Search results

Upper-limb joint kinematics are highly complex and the kinematics of rehabilitation exoskeletons fail to reproduce them, resulting in hyperstaticity and human–machine incompatibility. The purpose of this paper is to design and develop a compatible exoskeleton robot (Co-Exos II) to address these problems.

The configuration synthesis of Co-Exos II is completed using advanced mechanism theory. A compatible configuration is selected and four passive joints are introduced into the connecting interfaces based on optimal configuration principles. A Co-Exos II prototype with nine degrees of freedom (DOFs) is developed and still owns a compact structure and volume. A new approach is presented to compensate the vertical glenohumeral (GH) movements. Co-Exos II and the upper arm are simplified as a guide-bar mechanism at the elevating plane. The theoretical displacements of passive joints are calculated by the kinematic model of the shoulder loop. The compatible experiments are completed to measure the kinematics of passive joints.

The compatible configuration of the passive joints can effectively reduce the gravity influences of the exoskeleton device and the upper extremities. The passive joints exhibit excellent compensation effect for the GH joint movements by comparing the theoretical and measured results. Passive joints can compensate for most GH movements, especially vertical movements.

Co-Exos II possesses good human–machine compatibility and wearable comfort for the affected upper limbs. The proposed compensation method is convenient to therapists and stroke patients during the rehabilitation trainings.

This study aims to leverage inertial sensors via a walk test to associate kinematic variables with functional assessment results among walkable subjects with chronic stroke.

Adults with first-ever stroke survivors were recruited for this study. First, functional assessments were obtained by using Fugl–Meyer Assessment for lower extremity and Berg balance scales. A self-assembled inertial measurement system obtained walking variables from a walk test after being deployed on subjects’ affected limbs and lower back. The average walking speeds, average range of motion in the affected limbs and a new gait symmetry index were computed and correlated with the two functional assessment scales using Spearman’s rank correlation test.

The average walking speeds were moderately correlated with both Fugl–Meyer assessment scales (γ = 0.62, p < 0.01, n = 23) and Berg balance scales (γ = 0.68, p < 0.01, n = 23). After being modified by the subjects’ height, the new gait symmetry index revealed moderate negative correlations with the Fugl–Meyer assessment scales (γ = −0.51, p < 0.05) and Berg balance scales (γ = −0.52, p < 0.05). The other kinematics failed to correlate well with the functional scales.

Neuromotor and functional assessment results from inertial sensors can facilitate their application in telemonitoring and telerehabilitation.

The average walking speeds and modified gait symmetry index are valuable parameters for inertial sensors in clinical research to deduce neuromotor and functional assessment results. In addition, the lower back is the optimal location for the inertial sensors.

Most current lower extremity exoskeletons emphasize assistance for walking rather than stability. The purpose of this paper is to propose a rehabilitation gait based on the transfer of gravity center to improve the balance of exoskeleton rehabilitation training of the hemiplegic patients in the frontal plane, reducing the dependence on crutches/walking frames.

The real-time and predictable instability factors of human and exoskeleton system (HES) are analyzed. Inspired by the walking balance strategy of the blind, a rehabilitation gait based on the transfer of gravity center is proposed and studied by modeling and experimental test and is finally applied to the prototype – Zhejiang University lower extremity exoskeleton (ZJULEEX) – to verify its feasibility.

At least three real-time and predictable factors cause the instability of HES, and the factor of lateral tilt caused by gravity should be focused in the balance control of frontal plane. With the proposed gait, the hip height of stepping leg of HES does not reduce obviously even when the crutches do not work, which can improve the balance of HES.

However, the rehabilitation gait control needs to be more complete and intelligent to response to other types of perturbations to further improve the balance of HES. In addition, more clinical trials should be conducted to evaluate the effect of the proposed gait.

May bring happiness to the rehabilitation of patients with hemiplegia.

The rehabilitation gait based on the transfer of gravity center to improve the balance of HES is first proposed and applied to HES.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

The purpose of this study is to confirm that the body weight load reduction system which is developed by us is effective to reduce the knee joint force of the walking user. This system is driven by pneumatic artificial muscle, functions as a mobile walking assist system.

The developed body weight load reduction system driven by rubber-less artificial muscle (RLAM) was tested experimentally. Simple force feedback control is applied to the RLAM. The system moves as synchronized with vertical movement of the walking user. The knee joint force during walking experiments conducted using this system is estimated by measurement of floor reaction force and position data of lower limb joints.

The knee joint force during walking is reduced when using this system. This system contributes to smooth change of knee joint force when the lower limb contacts the floor.

This lightweight body weight load reduction system is particularly effective for realizing easy-to-use mobile walking assist system.

A lightweight body weight load reduction system using pneumatic artificial muscle is a novel proposal. Additionally, these new evaluation results demonstrate its effectiveness for reducing knee joint force during walking.

Many studies have shown that rehabilitation robots are crucial for lower limb dysfunction, but application of many robotics have yet to be seen to actual use in China. This study aimed to improve a lower limb rehabilitation robot by details improving and practical design.

Structures and control system of a lower limb rehabilitation robot are improved in detail, including joint calculations, comfort analysis and feedback logic creation, and prototype experiments on healthy individuals and patients are conducted in a hospital.

All participating subjects did not experience any problems. The experiment shows detail improving is reasonable, and feasibility of the robot was confirmed, which has potential for overcoming difficulties and problems in practical application.

Therapeutic effects need to be evaluated in the future. Also, more details should be improved continuously based on the actual demand.

The improved robot could assist the lower limb during standing or walking, which has significance for practical application and patients in China.

Medical devices are undergoing rapid changes because of the increasing affordability of advanced technologies like additive manufacturing (AM) and three-dimensional scanning. New avenues are available for providing solutions and comfort that were not previously conceivable. The purpose of this paper is to provide a comprehensive review of the research on developing prostheses using AM to understand the opportunities and challenges in the domain. Various studies on prosthesis development using AM are investigated to explore the scope of integration of AM in prostheses development.

A review of key publications from the past two decades was conducted. Integration of AM and prostheses development is reviewed from the technologies, materials and functionality point of view to identify challenges, opportunities and future scope.

AM in prostheses provides superior physical and cognitive ergonomics and reduced cost and delivery time. Patient-specific, lightweight solutions for complex designs improve comfort, functionality and clinical outcomes. Compared to existing procedures and methodologies, using AM technologies in prosthetics could benefit a large population.

This paper helps investigate the impact of AM and related technology in the field of prosthetics and can also be viewed as a collection of relevant medical research and findings.

The purpose of this paper is to evaluate the physiotherapeutic benefits of bilateral symmetric training (BST) for stroke survivors affected by hemiparesis.

Other studies have investigated symmetric physiotherapy. A key difficulty in previous work is in maintaining mirror-imaged trajectories between the affected and less-affected limbs. This obstacle was overcome in this work by using a two-armed robotic exoskeleton to enforce symmetry. In total, 15 subjects, > 6 months post stroke were, randomly assigned to bilateral symmetric robotic training, unilateral robotic training, and standard physical therapy.

After 12 training sessions (90 minutes/session), the bilateral training group had the greatest intensity of movement training. They also had the greatest improvement in range of motion at the shoulder. The unilateral training group showed the greatest reduction in spasticity.

The rationale for symmetric physiotherapy is that it might promote connections from the undamaged brain hemisphere. The robot generated copious amounts of detailed kinematic data. Even though these data provided insights into the human to machine interface using different training modalities, it proved difficult to draw neurological conclusions. It is recommended that future research along these lines should include measures of neurophysiological change and/or changes in neurological activity.

This research suggests that the advantage of bilateral symmetric movement over other modalities is slight, and that robotic training has comparable results with standard care. If BST is used, care is potentially needed to avoid exacerbation of spasticity. Finally, this research includes a novel quantitative approach for evaluating robotic training.

This study is of value to therapeutic researchers interested in new physiotherapy techniques, roboticists interested in developing rehabilitation devices, or for rehabilitation game designers interested in using virtual reality.

Mobility is one of the important elements in clothing design. The purpose of this paper is to examine the predictability of clothing mobility via musculoskeletal simulation.

In order to carry out the musculoskeletal simulation considering the influence of clothing, simulation of the dressed state was attempted. This paper simulated the dressed state and measured the motion-related deformation of the clothing to estimate the force applied to the human body based on the material property of the clothing samples. The dressed state was simulated using an external force in the musculoskeletal model.

When the elbow flexion torque with an elbow supporter was calculated using the above-mentioned method of musculoskeletal simulation, it was confirmed that the lower the stretchability of the sample, the higher the elbow flexion torque. In addition, the sensory evaluation performed under the same condition as that in the simulation showed that the lower the joint torque during the motion, the higher the subjective mobility, and that the higher the joint torque, the lower the subjective mobility. Thus, it is suggested that musculoskeletal simulation of the dressed state can predict the clothing mobility.

However, the method proposed in this paper requires the measurement of the deformation of the clothing to estimate the force applied to the human body. Thus, it is difficult to apply this in the measurement of general clothing that allows enough space between it and the human body, requiring further improvement of the dressed state simulation method.

Because it is difficult to estimate the force applied by the clothing to the human body, only a few studies have performed analysis on the effect of clothing by using musculoskeletal simulation. Conversely, although the force applied by the clothing to the human body needs to be estimated in advance by the measurement of the deformation, the utility of the simulation in clothing design seems to be high because the simulation can estimate clothing mobility and the effects of clothing on muscle activity.

In sensor-based activity recognition, most of the previous studies focused on single activities such as body posture, ambulation and simple daily activities. Few works have been done to analyze complex concurrent activities. The purpose of this paper is to use a statistical modeling approach to classify them.

In this study, the recognition problem of concurrent activities is explored with the framework of parallel hidden Markov model (PHMM), where two basic HMMs are used to model the upper limb movements and lower limb states, respectively. Statistical time-domain and frequency-domain features are extracted, and then processed by the principal component analysis method for classification. To recognize specific concurrent activities, PHMM merges the information (by combining probabilities) from both channels to make the final decision.

Four studies are investigated to validate the effectiveness of the proposed method. The results show that PHMM can classify 12 daily concurrent activities with an average recognition rate of 93.2 per cent, which is superior to regular HMM and several single-frame classification approaches.

A statistical modeling approach based on PHMM is investigated, and it proved to be effective in concurrent activity recognition. This might provide more accurate feedback on people’s behaviors.

The research may be significant in the field of pervasive healthcare, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.