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The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field.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.

Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.

The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.

The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.

Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.

The purpose of this paper is to identify and assess the key performance indicators (KPIs) for the maintenance of public hospital buildings in the Gaza Strip.

Four KPIs were identified and evaluated in this paper: building performance indicators (BPI), maintenance efficiency indicators (MEI), annual maintenance expenditure (AME) and urgent repair request indicator. Twenty-one buildings in 13 public hospitals in Gaza Strip Governorate were taken as the sample of this study.

The results indicated that the European Gaza hospital has the highest BPI score (81.66) and the Dorra hospital has the lowest BPI score (68.26). The findings revealed that the average AME for all hospitals was $13.8/m2 which is considered to be below the standard level of expenditure. The MEI for Gaza public hospital buildings was found to be equal to 0.3 which indicated low level of maintenance expenditure.

Unavailability of certain data, lack of maintenance documentation and comparison difficulty between the Gaza Strip and Israel due to political, cultural and financial situation were some of the limitations of this study.

The Ministry of Health (MoH) can utilize the results of this study and consider it as benchmarking for maintenance management in public hospital buildings. This can improve the current maintenance situation which ultimately will improve the health-care situation in Palestine. The Palestinian MoH should look for external funding to increase the AME, as well as aim at increasing the MEI.

The health-care situation in Palestine will be improved.

This study is considered the first study to identify and assess the KPIs in the Gaza Strip. KPIs will assist the MoH to compare the actual and estimated performance in terms of effectiveness, efficiency and quality of workmanship.

This study aims to examine the relationship of work–family demands with employees’ well-being, and the role of management/supervisory support in this relationship. The following hypotheses were proposed: work–family demands would be negatively related to employees’ well-being; management/supervisory support would moderate the relationship of work–family demands with employees’ well-being.

The researchers used 250 working female academicians as respondents, working in the research universities in Kuala Lumpur, Malaysia. Their ages ranged from 30 to 60 years.

The findings of the present study proved that the work–family demands were negatively associated with employees’ well-being. Results also revealed that management and supervisory support strengthens the relationship between work–family demands and employees’ well-being. Thus, management and supervisory support plays an important role in balancing work demands and family roles and also in increasing working female academicians’ well-being.

In this study, management and supervisory support was found to be directly related to well-being, including life satisfaction, job satisfaction and family satisfaction. However, the direct relationship between management/supervisory support and well-being was positive and significant. This study also found that management/supervisor support reduced work–family conflict and work–family demands. Also, supervisory and management support was found to have a significant and positive relationship with well-being. Given these findings, supervisory and management support plays a very important role as a moderator of work–family demands and in developing and improving well-being in working women.