Systematic Review of Exoskeletons towards a General Categorization Model Proposal

Three-dimensional printed exoskeletons and orthoses for the upper limb-A systematic review

This systematic review aims to assess and summarize the current landscape in exoskeletons and orthotic solutions developed for upper limb medical assistance, which are partly or fully produced using 3-dimensional printing technologies and contain at least the elbow or the shoulder joints. The initial search was conducted on Web of Science, PubMed, and IEEEXplore, resulting in 92 papers, which were reduced to 72 after removal of duplicates. From the application of the inclusion and exclusion criteria and selection questionnaire, 33 papers were included in the review, being divided according to the analyzed joints. The analysis of the selected papers allowed for the identification of different solutions that vary in terms of their target application, actuation type, 3-dimensional printing techniques, and material selection, among others. The results show that there has been far more research on the elbow joint than on the shoulder joint, which can be explained by the relative complexity of the latter. Moreover, the findings of this study also indicate that there is still a gap between the research conducted on these devices and their practical use in real-world conditions. Based on current trends, it is anticipated that the future of 3-dimensional printed exoskeletons will revolve around the use of flexible and high-performance materials, coupled with actuated devices. These advances have the potential to replace the conventional fabrication methods of exoskeletons with technologies based on additive manufacturing.

Exoskeletons: A challenge for development

The development of exoskeletons is currently a lengthy process full of challenges. We are proposing a framework to accelerate the process and make the resulting exoskeletons more user-centered. The needed accomplishments in science are described in an effort to lay the foundation for future research projects. Since the early 2000s, exoskeletons have been discussed as an emerging technology in industrial, medical, or military applications. Those systems are designed to support people during manual tasks. At first, those systems lacked broad acceptance. Many models found their niches in ongoing developments and more diverse systems entering the market. There are still applications that are in dire need of such assistance. Due to the lack of experience with body-worn robotics, the development of such systems has been shaped by trial and error. The lack of legacy products results in longer development times. In this paper, a process to generate a framework is presented to display the required research to enable future exoskeleton designers. Owing to their proximity to the user's body, exoskeletons are highly complex systems that need sophisticated subsystems, such as kinematic, control, interaction design, or actuators, to be accepted by users. Due to the wide variety of fields and high user demands, a synchronized multidisciplinary effort is necessary. To achieve this, a process to develop a modular framework for exoskeleton design is proposed. It focuses on user- and use-case-centered solutions for matching kinematics, actuation, and control. To ensure the usefulness of the framework, an evaluation of the incorporated solutions is required.

Lower Limb Exoskeleton Sensors: State-of-the-Art

Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human-machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments.

Machine-learned Adaptive Switching in Voluntary Lower-limb Exoskeleton Control: Preliminary Results

Lower-limb exoskeletons utilize fixed control strategies and are not adaptable to user's intention. To this end, the goal of this study was to investigate the potential of using temporal-difference learning and general value functions for predicting the next possible walking mode that will be selected by users wearing exoskeletons in order to reduce the effort and cognitive load while switching between different modes of walking. Experiments were performed with a user wearing the Indego exoskeleton and given the authority to switch between five walking modes that were different in terms of speed and turn direction. The user's switching preferences were learned and predicted from device-centric and room-centric measurements by considering similarities in the movements being performed. A switching list was updated to show the most probable future next modes to be selected by the user. In contrast to other approaches that either can only predict a single time-step or require intensive offline training, this work used a computationally inexpensive method for learning and has the potential of providing temporally extended sets of predictions in real-time. Comparing the number of required manual switches between the machine-learned switching list and the best possible static lists showed an average decrease of 42.44% in the required switches for the machine-learned adaptive strategy. These promising results will facilitate the path for real-time application of this technique.

The Impact of COVID on Lower-Limb Exoskeleton Robotic System Patents—A Review

In recent decades, the field of physical rehabilitation, with the help of robotic systems that aid the population of any age with locomotor difficulties, has been evolving rapidly. Several robotic exoskeleton systems of the lower limbs have been proposed in the patent literature and some are even commercially available. Given the above, we are asking ourselves at the end of the COVID-19 pandemic: how much has this pandemic affected both the publication of patents and the application of new ones? How has new patents’ publication volume or application in robotic exoskeleton systems changed? We hypothesize that this pandemic has caused a reduction in the volume of new applications and possibly publications. We compare pandemic analysis and the last decade’s analysis to answer these questions. In this study, we used a set of statistical tests to see if there were any statistically significant changes. Our results show that the pandemic had at least one effect on applying for new patents based on the information analyzed from the three databases examined.

Recognizing Continuous Multiple Degrees of Freedom Foot Movements with Inertial Sensors

Recognition of continuous foot motions is important in robot-assisted lower limb rehabilitation, especially in prosthesis and exoskeleton design. For instance, perceiving foot motion is essential feedback for the robot controller. However, few studies have focused on perceiving multiple-degree of freedom (DOF) foot movements. This paper proposes a novel human-machine interaction (HMI) recognition wearable system for continuous multiple-DOF ankle-foot movements. The proposed system uses solely kinematic signals from inertial measurement units and multiclass support vector machines by creating error-correcting output codes. We conducted a study with multiple participants to validate the performance of the system using two strategies, a general model and a subject-specific model. The experimental results demonstrated satisfactory performance. The subject-specific approach achieved 98.45% ± 1.17% (mean ± SD) overall accuracy within a prediction time of 10.9 ms ± 1.7 ms, and the general approach achieved 85.3% ± 7.89% overall accuracy within a prediction time of 14.1 ms ± 4.5 ms. The results prove that the proposed system can more effectively recognize multiple continuous DOF foot movements than existing strategies. It can be applied to ankle-foot rehabilitation and fills the HMI high-level control demand for multiple-DOF wearable lower-limb robotics.

What Happens When Robots Punish? Evaluating Human Task Performance During Robot-Initiated Punishment

This article examines how people respond to robot-administered verbal and physical punishments. Human participants were tasked with sorting colored chips under time pressure and were punished by a robot when they made mistakes, such as inaccurate sorting or sorting too slowly. Participants were either punished verbally by being told to stop sorting for a fixed time, or physically, by restraining their ability to sort with an in-house crafted robotic exoskeleton. Either a human experimenter or the robot exoskeleton administered punishments, with participant task performance and subjective perceptions of their interaction with the robot recorded. The results indicate that participants made more mistakes on the task when under the threat of robot-administered punishment. Participants also tended to comply with robot-administered punishments at a lesser rate than human-administered punishments, which suggests that humans may not afford a robot the social authority to administer punishments. This study also contributes to our understanding of compliance with a robot and whether people accept a robot’s authority to punish. The results may influence the design of robots placed in authoritative roles and promote discussion of the ethical ramifications of robot-administered punishment.

Integration and Testing of a High-Torque Servo-Driven Joint and Its Electronic Controller with Application in a Prototype Upper Limb Exoskeleton

Mechatronic systems that allow motorized activation in robotic exoskeletons have evolved according to their specific applications and the characteristics of the actuation system, including parameters such as size, mechanical properties, efficiency, and power draw. Additionally, different control strategies and methods could be implemented in various electronic devices to improve the performance and usability of these devices, which is desirable in any application. This paper proposes the integration and testing of a high-torque, servo-driven joint and its electronic controller, exposing its use in a robotic exoskeleton prototype as a case study. Following a brief background review, the development and implementation of the proposal are presented, allowing the control of the servo-driven joint in terms of torque, rotational velocity, and position through a straightforward, closed-loop control architecture. Additionally, the stability and performance of the servo-driven joint were assessed with and without load. In conclusion and based on the obtained results, the servo-driven joint and its control system demonstrate consistent performance under the proposed test protocol (max values: angular velocity 97 °/s, torque 33 Nm, positioning RMSE 1.46°), enabling this approach for use in various applications related to robotic exoskeletons, including human performance enhancement, rehabilitation, or support for daily living activities.

Operation Safety of a 2-DoF Planar Mechanism for Arm Rehabilitation

The operation safety of rehabilitation devices must be addressed early in the development process and before being tested on people. In this paper, the operation safety of a 2-DoF (degrees of freedom) planar mechanism for arm rehabilitation is addressed. Then, the safety and efficiency of the device operation is assessed through the Transmission Index (TI) distribution in its workspace. Furthermore, the produced stresses on the human arm are assessed via the FEM (finite element method) when the rehabilitation device reaches five critical positions within its workspace. The TI distribution showed that the proposed design has a proper behaviour from a force transmission point of view, avoiding any singular configuration that might cause a control failure and subsequent risk for the user and supporting the user’s motion with a good efficiency throughout its operational workspace. The FEM analysis showed that Nurse operation is safe for the human arm since a negligible maximum stress of 6.55 × 103 N/m2 is achieved by the human arm when the device is located on the evaluated critical positions.

Design, Development, and Testing of an Intelligent Wearable Robotic Exoskeleton Prototype for Upper Limb Rehabilitation

Neuromotor rehabilitation and recovery of upper limb functions are essential to improve the life quality of patients who have suffered injuries or have pathological sequels, where it is desirable to enhance the development of activities of daily living (ADLs). Modern approaches such as robotic-assisted rehabilitation provide decisive factors for effective motor recovery, such as objective assessment of the progress of the patient and the potential for the implementation of personalized training plans. This paper focuses on the design, development, and preliminary testing of a wearable robotic exoskeleton prototype with autonomous Artificial Intelligence-based control, processing, and safety algorithms that are fully embedded in the device. The proposed exoskeleton is a 1-DoF system that allows flexion-extension at the elbow joint, where the chosen materials render it compact. Different operation modes are supported by a hierarchical control strategy, allowing operation in autonomous mode, remote control mode, or in a leader-follower mode. Laboratory tests validate the proper operation of the integrated technologies, highlighting a low latency and reasonable accuracy. The experimental result shows that the device can be suitable for use in providing support for diagnostic and rehabilitation processes of neuromotor functions, although optimizations and rigorous clinical validation are required beforehand.

Control System Design of an Underactuated Dynamic Body Weight Support System Using Its Stability

This paper discusses the stability of systems controlling patient body weight support systems which are used in gait re-education. These devices belong to the class of underactuated mechanical systems. This is due to the application of elastic shock-absorbing connections between the active part of the system and the passive part which impacts the patient. The model takes into account properties of the system, such as inertia, attenuation and susceptibility to the elements. Stability is an essential property of the system due to human-device interaction. In order to demonstrate stability, Lyapunov's theory of stability, which is based on the model of system dynamics, was applied. The stability of the control system based on a model that requires knowledge of the structure and parameters of the equations of motion was demonstrated. Due to inaccuracies in the modeling of the rope (one of the basic elements of the device), an adaptive control system was introduced and its stability was also proved. The authors conducted simulation and experimental tests that illustrate the functionality of the analyzed control systems.

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.