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In certain industrial operations, workers are required to stand for a prolonged duration. This leads to muscular fatigue in the legs, posing a threat to the productivity and well-being of the workers. This paper aims to address this problem of women in the clothing industry with an exoskeleton designed for lower extremities and improve productivity.

Ulrich’s product design approach has been followed with suitable modifications. The methodology involves a study to justify the need for this product and terminating at the physical and virtual evaluations of the product. Required anthropometric parameters are considered along the design process.

The exoskeleton discussed in this paper is an innovative product made of Aluminium 6061 alloy. During the simulation phase of the product, total von-mises stresses to a part bearing 1 leg were 31.5 MPa, 94.7 MPa and 284 MPa for aluminium, SS308 and springs, respectively. These values are below the yield limit by a great margin. Based on a user survey of this product, 72% of the targeted customers were interested in buying. Also, comparing electromyography (EMG) mean value of the voltage between workers’ leg with and without exoskeleton revealed that there was an improvement in the voltage by 2.5% when exoskeleton was used.

This paper emphasizes, for the first time – the necessity of an exoskeleton indigenized for the Indian population and the process of realizing it by designing an exoskeleton.

This paper aims to propose a novel system design and control algorithm of lower limb exoskeleton, which provides walking assistance and load sharing for the wearer.

In this paper, the valve-controlled asymmetrical hydraulic cylinder is selected for driving the hip and knee joint of exoskeleton. Pressure shoe is developed that purpose on detecting changes in plantar force, and a fuzzy recognition algorithm using plantar pressure is proposed. Dynamic model of the exoskeleton is established, and the sliding mode control is developed to implement the position tracking of exoskeleton. A series of prototype experiments including benchtop test, full assistance, partial assistance and loaded walking experiments are set up to verify the tracking performance and power-assisted effect of the proposed exoskeleton.

The control performance of PID control and sliding mode control are compared. The experimental data shows the tracking trajectories and tracking errors of sliding mode control and demonstrate its good robustness to nonlinearities. sEMG of the gastrocnemius muscle tends to be significantly weakened during assisted walking.

In this paper, a structure that the knee joint and hip joint driven by the valve-controlled asymmetrical cylinder is used to provide walking assistance for the wearer. The sliding mode control is proposed to deal with the nonlinearities during joint rotation and fluids. It shows great robustness and frequency adaptability through experiments under different motion frequencies and assistance modes. The design and control method of exoskeleton is a good attempt, which takes positive impacts on the productivity or quality of the life of wearers.

The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

This paper aims to provide an insight into recent developments in the robotic exoskeleton business by considering research, corporate activities, products and emerging applications.

Following a short introduction, this first provides examples of exoskeleton research involving artificial intelligence (AI). It then identifies recent market entrants and their products and discusses emerging industrial applications. Finally, conclusions are drawn.

The exoskeleton business is in a highly dynamic state. A research effort involving AI techniques seeks to impart exoskeletons with greatly enhanced capabilities, particularly in clinical applications. Many new companies have been established during the past decade, and several are exploiting academic research. The majority are targeting applications in the clinical market. The industrial sector is viewed as a key growth area, but applications remain limited, although some exist for robotic gloves, upper-body, waist and lower-body devices in the logistics, construction, automotive and other industries. Industrial applications for full-body exoskeleton are yet to progress beyond the trial stage.

This provides details of recent academic and corporate developments and emerging industrial applications in the robotic exoskeleton business.

This paper aims to deal with the design of new hybrid approach for the assistance of the flexion extension movement of the knee joint.

The control approach combines the use of a knee joint orthosis along with functional electrical stimulation (FES) within an assist-as-needed paradigm. An active impedance controller is used to assist the generation of muscular stimulation patterns during the extension sub-phase of the knee joint movement. The generated FES patterns are appropriately tailored to achieve flexion/extension movement of the knee joint, which allows providing the required assistance by the subject through muscular stimulation. The generated torque through stimulation is tracked by a non-linear disturbance observer and fed to the impedance controller to generate the desired trajectory that will be tracked using a standard proportional derivative controller.

The approach was tested in experiments with two healthy subjects. Results show satisfactory performances in terms of estimating the knee joint torque, as well as in terms of cooperation between the FES and the orthosis actuator during the execution of the knee joint flexion/extension movements.

The authors designed a new hybrid approach for the assistance of the flexion extension movement of the knee joint, which has not been studied yet. The control approach combines the use of a knee joint orthosis along with FES within an assist-as-needed paradigm.

This study discusses the tracking trajectory issue of the exoskeleton under the bounded disturbance and designs an useful tracking trajectory control method to solve it. By using the proposed control method, the tracking error can be successfully convergence to the assigned boundary. Meanwhile, the chattering effect caused by the actuators is already reduced, and the tracking performance of the pneumatic artificial muscles (PAMs) elbow exoskeleton is improved effectively.

A prescribed performance sliding mode control method was developed in this study to fulfill the joint position tracking trajectory task on the elbow exoskeleton driven by two PAMs. In terms of the control structure, a dynamic model was built by conforming to the adaptive law to compensate for the time variety and uncertainty exhibited by the system. Subsequently, a super-twisting algorithm-based second-order sliding mode control method was subjected to the exoskeleton under the boundedness of external disturbance. Moreover, the prescribed performance control method exhibits a smooth prescribed function with an error transformation function to ensure the tracking error can be finally convergent to the pre-designed requirement.

From the theoretical perspective, the stability of the control method was verified through Lyapunov synthesis. On that basis, the tracking performance of the proposed control method was confirmed through the simulation and the manikin model experiment.

As revealed by the results of this study, the proposed control method sufficiently applies to the PAMs elbow exoskeleton for tracking trajectory, which means it has potential application in the actual robot-assisted passive rehabilitation tasks.

This research was to understand people's perceptions and trends in wearable robots and the research questions were as follows: (1) investigating key terms related to wearable robots that were frequently used by and exposed to people and (2) analyzing relationships among those key terms.

Textom, a big data collection and analysis software system, was used to collect data using the keyword – wearable robot.

The frequency-inverse document frequency, term frequency and central analyses were investigated, and the major key terms related to wearable robots and their connectivity were identified. After performing network analysis and convergence of iterated correlations analyses using UCINET and NetDraw programs, the major key term categories were identified.

It is important to understand how people think and perceive about wearable robots before developing wearable robots. The results of the research are expected to be helpful to better understand how people perceive and what key terms are mainly discussed by people in both countries and ultimately help when developing wearable robots with better market targeting approach methods.

Walking-aid exoskeletons can assist and protect effectively the group with lower limb muscle strength decline, workers, first responders and military personnel. However, there is almost no united control strategy that can effectively assist daily walking. This paper aims to propose a hybrid oscillators’ (HOs) model to adapt to irregular gait (IG) patterns (frequent alternation between walking and standing or rapid changing of walking speed, etc.) and generate compliant and no-delay assistive torque.

The proposed algorithm, HOs, combines adaptive oscillators (AOs) with phase oscillator through switching assistive mode depending on whether or not the AOs' predicting error of hip joint degree is exceeded our expectation. HOs can compensate for delay by predicting gait phase when in AOs mode. Several treadmill and free walking experiments are designed to test the adaptability and effectiveness of HOs model under IG.

The experimental results show that the assistive strategy based on the HOs is effective under IG patterns, and delay is compensated totally under quasiperiodic gait conditions where a smoother human–robot interaction (HRI) force and the reduction of HRI force peak are observed. Delay compensation is found very effective at improving the performance of the assistive exoskeleton.

A novel algorithm is proposed to improve the adaptability of a walking assist hip exoskeleton in daily walking as well as generate compliant, no-delay assistive torque when converging.

This paper aims to provide details of the emerging families of robotic exoskeletons that are aimed at industrial applications.

Following an introduction, this paper considers the reasons for, and benefits of, using robotic exoskeletons in industrial applications. The paper then discusses a range of products and developments and their applications. Finally, brief concluding comments are drawn.

Following earlier military and medical developments, recent years have seen a huge upsurge in interest in industrial robotic exoskeletons. A new generation of products are under development in the USA, the Europe and the Far East by a growing number of companies, and some have entered production. The aim of developing industrial robotic exoskeletons is to assist workers in physically demanding tasks and, thus, reduce the incidence of industrial injuries and associated financial consequences. Several applications have been reported, most notably in the Far East, across a diverse range of industries.

New families of robotic exoskeletons are being developed, and these are poised to exert a major impact on many industries and constitute a significant market opportunity. This paper provides a timely insight into these developments.

Lower-limb exoskeleton systems enable people with spinal cord injury to regain some degree of locomotion ability, as the expected motion curve needs to adapt with changing scenarios, i.e. stair heights, distance to the stairs. The authors’ approach enables exoskeleton systems to adapt to different scenarios in stair ascent task safely.

In this paper, the authors learn the locomotion from predefined trajectories and walk upstairs by re-planning the trajectories according to external forces posed on exoskeleton systems. Moreover, instead of using complex sensors as inputs for re-planning in real-time, the approach can obtain forces acting on exoskeleton through dynamic model of human-exoskeleton system learned by an online machine learning approach without accurate parameters.

The proposed approach is validated in both simulation environment and a real walking assistance exoskeleton system. Experimental results prove that the proposed approach achieves better performance than the traditional predefined gait approach.

First, the approach obtain the external forces by a learned dynamic model of human-exoskeleton system, which reduces the cost of exoskeletons and avoids the heavy task of translating sensor input into actuator output. Second, the approach enables exoskeleton accomplish stair ascent task safely in different scenarios.