Exoskeleton Research Articles

Modeling Neuromotor Adaptation to Pulsed Torque Assistance During Walking.

Multiple mechanisms of motor learning contribute to the response of individuals to robot-aided gait training, including error-based learning and use-dependent learning. Previous models described either of these mechanisms, but not both, and their relevance to gait training is unknown. In this paper, we establish the validity of existing models to describe the response of healthy individuals to robot-aided training of propulsion via a robotic exoskeleton, and propose a new model that accounts for both use-dependent and error-based learning. We formulated five state-space models to describe the stride-by-stride evolution of metrics of propulsion mechanics during and after robot-assisted training, applied by a hip/knee robotic exoskeleton for 200 consecutive strides. The five models included a single-state, a two-state, a two-state fast and slow, a use-dependent learning (UDL), and a newly-developed modified UDL model, requiring 4, 9, 5, 3, and 4 parameters, respectively. The coefficient of determination (R 2) and Akaike information criterion (AIC) values were calculated to quantify the goodness of fit of each model. Model fit was conducted both at the group and at the individual participant level. At the group level, the modified UDL model shows the best goodness-of-fit compared to other models in AIC values in 15/16 conditions. At the participant level, both the modified UDL model and the two-state model have significantly better goodness-of-fit compared to the other models. In summary, the modified UDL model is a simple 4-parameter model that achieves similar goodness-of-fit compared to a two-state model requiring 9 parameters. As such, the modified UDL model is a promising model to describe the effects of robot-aided gait training on propulsion mechanics.

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryoticand viral multi-omics from either short- or long-read sequencing.

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

Effects of exoskeleton-assisted walking on bowel function in motor-complete spinal cord injury patients: involvement of the brain-gut axis, a pilot study.

Evidence has demonstrated that exoskeleton robots can improve intestinal function in patients with spinal cord injury (SCI). However, the underlying mechanisms remain unelucidated. This study investigated the effects of exoskeleton-assisted walking (EAW) on intestinal function and intestinal flora structure in T2-L1 motor complete paraplegia patients. The results showed that five participants in the EAW group and three in the conventional group reported improvements in at least one bowel management index, including an increased frequency of bowel evacuations, less time spent on bowel management per day, and less external assistance (manual digital stimulation, medication, and enema usage). After 8 weeks of training, the amount of glycerol used in the EAW group decreased significantly (p <0.05). The EAW group showed an increasing trend in the neurogenic bowel dysfunction (NBD) score after 8 weeks of training, while the conventional group showed a worsening trend. Patients who received the EAW intervention exhibited a decreased abundance of Bacteroidetes and Verrucomicrobia, while Firmicutes, Proteobacteria, and Actinobacteria were upregulated. In addition, there were decreases in the abundances of Bacteroides, Prevotella, Parabacteroides, Akkermansia, Blautia, Ruminococcus 2, and Megamonas. In contrast, Ruminococcus 1, Ruminococcaceae UCG002, Faecalibacterium, Dialister, Ralstonia, Escherichia-Shigella, and Bifidobacterium showed upregulation among the top 15 genera. The abundance of Ralstonia was significantly higher in the EAW group than in the conventional group, and Dialister increased significantly in EAW individuals at 8 weeks. This study suggests that EAW can improve intestinal function of SCI patients in a limited way, and may be associated with changes in the abundance of intestinal flora, especially an increase in beneficial bacteria. In the future, we need to further understand the changes in microbial groups caused by EAW training and all related impact mechanisms, especially intestinal flora metabolites. Clinical trial registration: https://www.chictr.org.cn/.

Updating the Montreal walking exoskeleton satisfaction and perspectives questionnaire (MWESP-Q) following a 16-week walking program with the use of a wearable robotic exoskeleton

BACKGROUND: Physical activity as one of the major lifestyle-related health determinants is partially addressed by the Montreal Walking Exoskeleton Satisfaction and Perspectives-Questionnaire (MWESP-Q). OBJECTIVE: To document satisfaction of people with chronic spinal cord injury after the completion of a 10 to 16 weeks of the wearable robotic exoskeleton-assisted walking program, with the MWESP-Q updated to a context of health promotion. METHODS: Following a walking program (10–16 weeks), wheelchair users with chronic SCI completed the MWESP-Q online. Modification of the original questionnaire was conducted with 4 experts to ensure its content validity with a human framework to promote physical activity for health. RESULTS: Ten wheelchair users completed the questionnaire (men = 6; 45.8 ± 13.4 years, SCI duration: 10.1 ± 5.8 years). Participants strongly agreed to be satisfied with the overall program; agreed to be satisfied towards exoskeleton, motivation to engage in physical activity, learnability and program attributes; rated “medium effort” for physical and cognitive exertion during training; reported light improvements for health benefit domain, but light to moderate improvements for general endurance (mean 5.5 /7, SD 1.4) and psychological well-being (mean 5.7 /7, SD 1.3). CONCLUSIONS: The updated MWESP-Q is now better equipped to measure physical and cognitive efforts in physical activity and changes in body and organic systems and in capabilities (health promotion). The updated MWESP-Q has 54 statements (14 additional statements and 1 deleted) organized around seven domains. The original measure was replaced by three 7-point Likert scales, one regarding agreement level (40 statements), level of effort (12 statements), and level of change (2 statements).

Poet-Composer Collaborations in Modern Egyptian Song: A Social Network Analysis Approach to Music History

In this paper I formulate and apply a social network analysis methodology for understanding the history of song on Egyptian radio. Music is a massively relational cultural form, involving interactions among composers, poets, arrangers, conductors, and performers, among others. The reality of music history thus emerges as a complex network of relationships, unfolding and changing over time. Song production, in particular, centers on poet-composer-singer collaborations. Many Arab music histories highlight narratives of the stars, presented in historical and cultural context, but neglect the broader network of productive relationships. However many important musical figures are not celebrities, and the full complexity of the non-linear network can only be grasped holistically, including big data empirical analysis, not pointillistically through case studies of celebrities. Such holistic analysis can reveal surprising emergent, structural patterns that are not apparent in any single narrative. Social network analysis (SNA) offers a powerful suite of tools enabling such an approach, including metrics for centrality and the detection of cohesive subgroups. Starting with a large dataset of songs broadcast on Egyptian radio, I extract a network of poet-composer collaborations, and apply SNA algorithms to reveal its social structure. I then interpret that structure in light of wider socio-cultural and historical factors. In this way, my paper both sheds light on Egypt’s musical history, and supplies a model and method that may be applied, mutatis mutandis, to other musical domains.

Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition

Research on Lower Limb Motion Recognition (LLMR) based on various wearable sensors has been widely applied in exoskeleton robots, exercise rehabilitation, etc. Typically, employing multimodal information tends to yield higher accuracy and stronger robustness compared to using unimodal information. Due to the inevitable reliance on feature engineering in shallow machine learning-based LLMR methods, this study leverages the powerful non-linear feature mapping capability of deep learning (DL) to construct several end-to-end LLMR frameworks, including: Convolutional Neural Networks (CNNs), CNN-Recurrent Neural Networks (RNNs) and CNN-Graph Neural Networks (GNNs). The effectiveness of the proposed frameworks is verified in distinct tasks, including the recognition of seven types of lower limb motions in healthy subjects and three types of motions in patients with stroke, as well as the phase recognition task during the sit-to-stand (SitTS) process in patients with stroke, achieving the highest mean accuracy of 95.198 %, 99.784 %, and 99.845 %, respectively. Further research and integration of two transfer learning techniques, adaptive Batch Normalization (BN) and model fine-tuning, significantly enhance the applicability of the proposed frameworks in inter-subject prediction. Additionally, systematic analyses are conducted to assess the strengths and weaknesses of different models in terms of recognition performance, complexity, and adaptability to variations in the number of modalities and sensor channels. Experimental results indicate that the proposed frameworks hold promise in providing potential support for the development of human-robot collaborative lower limb exoskeletons or rehabilitation robots.

Preliminary Virtual Constraint-Based Control Evaluation on a Pediatric Lower-Limb Exoskeleton.

Pediatric gait rehabilitation and guidance strategies using robotic exoskeletons require a controller that encourages user volitional control and participation while guiding the wearer towards a stable gait cycle. Virtual constraint-based controllers have created stable gait cycles in bipedal robotic systems and have seen recent use in assistive exoskeletons. This paper evaluates a virtual constraint-based controller for pediatric gait guidance through comparison with a traditional time-dependent position tracking controller on a newly developed exoskeleton system. Walking experiments were performed with a healthy child subject wearing the exoskeleton under proportional-derivative control, virtual constraint-based control, and while unpowered. The participant questionnaires assessed the perceived exertion and controller usability measures, while sensors provided kinematic, control torque, and muscle activation data. The virtual constraint-based controller resulted in a gait similar to the proportional-derivative controlled gait but reduced the variability in the gait kinematics by 36.72% and 16.28% relative to unassisted gait in the hips and knees, respectively. The virtual constraint-based controller also used 35.89% and 4.44% less rms torque per gait cycle in the hips and knees, respectively. The user feedback indicated that the virtual constraint-based controller was intuitive and easy to utilize relative to the proportional-derivative controller. These results indicate that virtual constraint-based control has favorable characteristics for robot-assisted gait guidance.

Active Power Assist with Equivalent Force on Connection for Lower Limb Exoskeleton Robots

Active power-assist lower limb exoskeleton robots aim to enhance wearer assistance while ensuring wearer comfort and simplifying the exoskeleton’s design and control. This study proposes an active assistance method known as Equivalent Force on Connection (EFOC). The EFOC method effectively addresses the limitations encountered in conventional Joint Torque Proportional Compensation (JTPC) approaches. These limitations include the necessity for exoskeleton robot configurations to align with human limb structures for parallel assistance at each lower limb joint, as well as the exoskeleton’s inability to contribute a greater proportion of assistance due to the excessive load on specific skeletal and muscular structures, resulting in wearer discomfort. Furthermore, the effectiveness of the EFOC method is evaluated and validated for assistance during both the stance and swing phases of single-leg movements. Finally, the proposed EFOC method is implemented on a hydraulic-driven lower limb exoskeleton robot to assist wearers in squatting, stepping, and jumping locomotion. The experimental results demonstrate that the proposed EFOC method can effectively achieve the desired assistance effect.

Preliminary Testing of a Passive Exoskeleton Prototype Based on McKibben Muscles

Upper-limb exoskeletons for industrial applications can enhance the comfort and productivity of workers by reducing muscle activity and intra-articular forces during overhead work. Current devices typically employ a spring-based mechanism to balance the gravitational torque acting on the shoulder. As an alternative, this paper presents the design of a passive upper-limb exoskeleton based on McKibben artificial muscles. The interaction forces between the exoskeleton and the user, as well as the mechanical resistance of the exoskeleton structure, were investigated to finalize the design of the device prior to its prototyping. Details are provided about the solutions adopted to assemble, wear, and regulate the exoskeleton’s structure. The first version of the device weighing about 5.5 kg was manufactured and tested by two users in a motion analysis laboratory. The results of this study highlight that the exoskeleton can effectively reduce the activation level of shoulder muscles without affecting the lumbar strain.

Development and Validation of Robotic Ankle Exoskeleton With Parallel Nonlinear Elastic Actuator

Abstract This paper presents the development of a robotic ankle exoskeleton for human walking assistance. First, the biomechanical properties of a human ankle joint during walking are presented. Next, design of the robotic ankle exoskeleton is introduced. The exoskeleton is actuated by a novel parallel nonlinear elastic actuator. The cam-spring mechanism in the actuator can function as a parallel nonlinear spring with an adjustable stiffness, and the design of the cam profile curve is described. Additionally, an adaptive controller is proposed for the exoskeleton to generate a desired assistive torque according to the wearer's total weight. Finally, experiments are conducted to validate the effectiveness of the developed robotic ankle exoskeleton. The experimental results demonstrate that during a gait cycle, reductions of 42.7% and 40.1% of the peak and average currents of the driving motor in the actuator are observed, respectively, with the designed cam-spring mechanism. A peak assistive torque of 23.9 Nm can be provided for the wearers by the exoskeleton during walking. With the assistance provided by the exoskeleton, the average and peak soleus activities of the wearers during a gait cycle are decreased by 25.42% and 31.94%, respectively.

BiLSTM-KNN network for locomotion mode recognition in lower limb exoskeleton robot

Abstract Lower limb exoskeleton robots have become a research hotspot among various enterprises, universities, and institutions. This paper proposes a deep learning network based on the recognition of five movement patterns of lower limb exoskeletons, including walking on level ground, going upstairs, going downstairs, uphill, and downhill. The network uses BiLSTM to capture the forward and backward relationships of time series data, which has better resolution than the original features. In addition, the network uses the KNN algorithm based on shortest-distance voting to improve the recognition accuracy. The network achieved a recognition rate of 99.18% for five motion patterns. In summary, the proposed network has excellent performance in terms of accuracy and generalization.

Design and analysis of a lower limb assistive exoskeleton robot.

In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots that can be worn on a human body and provide additional strength, speed or other abilities. Exoskeleton robots have a wide range of applications, such as medical rehabilitation, logistics and disaster relief and other fields. The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordination of man-machine movement and the comfort of wearing. Then a gait prediction method based on neural network was designed. In addition, a control strategy according to iterative learning control was designed. The experiment results showed that the proposed exoskeleton robot can produce effective assistance and reduce the wearer's muscle force output. A lower limb assistive exoskeleton robot was introduced in this paper. The kinematics model and dynamic model of the exoskeleton robot were established. Tracking effects of joint angle displacement and velocity were analyzed to verify feasibility of the control strategy. The learning error of joint angle can be improved with increase of the number of iterations. The error of trajectory tracking is acceptable.

Research on exoskeleton compliance control strategy based on dual interaction torque split phase control method.

Human gait pattern recognition and compliance control are key technologies for achieving high coordination and assistance between exoskeleton robots and human movements. In order to improve the adaptability of exoskeleton robots to the human body, this paper proposes an exoskeleton compliance control strategy based on dual interaction torque phase separation control method. A support phase swing phase split control strategy based on dual interaction torque is proposed. Utilize the interaction force of human joints and adopt a model-based method to control the support phase. By utilizing the interaction force of exoskeleton joints and using a torque closed-loop method to control the swing phase, a multi-state control method of motion is achieved. A lower limb exoskeleton knee joint testing platform is built to verify the proposed human gait recognition The effectiveness of human-machine interaction force identification and human-machine coupling system compliance control technology. The proposed control method can effectively adjust joint torque, enabling the exoskeleton robot to maintain balance and stability during the entire walking phase.

Passive and Active Training Control of an Omnidirectional Mobile Exoskeleton Robot for Lower Limb Rehabilitation

Passive and Active Training Control of an Omnidirectional Mobile Exoskeleton Robot for Lower Limb Rehabilitation

Current research on exoskeletal lower limb rehabilitation robots

Stroke is characterized by a high incidence rate, and walking is one of the essential human functions. Lower limb rehabilitation robots can offer safe and effective walking training to patients with walking impairments caused by conditions such as neural injuries, aiding in their recovery or assisting in walking. For the needs of patients themselves, restoring their physical state and motor function as much as possible is the primary purpose of rehabilitation, and rehabilitation efficiency is an important factor affecting the quality of life of patients after surgery. Research on exoskeleton robots varies both domestically and internationally, each with its own unique characteristics and features. This paper outlines the design of lower limb rehabilitation robots based on gait analysis and control methods. It also provides an overview of the current state of research on lower limb exoskeleton robots in terms of degrees of freedom, actuation methods, and clinical applications. Furthermore, it offers insights into future directions for research on lower limb exoskeleton robots.

Mechanism Analysis and Optimization Design of Exoskeleton Robot with Non-Circular Gear–Pentabar Mechanism

To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to constrain the degrees of freedom of the R-para-rod mechanism. In this study, the kinematic model of a non-circular gear–five−rod mechanism is established based on motion mapping theory by obtaining the normal motion positions of the human lower limb. An optimization design software for the non-circular gear–five−rod mechanism is developed using the MATLAB 2018b visualization platform, with the non−circular active gear as the sole input variable. A set of ideal parameters is obtained through parameter adjustment and optimal parameter selection, and the corresponding trajectories are compared with human trajectories. The three−dimensional model of the mechanism is established according to the obtained parameters, and the motion simulation of the non−circular gear–five−bar mechanism demonstrates that the mechanism can better reproduce the expected human knee joint motion posture, meeting the working requirements of an exoskeleton knee robot.

Giant Humanoid Mobility Robot Suit-Method II and Research Challenges

Robot suits can amplify human power, mobility, and height. A giant humanoid mobility robot-Method II was designed using the concept of battle robot suit, AMP in the movie Avatar. The operator aboard Method II is a giant robot that can expand its movement to five times the human muscle strength and 2.5 times the height. The robot suit-type controller can move in synchronization with the movement of both arms and control fingers to collaborate with various tasks. However, there are critical challenges, such as battery weight and duration inefficiency, control complexity, network stability, and difficulties in failure or repair work. This study develops and operates the world’s largest humanoid mobility robot without support fixture up to now, and presents its five future research challenges.

Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography.

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively.

Pneumatically Actuated Soft Robotic Hand and Wrist Exoskeleton for Motion Assistance in Rehabilitation

Exoskeletons are being explored for assisting motion therapy for neurological impairment-related rehabilitation. Soft robotic exoskeletons are gaining more attention for upper-extremity applications due to their simplistic actuation mechanisms and compliant nature. To regain fine motor hand functions, it is desired to have both hand and wrist motions in a coordinated fashion, as most daily living tasks require a combination of both hand and wrist joint motions. However, a soft robotic exoskeleton with hand and wrist motion together is an underdeveloped area. This paper presents a pneumatically actuated soft robotic exoskeleton designed to provide coordinated assistive motion to the hand and wrist joints using PD-based feedback control. The results showed the potential of the exoskeleton to provide flexion/extension rehabilitation exercises and task-oriented rehabilitation practices. Additionally, the results have confirmed that the implemented PD control ensures that the exoskeleton reaches the targeted angular trajectories and velocities. Two modes, full and partial assistance, were successfully tested to verify the ability of the exoskeleton to accommodate varying levels of impairment.

Adaptive backstepping sliding mode subject-cooperative control for a pediatric lower-limb exoskeleton robot

The passive-assist rehabilitation mode with a fixed desired trajectory neglects the subject’s active involvement and degrades the therapeutic performance in case of partial muscle strength. Therefore, this study proposes a novel subject-cooperative control based on a variable admittance control scheme and a robust trajectory control scheme for a pediatric lower-limb exoskeleton robot. Initially, the system description and dynamic modeling are briefly explained. Thereafter, a neural-fuzzy–based variable admittance control ( nf VAC) is designed to incorporate a realistic subject-exoskeleton interaction and consider the subject’s active participation. Finally, a robust adaptive backstepping sliding mode control with rapid reaching law is used to handle parametric uncertainties and external disturbances. A stepwise selection of Lyapunov functions is utilized to address the stability of the trajectory control. The effectiveness of the proposed adaptive backstepping sliding mode–neural-fuzzy variable admittance control (ABSM- nf VAC) scheme is compared with two contrast control schemes, namely, adaptive backstepping-fixed admittance control (AB-FAC) and adaptive terminal sliding mode-fuzzy variable admittance control (ATSM- f VAC) for the active-assist mode with the effect of sudden reflex. Based on the numerical results, the suggested cooperative controller has demonstrated favorable tracking performance, compliant interaction, and safety aspects during gait training.