve the back exoskeleton design further. For this purpose, the passive exoskeleton is modeled as a set of elastic elements (springs), and dynamic analysis for a lifting cycle is done during a squat lifting task. Musculoskeletal simulations were carried out in OpenSim software utilizing the open-source OpenSim lifting full body model. The model is modified by adding an external spring that provides assistive forces like a passive exoskeleton. During the whole lifting cycle, the erector spine muscle is strained, so unloading the erector spine muscle is the primary goal of most back support exoskeletons. This work aims to identify optimal spring configurations and design parameters for a passive back exoskeleton to aid in squat lifting by varying spring parameters and through a formulation of a multi-objective optimization problem. The optimized back exoskeleton is expected to reduce compressive and shear forces on the L5S1 joint during lifting.

Evaluating the interaction between human and paediatric robotic lower-limb exoskeleton: a model-based method

Abstract Lower-limb exoskeletons (LLEs) have shown potential in improving motor function in patients for both clinical rehabilitation and daily life. Despite this, the development and control of pediatric exoskeletons remain notably underserved. This study focuses on a unique pediatric robotic lower limb exoskeleton (PRLLE), tailored particularly for children aged 8–12. Each leg of the robot has 5 Degrees of Freedom (DOFs)—three at the hip and one each at the knee and ankle. The interaction between the child user and the PRLLE is intricate, necessitating adherence to essential requirements of comfort, safety, and adaptability. Testing numerous prototype variations against diverse user profiles, particularly for children with neurological disorders where each child differs, is impractical. Model-based methods offer a virtual testbed that is useful in the design stage. This study uses MATLAB® to simulate and evaluate the interaction between users and PRLLE after deriving the nonlinear dynamic model of the PRLLE, which is simplified through multiple layers. To verify the accuracy of the derived dynamic model, a Computed Torque Control method is employed. The study provides detailed outcomes for children aged 8, 10, and 12 years, for passive and active users along with variations in PRLLE assistance levels. The study shows significant reductions in human joint torques, up to 56%, alongside substantial actuator powers, reaching up to 98W, for a 10-year-old child user. Furthermore, examining 8 and 12-year-old child users revealed variations in interaction forces, with changes up to 29.5%. Consequently, meticulous consideration of the human user’s limitations is crucial during the PRLLE’s design and conceptualization phases, particularly for PRLLEs.

Surmounting the ceiling effect of motor expertise by novel sensory experience with a hand exoskeleton.

For trained individuals such as athletes and musicians, learning often plateaus after extensive training, known as the "ceiling effect." One bottleneck to overcome it is having no prior physical experience with the skill to be learned. Here, we challenge this issue by exposing expert pianists to fast and complex finger movements that cannot be performed voluntarily, using a hand exoskeleton robot that can move individual fingers quickly and independently. Although the skill of moving the fingers quickly plateaued through weeks of piano practice, passive exposure to otherwise impossible complex finger movements generated by the exoskeleton robot at a speed faster than the pianists' fastest one enabled them to play faster. Neither a training for fast but simple finger movements nor one for slow but complex movements with the exoskeleton enhanced the overtrained motor skill. The exoskeleton training with one hand also improved the motor skill of the untrained contralateral hand, demonstrating the intermanual transfer effect. The training altered patterns of coordinated activities across multiple finger muscles during piano playing but not in general motor and somatosensory functions or in anatomical characteristics of the hand (range of motion). Patterns of the multifinger movements evoked by transcranial magnetic stimulation over the left motor cortex were also changed through passive exposure to fast and complex finger movements, which accompanied increased involvement of constituent movement elements characterizing the individuated finger movements. The results demonstrate evidence that somatosensory exposure to an unexperienced motor skill allows surmounting of the ceiling effect in a task-specific but effector-independent manner.

A new model for radiocarbon dating of marine shells from the Netherlands

Abstract A new model for the interpretation of radiocarbon (14C) dates of Holocene marine shells is presented. For the Netherlands, the size of reservoir effect is difficult to assess, as these shells often lived in an environment of mixed marine- and river waters. Both stable isotopes 13C and 18O of the shell carbonate give insight in the environmental conditions the shells lived in. River water occurs in two main categories, distinguished by 18O: the Rhine which is dominant, and other rivers. This leads to two estuary mixing lines between the North Sea and rivers. The stable isotopes of the shell carbonate are also indicative for additional processes, such as uptake of secondary carbonate from the soil by shells, and exchange of C isotopes between atmosphere and water. Extensions of the main model deal with special cases such as pools of stagnant water and lakes. The model leads to an assessment of the recent 14C activities of the system the shells lived in, called 14aSYS. The measured 14C activities relative to these 14aSYS values determine the 14C age of the shells and include the reservoir effect. This way we circumvent normalizing to δ13C = –25‰, i.e. the terrestrial timescale and the subsequent correction for reservoir effects. The model is applied to a large legacy dataset of marine shells from the Netherlands, obtained during the last 7 decades. It contains 1116 14C dated shells; for the majority of these, the 3 isotopes 13C, 14C and 18O are measured.

Annotations on Lower-Limb Exoskeleton in Different Parts and Successful Commercial Rehabilitation Robots

The exoskeleton is always a famous topic for robot-human relations. In the novel and movie, exoskeleton robots function as additional bodies or arms for human users. However, because of the restrictions of materials and some detection skills, people cannot achieve such a goal. The research on exoskeletons nowadays mainly focuses on rehabilitation and military use. Because of the low cost of exoskeletons in rehabilitation and their safety, exoskeletons become a common and good choice for hospitals and nursing homes, especially lower-limb exoskeletons for paralysis or spinal cord injury patients who need to do rehabilitation for walking ability. This overview will first examine the algorithms for hip exoskeletons, followed by an analysis of using the new type of component in knee exoskeleton and then the annotation system of ankle exoskeletons, and last, talk about the algorithm for one successfully commercialized lower-limb exoskeleton. This comprehensive review aims to contribute to the field by providing valuable insights for researchers and practitioners in lower-limb exoskeleton development.

Development and Evaluation of Passive Exoskeleton (SolatExo) for Assisting Muslims with Physical Disability in Salah

Development and Evaluation of Passive Exoskeleton (SolatExo) for Assisting Muslims with Physical Disability in Salah

Quantum technologies in robotics: a preliminary appraisal

PurposeThe purpose of this paper is to provide details of developments in quantum technologies and consider their potential applications in robotics.Design/methodology/approachFollowing a short introduction, this study first provides an overview of the global quantum technology landscape. It then discusses developments in quantum computing and sensing technologies. Potential applications in robotics are then considered and finally, brief conclusions are drawn.FindingsQuantum technologies are the topic of a rapidly growing global R&D effort. Quantum computing has the potential to conduct conventional computations far more rapidly than traditional computers and solve complex problems that are presently challenging or impossible. If realised, robotic applications could include enhanced route planning, machine learning and data fusion. Quantum position and magnetic field sensors have the potential to revolutionise navigation systems in airborne, land and marine robots and overcome limitations of GPS and inertial measurement units. Magnetic sensors also have a role in health care in the control of robotic prostheses and exoskeletons and in brain–computer interface techniques. Quantum radar, lidar and imaging systems stand to outperform their conventional counterparts, and applications are anticipated in military and civilian robots. Quantum technologies are still at an early stage of development, and much progress will be made in the future, opening up many further robotic applications.Originality/valueThis study provides an insight into quantum technology developments and their potential applications in robotics.

Acceptability of Overground Wearable Powered Exoskeletons for People with Spinal Cord Injury: A Multicenter Qualitative Study.

Background: Exoskeletons are used in rehabilitation centers for people with spinal cord injuries (SCI) due to the potential benefits they offer for locomotor rehabilitation. The acceptability of exoskeletons is crucial to promote rehabilitation and to ensure a successful implementation of this technology. The objective was to explore the acceptability of overground wearable powered exoskeleton used in rehabilitation among people with SCI. Methods: Fourteen individuals with SCI (9 men, mean [SD] age 47 years [14.8], a majority with traumatic and thoracic lesion (T6-T12)) who had utilized an exoskeleton in Canada or in France during their rehabilitation participated in a semi-structured interview. A thematic analysis using the theoretical framework of acceptability was carried out. Results: Participants were motivated to use an exoskeleton during their rehabilitation. They reported several perceived benefits to its use, including better walking pattern, increased endurance, and greater muscle mass. They also experienced mild pain, notable concentration demands, and fatigue. Most participants reported that using exoskeletons in their rehabilitation process was appropriate and relevant to them. Conclusions: Exoskeletons are generally well accepted by participants in this study. Adjustments in their use, such as conducting training sessions in obstacle-free environment and technological improvements to address the device's restrictive characteristics, heaviness, and massiveness are however still needed.

Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review

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.

Swarm-initialized adaptive controller with beetle antenna searching of wearable lower limb exoskeleton for sit-to-stand and walking motions.

In recent years, exoskeleton robots have attracted great interest from researchers in the area of robotics due to their ability to assist human functionality improvement. A wearable lower limb exoskeleton is aimed at supporting the limb functionality rehabilitation process and to assist physical therapists. Development of a stable and robust control system for multi-joint rehabilitation robots is a challenging task due to their non-linear dynamic systems. This paper presents the development of a Swarm-Initialized Adaptive (SIA) based controller, which is a combination of a swarm-based intelligence, named Swarm Beetle Antenna Searching (SBAS), and an adaptive Lyapunov-based controller. The SBAS initializes the parameters of SIA to efficiently improve the performance of the control system and then these controller parameters are updated by an adaptive controller. The control system is validated in a lower limb exoskeleton prototype with four degrees of freedom, using a healthy human subject for sit-to-stand and walking motions. The experimental results show the applicability of the proposed method and demonstrate that our approach obtained efficient control performance in terms of steady-state error and robustness and can be used for a lower limb exoskeleton to improve human mobility.

Robotic Exoskeleton with Mechanically Implemented Kinematic Synergy for Quadrupedal Gait of Rats

Robotic Exoskeleton with Mechanically Implemented Kinematic Synergy for Quadrupedal Gait of Rats

Explicit model based fuzzy control method for lower limb exoskeleton robot

Explicit model based fuzzy control method for lower limb exoskeleton robot