State of the Art and Future Directions for Lower Limb Robotic Exoskeletons

E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 3rd eNMD Congress: Pisa, Italy, 29-30 October 2021

Neuromuscular diseases (NMDs), in their phenotypic heterogeneity, share quite invariably common issues that involve several clinical and socio-economical aspects, needing a deep critical analysis to develop better management strategies. From diagnosis to treatment and follow-up, the development of technological solutions can improve the detection of several critical aspects related to the diseases, addressing both the met and unmet needs of clinicians and patients. Among several aspects of the digital transformation of health and care, this congress expands what has been learned from previous congresses editions on applicability and usefulness of technological solutions in NMDs. In particular the focus on new solutions for remote monitoring provide valuable insights to increase disease-specific knowledge and trigger prompt decision-making. In doing that, several perspectives from different areas of expertise were shared and discussed, pointing out strengths and weaknesses on the current state of the art on topic, suggesting new research lines to advance technology in this specific clinical field.

Towards meaningful community ambulation in individuals post stroke through use of a smart hip exoskeleton: A preliminary investigation

Stroke is the leading cause of long-term disability in the United States, leaving survivors with profound mobility challenges that impact independent community ambulation. Evidence shows assistance at the hip during walking may be beneficial for stroke survivors. In this cross-over design study, we examine the impact of a novel hip exoskeleton on both functional and patient reported outcomes measuring speed, fall risk, gait symmetry, energy expenditure and perceived walking ability during both indoors and outdoors in single and serial counting dual task paradigms. Nine ambulatory stroke survivors with hemiplegia were included. No differences were seen between the exoskeleton and baseline conditions for any outcomes. Only the patient reported outcome in which subjects were asked to rate their ability to walk outdoors approached statistical significance (p = 0.051) with greater improvement reported for the exoskeleton condition. When asked to rate several key factors about the exoskeleton, weight and assistance emerged as primary perceived negative factors of the exoskeleton underscoring the need for improvements to the technology in this area. Despite lack of differences across groups, some individuals responded positively to the exoskeleton for several functional outcomes measured, highlighting the need for additional exploration into the use of personalized hip exoskeletons for post-stroke rehabilitation.

Bilateral Elimination Rule-Based Finite Class Bayesian Inference System for Circular and Linear Walking Prediction

OBJECTIVE

The prediction of upcoming circular walking during linear walking is important for the usability and safety of the interaction between a lower limb assistive device and the wearer. This study aims to build a bilateral elimination rule-based finite class Bayesian inference system (BER-FC-BesIS) with the ability to predict the transition between circular walking and linear walking using inertial measurement units.

METHODS

Bilateral motion data of the human body were used to improve the recognition and prediction accuracy of BER-FC-BesIS.

RESULTS

The mean predicted time of BER-FC-BesIS in predicting the left and right lower limbs' upcoming steady walking activities is 119.32 ± 9.71 ms and 113.75 ± 11.83 ms, respectively. The mean time differences between the predicted time and the real time of BER-FC-BesIS in the left and right lower limbs' prediction are 14.22 ± 3.74 ms and 13.59 ± 4.92 ms, respectively. The prediction accuracy of BER-FC-BesIS is 93.98%.

CONCLUSION

Upcoming steady walking activities (e.g., linear walking and circular walking) can be accurately predicted by BER-FC-BesIS innovatively.

SIGNIFICANCE

This study could be helpful and instructional to improve the lower limb assistive devices' capabilities of walking activity prediction with emphasis on non-linear walking activities in daily living.

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.

Model-free finite-time robust control using fractional-order ultra-local model and prescribed performance sliding surface for upper-limb rehabilitation exoskeleton

To address the trajectory tracking issue of upper-limb rehabilitation exoskeleton with uncertainties and external disturbances, this paper proposes a fractional-order ultra-local model-based model-free finite-time robust controller (FO-FTRC) using predefined performance sliding surface. Different from previous model-free control strategies, a novel multi-input multi-output (MIMO) fractional-order ultra-local model which is a virtual model is proposed to approximate the complex uncertain nonlinear exoskeleton dynamics in a short sliding time window. This allows the design of controller to be independent of any exoskeleton model information and reduces the difficulty of controller design. The developed robust model-free control method incorporates a fractional-order quasi-time delay estimator (FO-QTDE), unknown disturbance estimator (UDE) as well as prescribed performance sliding mode control (PPSMC). The FO-QTDE is utilized to estimate the unknown lumped uncertainties which employs short time delayed knowledge only about the control input. However, the low-pass filter is always added for FO-QTDE when disturbances change fast, which leads to unavoidable estimation error. Then, UDE is designed to further eliminate the estimation error of FO-QTDE to enhance control performance. The PPSMC is constructed to converge sliding surface to zero in a finite time. Besides, the sliding surface is always limited in performance boundaries. After that, the overall system stability and convergence analyses are demonstrated by using the Lyapunov theorem. Finally, with the comparison to other methods of α-variable adaptive model free control (α-AMFC), time-delay estimation-based continuous nonsingular fast terminal sliding mode controller (TDE-CNFTSMC), time delay estimation (TDE)-based model-free fractional-order nonsingular fast terminal sliding mode control (MFF-TSM) and fractional-order proportion-differential (PDβ), the co-simulation results on 7-degree-of-freedom (DOF) iReHave upper-limb exoskeleton virtual prototype and experiment results on 2-DOF upper-limb exoskeleton are obtained to illustrate the effectiveness and superiority of the proposed FO-FTRC method.

Use of a Robotic Walking Device for Home and Community Mobility in Parkinson Disease: A Randomized Controlled Trial

BACKGROUND/PURPOSE

Gait impairments in Parkinson disease (PD) contribute to decreased quality of life. This randomized controlled trial examined immediate- and longer-term effects of a single joint robotic exoskeleton device (EXOD), the Honda Walking Assist device, on gait.

METHODS

Participants (n = 45) with PD (Hoehn and Yahr stages 1-3) were randomized to a robotic-assisted gait training (RAGT) group (n = 23) or control (CON) group (n = 22). The RAGT group was tested with and without the EXOD at baseline and then received supervised in-home and community training with the EXOD twice weekly for 8 weeks. The CON group received no interventions. Outcome measures included gait speed (primary), gait endurance (6-minute walk test), perceived ease of walking, and questionnaires and logs assessing performance of daily activities, freezing of gait, and daily activity levels.

RESULTS

Forty participants completed the study. No significant immediate impact of EXOD usage on participants' gait measures was found. Differences in gait speed and secondary outcome measures postintervention were not significantly different between the RAGT and CON groups. Participants with greater disease severity (worse baseline motor scores) had greater improvements in stride length during unassisted walking after the intervention than those with lower severity (mean difference: 3.22, 95% confidence interval: 0.05-6.40; P = 0.04).

DISCUSSION AND CONCLUSIONS

All RAGT participants could use the EXOD safely. The RAGT treatment used in this mostly low impairment population of people with PD may be ineffective and/or was insufficiently dosed to see a positive treatment effect. Our findings suggest that RAGT interventions in PD may be more effective in individuals with greater motor impairments.

Estimating human joint moments unifies exoskeleton control, reducing user effort

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

Evaluation of controllers for augmentative hip exoskeletons and their effects on metabolic cost of walking: explicit versus implicit synchronization

Background: Efficient gait assistance by augmentative exoskeletons depends on reliable control strategies. While numerous control methods and their effects on the metabolic cost of walking have been explored in the literature, the use of different exoskeletons and dissimilar protocols limit direct comparisons. In this article, we present and compare two controllers for hip exoskeletons with different synchronization paradigms. Methods: The implicit-synchronization-based approach, termed the Simple Reflex Controller (SRC), determines the assistance as a function of the relative loading of the feet, resulting in an emerging torque profile continuously assisting extension during stance and flexion during swing. On the other hand, the Hip-Phase-based Torque profile controller (HPT) uses explicit synchronization and estimates the gait cycle percentage based on the hip angle, applying a predefined torque profile consisting of two shorter bursts of assistance during stance and swing. We tested the controllers with 23 naïve healthy participants walking on a treadmill at 4 km ⋅ h-1, without any substantial familiarization. Results: Both controllers significantly reduced the metabolic rate compared to walking with the exoskeleton in passive mode, by 18.0% (SRC, p < 0.001) and 11.6% (HPT, p < 0.001). However, only the SRC led to a significant reduction compared to walking without the exoskeleton (8.8%, p = 0.004). The SRC also provided more mechanical power and led to bigger changes in the hip joint kinematics and walking cadence. Our analysis of mechanical powers based on a whole-body analysis suggested a reduce in ankle push-off under this controller. There was a strong correlation (Pearson's r = 0.778, p < 0.001) between the metabolic savings achieved by each participant with the two controllers. Conclusion: The extended assistance duration provided by the implicitly synchronized SRC enabled greater metabolic reductions compared to the more targeted assistance of the explicitly synchronized HPT. Despite the different assistance profiles and metabolic outcomes, the correlation between the metabolic reductions with the two controllers suggests a difference in individual responsiveness to assistance, prompting more investigations to explore the person-specific factors affecting assistance receptivity.

A Wearable Upper Limb Exoskeleton System and Intelligent Control Strategy

Heavy lifting operations frequently lead to upper limb muscle fatigue and injury. In order to reduce muscle fatigue, auxiliary force for upper limbs can be provided. This paper presents the development and evaluation of a wearable upper limb exoskeleton (ULE) robot system. A flexible cable transmits auxiliary torque and is connected to the upper limb by bypassing the shoulder. Based on the K-nearest neighbors (KNN) algorithm and integrated fuzzy PID control strategy, the ULE identifies the handling posture and provides accurate active auxiliary force automatically. Overall, it has the quality of being light and easy to wear. In unassisted mode, the wearer's upper limbs minimally affect the range of movement. The KNN algorithm uses multi-dimensional motion information collected by the sensor, and the test accuracy is 94.59%. Brachioradialis muscle (BM), triceps brachii (TB), and biceps brachii (BB) electromyogram (EMG) signals were evaluated by 5 kg, 10 kg, and 15 kg weight conditions for five subjects, respectively, during lifting, holding, and squatting. Compared with the ULE without assistance and with assistance, the average peak values of EMG signals of BM, TB, and BB were reduced by 19-30% during the whole handling process, which verified that the developed ULE could provide practical assistance under different load conditions.

StairNet: visual recognition of stairs for human-robot locomotion

Human-robot walking with prosthetic legs and exoskeletons, especially over complex terrains, such as stairs, remains a significant challenge. Egocentric vision has the unique potential to detect the walking environment prior to physical interactions, which can improve transitions to and from stairs. This motivated us to develop the StairNet initiative to support the development of new deep learning models for visual perception of real-world stair environments. In this study, we present a comprehensive overview of the StairNet initiative and key research to date. First, we summarize the development of our large-scale data set with over 515,000 manually labeled images. We then provide a summary and detailed comparison of the performances achieved with different algorithms (i.e., 2D and 3D CNN, hybrid CNN and LSTM, and ViT networks), training methods (i.e., supervised learning with and without temporal data, and semi-supervised learning with unlabeled images), and deployment methods (i.e., mobile and embedded computing), using the StairNet data set. Finally, we discuss the challenges and future directions. To date, our StairNet models have consistently achieved high classification accuracy (i.e., up to 98.8%) with different designs, offering trade-offs between model accuracy and size. When deployed on mobile devices with GPU and NPU accelerators, our deep learning models achieved inference speeds up to 2.8 ms. In comparison, when deployed on our custom-designed CPU-powered smart glasses, our models yielded slower inference speeds of 1.5 s, presenting a trade-off between human-centered design and performance. Overall, the results of numerous experiments presented herein provide consistent evidence that StairNet can be an effective platform to develop and study new deep learning models for visual perception of human-robot walking environments, with an emphasis on stair recognition. This research aims to support the development of next-generation vision-based control systems for robotic prosthetic legs, exoskeletons, and other mobility assistive technologies.

Low-force human-human hand interactions induce gait changes through sensorimotor engagement instead of direct mechanical effects

Physical human-robot interactions (pHRI) often provide mechanical force and power to aid walking without requiring voluntary effort from the human. Alternatively, principles of physical human-human interactions (pHHI) can inspire pHRI that aids walking by engaging human sensorimotor processes. We hypothesize that low-force pHHI can intuitively induce a person to alter their walking through haptic communication. In our experiment, an expert partner dancer influenced novice participants to alter step frequency solely through hand interactions. Without prior instruction, training, or knowledge of the expert's goal, novices decreased step frequency 29% and increased step frequency 18% based on low forces (< 20 N) at the hand. Power transfer at the hands was 3-700 × smaller than what is necessary to propel locomotion, suggesting that hand interactions did not mechanically constrain the novice's gait. Instead, the sign/direction of hand forces and power may communicate information about how to alter walking. Finally, the expert modulated her arm effective dynamics to match that of each novice, suggesting a bidirectional haptic communication strategy for pHRI that adapts to the human. Our results provide a framework for developing pHRI at the hand that may be applicable to assistive technology and physical rehabilitation, human-robot manufacturing, physical education, and recreation.

Actuators and transmission mechanisms in rehabilitation lower limb exoskeletons: a review

Research has shown that rehabilitation lower limb exoskeletons (RLLEs) are effective tools for improving recovery or regaining lower limb function. This device interacts with the limbs of patients. Thus, actuators and power transmission mechanisms are the key factors in determining smooth human‒machine interaction and comfort in physical therapy activities. A multitude of distinct technologies have been proposed. However, we questioned which consideration point in actuator selection and power transmission mechanisms are used for RLLE. A review of the technical characteristics and status of advanced RLLE designs is discussed. We review actuator selection for RLLE devices. Furthermore, the power transmission mechanisms over the years within each of the RLLE devices are presented. The development issues and possible research directions related to actuators and power transmission mechanisms are provided. Most RLLEs are still in the research phase, and only a few have been commercialized. The aim of this paper is to provide researchers with useful information for investigating technological progress and highlight the latest technological choices in RLLE development.

An Approach to Using Electrical Impedance Myography Signal Sensors to Assess Morphofunctional Changes in Tissue during Muscle Contraction

This present work is aimed at conducting fundamental and exploratory studies of the mechanisms of electrical impedance signal formation. This paper also considers morphofunctional changes in forearm tissues during the performance of basic hand actions. For this purpose, the existing research benches were modernized to conduct experiments of mapping forearm muscle activity by electrode systems on the basis of complexing the electrical impedance signals and electromyography signals and recording electrode systems' pressing force using force transducers. Studies were carried out with the involvement of healthy volunteers in the implementation of vertical movement of the electrode system and ultrasound transducer when the subject's upper limb was positioned in the bed of the stand while performing basic hand actions in order to identify the relationship between the morphofunctional activity of the upper limb muscles and the recorded parameters of the electro-impedance myography signal. On the basis of the results of the studies, including complex measurements of neuromuscular activity on healthy volunteers such as the signals of electro-impedance myography and pressing force, analyses of the morphofunctional changes in tissues during action performance on the basis of ultrasound and MRI studies and the factors influencing the recorded signals of electro-impedance myography are described. The results are of fundamental importance and will enable reproducible electro-impedance myography signals, which, in turn, allow improved anthropomorphic control.

Skin preparation-free, stretchable microneedle adhesive patches for reliable electrophysiological sensing and exoskeleton robot control

High-fidelity and comfortable recording of electrophysiological (EP) signals with on-the-fly setup is essential for health care and human-machine interfaces (HMIs). Microneedle electrodes allow direct access to the epidermis and eliminate time-consuming skin preparation. However, existing microneedle electrodes lack elasticity and reliability required for robust skin interfacing, thereby making long-term, high-quality EP sensing challenging during body movement. Here, we introduce a stretchable microneedle adhesive patch (SNAP) providing excellent skin penetrability and a robust electromechanical skin interface for prolonged and reliable EP monitoring under varying skin conditions. Results demonstrate that the SNAP can substantially reduce skin contact impedance under skin contamination and enhance wearing comfort during motion, outperforming gel and flexible microneedle electrodes. Our wireless SNAP demonstration for exoskeleton robot control shows its potential for highly reliable HMIs, even under time-dynamic skin conditions. We envision that the SNAP will open new opportunities for wearable EP sensing and its real-world applications in HMIs.

Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements

BACKGROUND AND OBJECTIVE

For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training.

METHODS

This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern.

RESULTS

Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent.

CONCLUSION

The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Objective and subjective evaluation of walking ability with and without the use of a passive brace for hip flexor muscles in individuals with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) affects the cognitive and motor domains. Muscle weakness often leads to abnormal gait. Several solutions are rising, including the use of passive exoskeletons.

OBJECTIVE

The purpose of this study is to evaluate the effect of a first-ever use of a passive exoskeleton on walking ability in people with MS.

METHODS

We recruited 50 persons with MS. All subjects were assessed using the 2-min walking test, the timed 25-foot walk test, and a two-stage rate of perceived exertion (RPE) without the exoskeleton (T0) and with the exoskeleton (T1).

RESULTS

The data showed a significant decrease in walking endurance while the exoskeleton is worn (2-min walking test: T0: 65.19 ± 23.37 m; T1: 59.40 ± 22.99; p < 0.0001) and a not significant difference in walking speed on a shortened distance (T0: 15.71 ± 10.30 s; T1: 15.73 ± 11.86 s; p = 0.25). No significant differences were also found for the effort perception scale (RPE: T0: 13.24 ± 3.01; T1: 13.60 ± 2.9; p = 0.3). Seventy-two percent of subjects reported a positive or neutral global perceived effect.

CONCLUSIONS

The exoskeleton does not add any fatiguing or negative effects. Although the walking performance decreases, the overall perception of the subjects is positive. Further studies are needed to evaluate the effect of the exoskeleton on gait quality.

Exo Supportive Devices: Summary of Technical Aspects

Human societies have been trying to mitigate the suffering of individuals with physical impairments, with a special effort in the last century. In the 1950s, a new concept arose, finding similarities between animal exoskeletons, and with the goal of medically aiding human movement (for rehabilitation applications). There have been several studies on using exosuits with this purpose in mind. So, the current review offers a critical perspective and a detailed analysis of the steps and key decisions involved in the conception of an exoskeleton. Choices such as design aspects, base materials (structure), actuators (force and motion), energy sources (actuation), and control systems will be discussed, pointing out their advantages and disadvantages. Moreover, examples of exosuits (full-body, upper-body, and lower-body devices) will be presented and described, including their use cases and outcomes. The future of exoskeletons as possible assisted movement solutions will be discussed-pointing to the best options for rehabilitation.

Gait variability of outdoor vs treadmill walking with bilateral robotic ankle exoskeletons under proportional myoelectric control

Lower limb robotic exoskeletons are often studied in the context of steady-state treadmill walking in laboratory environments. However, the end goal of these devices is often adoption into our everyday lives. To move outside of the laboratory, there is a need to study exoskeletons in real world, complex environments. One way to study the human-machine interaction is to look at how the exoskeleton affects the user's gait. In this study we assessed changes in gait spatiotemporal variability when using a robotic ankle exoskeleton under proportional myoelectric control both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would not lead to significant changes in variability inside on a treadmill or outside compared to not using the exoskeletons. In addition, we hypothesized that walking outside would lead to higher variability both with and without the exoskeletons compared to treadmill walking. In support of our hypothesis, we found significantly higher coefficients of variation of stride length, stance time, and swing time when walking outside both with and without the exoskeleton. We found a significantly higher variability when using the exoskeletons inside on the treadmill, but we did not see significantly higher variability when walking outside overground. The value of this study to the literature is that it emphasizes the importance of studying exoskeletons in the environment in which they are meant to be used. By looking at only indoor gait spatiotemporal measures, we may have assumed that the exoskeletons led to higher variability which may be unsafe for certain target populations. In the context of the literature, we show that variability due to robotic ankle exoskeletons under proportional myoelectric control does not elicit different changes in stride time variability than previously found in other daily living tasks (uneven terrain, load carriage, or cognitive tasks).

Users' experiences of intensive robotic-assisted gait training post-stroke - "a push forward or feeling pushed around?"

PURPOSE

Robotic-assisted gait training (RAGT) is suggested to improve walking ability after stroke. The purpose of this study was to describe experiences of robotic-assisted gait training as part of a gait training intervention among persons in the chronic phase after stroke.

MATERIALS AND METHODS

Semi-structured interviews were performed with 13 participants after a 6-week intervention including treadmill gait training with the Hybrid Assistive Limb® (HAL) exoskeleton. Data were analysed using qualitative content analysis.

RESULTS

Four categories emerged: (1) A rare opportunity for potential improvements describes the mindset before the start of the intervention; (2) Being pushed to the limit represents the experience of engaging in intensive gait training; (3) Walking with both resistance and constraints reveals barriers and facilitators during HAL training; (4) Reaching the end and taking the next step alone illustrates feelings of confidence or concern as the intervention ended.

CONCLUSIONS

The gait training intervention including RAGT was considered demanding but appreciated. Support and concrete, individual feedback was crucial for motivation, whilst the lack of variation was a barrier. Results encourage further development of exoskeletons that are comfortable to wear and stimulate active participation by enabling smoothly synchronised movements performed during task-specific activities in different environments. IMPLICATIONS FOR REHABILITATIONWhen provided in a suitable context, the mental and physical challenges of intensive robotic-assisted gait training can be both inspiring and motivating.Support and engagement along with informative feedback from therapists are suggested crucial for motivation.Intensive task-specific gait training may preferably be performed in an enriched environment and combined with other physiotherapy treatments to stimulate engagement.

User preference optimization for control of ankle exoskeletons using sample efficient active learning

One challenge to achieving widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are "tuned" to optimize a physiological or biomechanical objective. However, these approaches are resource intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is likely derived from many factors, including comfort, fatigue, and stability, among others. This work introduces an approach to conveniently tune the four parameters of an exoskeleton controller to maximize user preference. Our overarching strategy is to leverage the wearer to internally balance the experiential factors of wearing the system. We used an evolutionary algorithm to recommend potential parameters, which were ranked by a neural network that was pretrained with previously collected user preference data. The controller parameters that had the highest preference ranking were provided to the exoskeleton, and the wearer responded with real-time feedback as a forced-choice comparison. Our approach was able to converge on controller parameters preferred by the wearer with an accuracy of 88% on average when compared with randomly generated parameters. User-preferred settings stabilized in 43 ± 7 queries. This work demonstrates that user preference can be leveraged to tune a partial-assist ankle exoskeleton in real time using a simple, intuitive interface, highlighting the potential for translating lower-limb wearable technologies into our daily lives.

A Hybrid Anchoring Technology Composed of Reinforced Flexible Shells for a Knee Unloading Exosuit

Soft robotic wearables have emerged as an ergonomic alternative to rigid robotic wearables, commonly utilizing tension-based actuation systems. However, their soft structure's natural tendency to buckle limits their use for compression bearing applications. This study presents reinforced flexible shell (RFS) anchoring, a compliant, low-profile, ergonomic wearable platform capable of high compression resistance. RFS anchors are fabricated with soft and semirigid materials that typically buckle under compressive loads. Buckling is overcome using the wearer's leg as a support structure, reinforcing the shells with straps, and minimizing the space between the shells and the wearer's skin-enabling force transmission orders of magnitude larger. RFS anchoring performance was evaluated comparatively by examining the shift-deformation profiles of three identically designed braces fabricated with different materials: rigid, strapped RFS, and unstrapped RFS. The unstrapped RFS severely deformed before 200 N of force could be applied. The strapped RFS successfully supported 200 N of force and exhibited a nearly identical transient shift-deformation profile with the rigid brace condition. RFS anchoring technology was applied to a compression-resistant hybrid exosuit, Exo-Unloader, for knee osteoarthritis. Exo-Unloader utilizes a tendon-driven linear sliding actuation system that unloads the medial and lateral compartments of the knee. Exo-Unloader can deliver 200 N of unloading force without deforming, as indicted by its similar transient shift-deformation profile with a rigid unloader baseline. Although rigid braces effectively withstand and transmit high compressive loads, they lack compliance; RFS anchoring technology expands the application of soft and flexible materials to compression-based wearable assistive systems.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

Hip and Knee Joint Kinematics Predict Quadriceps Hyperreflexia in People with Post-stroke Stiff-Knee Gait

Wearable assistive technology for the lower extremities has shown great promise towards improving gait function in people with neuromuscular injuries. But common secondary impairments, such as hypersensitive stretch reflexes or hyperreflexia, have been often neglected. Incorporation of biomechanics into the control loop could improve individualization and avoid hyperreflexia. However, adding hyperreflexia prediction to the control loop would require expensive or complex measurement of muscle fiber characteristics. In this study, we explore a clinically accessible biomechanical predictor set that can accurately predict rectus femoris (RF) reaction after knee flexion assistance in pre-swing by a powered orthosis. We examined a total of 14 gait parameters based on gait kinematic, kinetic, and simulated muscle-tendon states from 8 post-stroke individuals with Stiff-Knee gait (SKG) wearing a knee exoskeleton robot. We independently performed both parametric and non-parametric variable selection approaches using machine learning regression techniques. Both models revealed the same four kinematic variables relevant to knee and hip joint motions were sufficient to effectively predict RF hyperreflexia. These results suggest that control of knee and hip kinematics may be a more practical method of incorporating quadriceps hyperreflexia into the exoskeleton control loop than the more complex acquisition of muscle fiber properties.

Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors

Lower limb robotic exoskeletons are often studied in the context of steady state treadmill walking in a laboratory environment. However, the end goal for exoskeletons is to be used in real world, complex environments. To reach the point that exoskeletons are openly adopted into our everyday lives, we need to understand how the human and robot interact outside of a laboratory. Metabolic cost is often viewed as a gold standard metric for measuring exoskeleton performance but is rarely used to evaluate performance at non steady state walking outside of a laboratory. In this study, we tested the effects of robotic ankle exoskeletons under proportional myoelectric control on the cost of transport of walking both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would lead to a lower cost of transport compared to walking without them both on a treadmill and outside. We saw no significant increases or decreases in cost of transport or exoskeleton mechanics when walking with the exoskeletons compared to walking without them both on a treadmill and outside. We saw a strong negative correlation between walking speed and cost of transport when walking with and without the exoskeletons. In the future, research should consider how performing more difficult tasks, such as incline and loaded walking, affects the cost of transport while walking with and without robotic ankle exoskeletons. The value of this study to the literature is that it emphasizes the importance of both hardware dynamics and controller design towards reducing metabolic cost of transport with robotic ankle exoskeletons. When comparing our results to other studies using the same hardware with different controllers or very similar controllers with different hardware, there are a wide range of outcomes as to metabolic benefit.

Patent Review of Lower Limb Rehabilitation Robotic Systems by Sensors and Actuation Systems Used

Robotic systems for lower limb rehabilitation are essential for improving patients' physical conditions in lower limb rehabilitation and assisting patients with various locomotor dysfunctions. These robotic systems mainly integrate sensors, actuation, and control systems and combine features from bionics, robotics, control, medicine, and other interdisciplinary fields. Several lower limb robotic systems have been proposed in the patent literature; some are commercially available. This review is an in-depth study of the patents related to robotic rehabilitation systems for lower limbs from the point of view of the sensors and actuation systems used. The patents awarded and published between 2013 and 2023 were investigated, and the temporal distribution of these patents is presented. Our results were obtained by examining the analyzed information from the three public patent databases. The patents were selected so that there were no duplicates after several filters were used in this review. For each patent database, the patents were analyzed according to the category of sensors and the number of sensors used. Additionally, for the main categories of sensors, an analysis was conducted depending on the type of sensors used. Afterwards, the actuation solutions for robotic rehabilitation systems for upper limbs described in the patents were analyzed, highlighting the main trends in their use. The results are presented with a schematic approach so that any user can easily find patents that use a specific type of sensor or a particular type of actuation system, and the sensors or actuation systems recommended to be used in some instances are highlighted.

High-density Surface and Intramuscular EMG Data from the Tibialis Anterior During Dynamic Contractions

Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established approach for mapping neural input to mechanical output, there is a lack of adaptability to dynamic environments due to a lack of data from dynamic movements. This report presents data consisting of simultaneously recorded high density surface EMG, intramuscular EMG, and joint dynamics from the tibialis anterior during static and dynamic muscle contractions. The dataset comes from seven subjects performing three to five trials each of different types of muscle contractions, both static (isometric) and dynamic (isotonic and isokinetic). Each subject was seated in an isokinetic dynamometer such that ankle movement was isolated and instrumented with four fine wire electrodes and a 126-electrode surface EMG grid. This data set can be used to (i) validate methods for extracting neural signals from surface EMG, (ii) develop models for predicting torque output, or (iii) develop classifiers for movement intent.

Effects of induced motor fatigue on walking mechanics and energetics

Lower-body robotic exoskeletons can be used to reduce the energy demand of locomotion and increase the endurance of wearers. Understanding how motor fatigue affects walking performance may lead to better exoskeleton designs to support the changing physical capacity of an individual due to motor fatigue. The purpose of this study was to investigate the effects of motor fatigue on walking mechanics and energetics. Treadmill walking with progressively increased incline gradient was used to induce motor fatigue. Twenty healthy young participants walked on an instrumented treadmill at 1.25 m/s and 0° of incline for 5 min before (PRE) and after (POST) motor fatigue. We examined lower-limb joint mechanics, metabolic cost, and the efficiency of positive mechanical work (η+work). Compared to PRE, participants had increased net metabolic power by ∼14% (p < 0.001) during POST. Participants also had increased total-limb positive mechanical power (Total P+mech) by ∼4% during POST (p < 0.001), resulting in a reduced η+work by ∼8% (p < 0.001). In addition, the positive mechanical work contribution of the lower-limb joints during POST was shifted from the ankle to the knee while the negative mechanical work contribution was shifted from the knee to the ankle (all p < 0.017). Although greater knee positive mechanical power was generated to compensate for the reduction in ankle positive power after motor fatigue, the disproportionate increase in metabolic cost resulted in a reduced walking efficiency. The findings of this study suggest that powering the ankle joint may help delay the onset of the lower-limb joint work redistribution observed during motor fatigue.

Neuromechanical Adaptation to Walking With Electromechanical Ankle Exoskeletons Under Proportional Myoelectric Control

OBJECTIVE

To determine if robotic ankle exoskeleton users decrease triceps surae muscle activity when using proportional myoelectric control, we studied healthy young participants walking with commercially available electromechanical ankle exoskeletons (Dephy Exoboot) with a novel controller. The vast majority of robotic lower limb exoskeletons do not have direct neural input from the user which makes adaptation of exoskeleton dynamics based on user intent difficult. Proportional myoelectric control has proven to allow considerable adaptation in muscle activation and gait kinematics in pneumatic, tethered ankle exoskeletons. In this study we quantified the changes in muscle activity and joint biomechanics of twelve participants walking for 30 minutes on a treadmill.

RESULTS

The exoskeletons provided 29% of the peak total ankle power and 18% of the peak total ankle moment by the end of the practice session. There was a decrease of 12% in soleus, 17% in lateral gastrocnemius and 5% in medial gastrocnemius electromyography (EMG) root mean square (root mean squared) after walking with the exoskeleton for 30 minutes compared to not wearing the exoskeleton, but this difference was not statistically significant. There were no differences in joint biomechanics of the ankle, hip, or knee between the end of training compared to walking without the exoskeletons.

CONCLUSIONS

Contrary to expectations, triceps surae muscle activity showed only small non-significant decreases in 30 minutes of walking with portable, electromechanical ankle exoskeletons under proportional myoelectric control. The commercially available ankle exoskeletons were likely too weak to produce a statistically meaningful decline in triceps surae recruitment. Future research should include a wider variety of tasks, including measurements of metabolic energy expenditure, and provide a longer period of adaptation to evaluate the ankle exoskeletons.

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

Walking in real-world environments involves constant decision-making, e.g., when approaching a staircase, an individual decides whether to engage (climbing the stairs) or avoid. For the control of assistive robots (e.g., robotic lower-limb prostheses), recognizing such motion intent is an important but challenging task, primarily due to the lack of available information. This paper presents a novel vision-based method to recognize an individual's motion intent when approaching a staircase before the potential transition of motion mode (walking to stair climbing) occurs. Leveraging the egocentric images from a head-mounted camera, the authors trained a YOLOv5 object detection model to detect staircases. Subsequently, an AdaBoost and gradient boost (GB) classifier was developed to recognize the individual's intention of engaging or avoiding the upcoming stairway. This novel method has been demonstrated to provide reliable (97.69%) recognition at least 2 steps before the potential mode transition, which is expected to provide ample time for the controller mode transition in an assistive robot in real-world use.

Evaluation of a machine-learning-driven active-passive upper-limb exoskeleton robot: Experimental human-in-the-loop study

Evaluating exoskeleton actuation methods and designing an effective controller for these exoskeletons are both challenging and time-consuming tasks. This is largely due to the complicated human-robot interactions, the selection of sensors and actuators, electrical/command connection issues, and communication delays. In this research, a test framework for evaluating a new active-passive shoulder exoskeleton was developed, and a surface electromyography (sEMG)-based human-robot cooperative control method was created to execute the wearer's movement intentions. The hierarchical control used sEMG-based intention estimation, mid-level strength regulation, and low-level actuator control. It was then applied to shoulder joint elevation experiments to verify the exoskeleton controller's effectiveness. The active-passive assistance was compared with fully passive and fully active exoskeleton control using the following criteria: (1) post-test survey, (2) load tolerance duration, and (3) computed human torque, power, and metabolic energy expenditure using sEMG signals and inverse dynamic simulation. The experimental outcomes showed that active-passive exoskeletons required less muscular activation torque (50%) from the user and reduced fatigue duration indicators by a factor of 3, compared to fully passive ones.

A mechatronic leg replica to benchmark human-exoskeleton physical interactions

Evaluating human-exoskeleton interaction typically requires experiments with human subjects, which raises safety issues and entails time-consuming testing procedures. This paper presents a mechatronic replica of a human leg, which was designed to quantify physical interaction dynamics between exoskeletons and human limbs without the need for human testing. In the first part of this work, we present the mechanical, electronic, sensory system and software solutions integrated in our leg replica prototype. In the second part, we used the leg replica to test its interaction with two types of commercially available wearable devices, i.e. an active full leg exoskeleton and a passive knee orthosis. We ran basic test examples to demonstrate the functioning and benchmarking potential of the leg replica to assess the effects of joint misalignments on force transmission. The integrated force sensors embedded in the leg replica detected higher interaction forces in the misaligned scenario in comparison to the aligned one, in both active and passive modalities. The small standard deviation of force measurements across cycles demonstrates the potential of the leg replica as a standard test method for reproducible studies of human-exoskeleton physical interaction.

Exoskeletal solutions to enable mobility with a lower leg fracture in austere environments

The treatment and evacuation of people with lower limb fractures in austere environments presents unique challenges that assistive exoskeletal devices could address. In these dangerous situations, independent mobility for the injured can preserve their vital capabilities so that they can safely evacuate and minimize the need for additional personnel to help. This expert view article discusses how different exoskeleton archetypes could provide independent mobility while satisfying the requisite needs for portability, maintainability, durability, and adaptability to be available and useful within austere environments. The authors also discuss areas of development that would enable exoskeletons to operate more effectively in these scenarios as well as preserve the health of the injured limb so that definitive treatment after evacuation will produce better outcomes.

Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness

BACKGROUND

In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.

METHODS

Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.

RESULTS

(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.

CONCLUSIONS

Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance

In the field of wearable robotics, assistance needs to be individualized for the user to maximize benefit. Information from muscle fascicles automatically recorded from brightness mode (B-mode) ultrasound has been used to design assistance profiles that are proportional to the estimated muscle force of young individuals. There is also a desire to develop similar strategies for older adults who may have age-altered physiology. This study introduces and validates a ResNet + 2x-LSTM model for extracting fascicle lengths in young and older adults. The labeling was generated in a semimanual manner for young (40,696 frames) and older adults (34,262 frames) depicting B-mode imaging of the medial gastrocnemius. First, the model was trained on young and tested on both young (R2 = 0.85, RMSE = 2.36 ± 1.51 mm, MAPE = 3.6%, aaDF = 0.48 ± 1.1 mm) and older adults (R2 = 0.53, RMSE = 4.7 ± 2.51 mm, MAPE = 5.19%, aaDF = 1.9 ± 1.39 mm). Then, the performances were trained across all ages (R2 = 0.79, RMSE = 3.95 ± 2.51 mm, MAPE = 4.5%, aaDF = 0.67 ± 1.8 mm). Although age-related muscle loss affects the error of the tracking methodology compared to the young population, the absolute percentage error for individual fascicles leads to a small variation of 3-5%, suggesting that the error may be acceptable in the generation of assistive force profiles.

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017-2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.

Modelling the interaction between wearable assistive devices and digital human models-A systematic review

Exoskeletons, orthoses, exosuits, assisting robots and such devices referred to as wearable assistive devices are devices designed to augment or protect the human body by applying and transmitting force. Due to the problems concerning cost- and time-consuming user tests, in addition to the possibility to test different configurations of a device, the avoidance of a prototype and many more advantages, digital human models become more and more popular for evaluating the effects of wearable assistive devices on humans. The key indicator for the efficiency of assistance is the interface between device and human, consisting mainly of the soft biological tissue. However, the soft biological tissue is mostly missing in digital human models due to their rigid body dynamics. Therefore, this systematic review aims to identify interaction modelling approaches between wearable assistive devices and digital human models and especially to study how the soft biological tissue is considered in the simulation. The review revealed four interaction modelling approaches, which differ in their accuracy to recreate the occurring interactions in reality. Furthermore, within these approaches there are some incorporating the appearing relative motion between device and human body due to the soft biological tissue in the simulation. The influence of the soft biological tissue on the force transmission due to energy absorption on the other side is not considered in any publication yet. Therefore, the development of an approach to integrate the viscoelastic behaviour of soft biological tissue in the digital human models could improve the design of the wearable assistive devices and thus increase its efficiency and efficacy.

Effects of Bilateral Assistance for Hemiparetic Gait Post-Stroke Using a Powered Hip Exoskeleton

Hemiparetic gait due to stroke is characterized by an asymmetric gait due to weakness in the paretic lower limb. These inter-limb asymmetries increase the biomechanical demand and reduce walking speed, leading to reduced community mobility and quality of life. With recent progress in the field of wearable technologies, powered exoskeletons have shown great promise as a potential solution for improving gait post-stroke. While previous studies have adopted different exoskeleton control methodologies for restoring gait post-stroke, the results are highly variable due to limited understanding of the biomechanical effect of exoskeletons on hemiparetic gait. In this study, we investigated the effect of different hip exoskeleton assistance strategies on gait function and gait biomechanics of individuals post-stroke. We found that, compared to walking without a device, powered assistance from hip exoskeletons improved stroke participants' self-selected overground walking speed by 17.6 ± 2.5% and 11.1 ± 2.7% with a bilateral and unilateral assistance strategy, respectively (p < 0.05). Furthermore, both bilateral and unilateral assistance strategies significantly increased the paretic and non-paretic step length (p < 0.05). Our findings suggest that powered assistance from hip exoskeletons is an effective means to increase walking speed post-stroke and tuning the balance of assistance between non-paretic and paretic limbs (i.e., a bilateral strategy) may be most effective to maximize performance gains.

Artificial intelligence in elderly healthcare: A scoping review

The ageing population has led to a surge in the adoption of artificial intelligence (AI) technologies in elderly healthcare worldwide. However, in the advancement of AI technologies, there is currently a lack of clarity about the types and roles of AI technologies in elderly healthcare. This scoping review aimed to provide a comprehensive overview of AI technologies in elderly healthcare by exploring the types of AI technologies employed, and identifying their roles in elderly healthcare based on existing studies. A total of 10 databases were searched for this review, from January 1 2000 to July 31 2022. Based on the inclusion criteria, 105 studies were included. The AI devices utilized in elderly healthcare were summarised as robots, exoskeleton devices, intelligent homes, AI-enabled health smart applications and wearables, voice-activated devices, and virtual reality. Five roles of AI technologies were identified: rehabilitation therapists, emotional supporters, social facilitators, supervisors, and cognitive promoters. Results showed that the impact of AI technologies on elderly healthcare is promising and that AI technologies are capable of satisfying the unmet care needs of older adults and demonstrating great potential in its further development in this area. More well-designed randomised controlled trials are needed in the future to validate the roles of AI technologies in elderly healthcare.

Engineering design strategies for force augmentation exoskeletons: A general review

In the industrial and military sector, work activities are required transporting or supporting heavy loads manually, affecting this the human spinal column due to the weight of the loads or the repetition of this labor. In this regard, the use of force-enhancing exoskeletons is a potential solution to this issue. Therefore, this article summarizes the state of the art in relevant contributions to structural design, control systems, actuators, and performance metrics to evaluate the proper functioning of exoskeletons used for load support and transfer. This is essential to address current and new open problems in these applications, and this includes reducing the metabolic cost and enhancing the loading force in exoskeletons, in which challenges such as structural design and kinetic interactions between the human and the robot are presented. The systematic review of the strategies found in the literature helps addressing these challenges in an orderly way. The proposal of some alternative solutions could help to solving some of the challenges mentioned above, as well as further research to improve the design of these devices is necessary.

Opportunities and challenges in the development of exoskeletons for locomotor assistance

Exoskeletons can augment the performance of unimpaired users and restore movement in individuals with gait impairments. Knowledge of how users interact with wearable devices and of the physiology of locomotion have informed the design of rigid and soft exoskeletons that can specifically target a single joint or a single activity. In this Review, we highlight the main advances of the past two decades in exoskeleton technology and in the development of lower-extremity exoskeletons for locomotor assistance, discuss research needs for such wearable robots and the clinical requirements for exoskeleton-assisted gait rehabilitation, and outline the main clinical challenges and opportunities for exoskeleton technology.

Interaction learning control with movement primitives for lower limb exoskeleton

Research on robotic exoskeletons both in the military and medical fields has rapidly expanded over the previous decade. As a human-robot interaction system, it is a challenge to develop an assistive strategy that makes the exoskeleton supply efficient and natural assistance following the user's intention. This paper proposed a novel interaction learning control strategy for the lower extremity exoskeleton. A powerful representative tool probabilistic movement primitives (ProMPs) is adopted to model the motion and generate the desired trajectory in real-time. To adjust the trajectory by the user's real-time intention, a compensation term based on human-robot interaction force is designed and merged into the ProMPs model. Then, compliant impedance control is adopted as a low-level control where the desired trajectory is put into. Moreover, the model will be dynamically adapted online by penalizing both the interaction force and trajectory mismatch, with all the parameters that can be further learned by learning algorithm PI^BB. The experimental results verified the effectiveness of the proposed control framework.

EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review

BACKGROUND

There is increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community.

METHODS

We performed an electronic search in Scopus, looking for studies reporting on rehabilitation in patients with neurological disabilities. We used the most influential papers to outline the knowledge base and carried out a word co-occurrence analysis to identify the research hotspots. We also used depicted collaboration networks between universities, authors, and countries after analyzing the cocitations. The results were presented in summary tables, plots, and maps. Finally, a content review based on the top-20 most cited articles completed our study.

RESULTS

Our current bibliometric study was based on 874 records from 420 sources. There was vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by J. Daly and J.R. Wolpaw (708 citations). The US, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of functional magnetic imaging to EEG-based brain-machine interface, motor imagery, and deep learning.

CONCLUSIONS

EEG constitutes the most significant input in brain-computer interfaces (BCIs) and can be successfully used in the neurorehabilitation of patients with stroke symptoms, amyotrophic lateral sclerosis, and traumatic brain and spinal injuries. EEG-based BCI facilitates the training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG-filtering algorithms.

User-centered design and development of TWIN-Acta: A novel control suite of the TWIN lower limb exoskeleton for the rehabilitation of persons post-stroke

Introduction

Difficulties faced while walking are common symptoms after stroke, significantly reducing the quality of life. Walking recovery is therefore one of the main priorities of rehabilitation. Wearable powered exoskeletons have been developed to provide lower limb assistance and enable training for persons with gait impairments by using typical physiological movement patterns. Exoskeletons were originally designed for individuals without any walking capacities, such as subjects with complete spinal cord injuries. Recent systematic reviews suggested that lower limb exoskeletons could be valid tools to restore independent walking in subjects with residual motor function, such as persons post-stroke. To ensure that devices meet end-user needs, it is important to understand and incorporate their perspectives. However, only a limited number of studies have followed such an approach in the post-stroke population.

Methods

The aim of the study was to identify the end-users needs and to develop a user-centered-based control system for the TWIN lower limb exoskeleton to provide post-stroke rehabilitation. We thus describe the development and validation, by clinical experts, of TWIN-Acta: a novel control suite for TWIN, specifically designed for persons post-stroke. We detailed the conceived control strategy and developmental phases, and reported evaluation sessions performed on healthy clinical experts and people post-stroke to evaluate TWIN-Acta usability, acceptability, and barriers to usage. At each developmental stage, the clinical experts received a one-day training on the TWIN exoskeleton equipped with the TWIN-Acta control suite. Data on usability, acceptability, and limitations to system usage were collected through questionnaires and semi-structured interviews.

Results

The system received overall good usability and acceptability ratings and resulted in a well-conceived and safe approach. All experts gave excellent ratings regarding the possibility of modulating the assistance provided by the exoskeleton during the movement execution and concluded that the TWIN-Acta would be useful in gait rehabilitation for persons post-stroke. The main limit was the low level of system learnability, attributable to the short-time of usage. This issue can be minimized with prolonged training and must be taken into consideration when planning rehabilitation.

Discussion

This study showed the potential of the novel control suite TWIN-Acta for gait rehabilitation and efficacy studies are the next step in its evaluation process.

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.

Model-Based Biomechanical Exoskeleton Concept Optimization for a Representative Lifting Task in Logistics

Occupational exoskeletons are a promising solution to prevent work-related musculoskeletal disorders (WMSDs). However, there are no established systems that support heavy lifting to shoulder height. Thus, this work presents a model-based analysis of heavy lifting activities and subsequent exoskeleton concept optimization. Six motion sequences were captured in the laboratory for three subjects and analyzed in multibody simulations with respect to muscle activities (MAs) and joint forces (JFs). The most strenuous sequence was selected and utilized in further simulations of a human model connected to 32 exoskeleton concept variants. Six simulated concepts were compared concerning occurring JFs and MAs as well as interaction loads in the exoskeleton arm interfaces. Symmetric uplifting of a 21 kg box from hip to shoulder height was identified as the most strenuous motion sequence with highly loaded arms, shoulders, and back. Six concept variants reduced mean JFs (spine: >70%, glenohumeral joint: >69%) and MAs (back: >63%, shoulder: >59% in five concepts). Parasitic loads in the arm bracing varied strongly among variants. An exoskeleton design was identified that effectively supports heavy lifting, combining high musculoskeletal relief and low parasitic loads. The applied workflow can help developers in the optimization of exoskeletons.

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Assessment methodology for human-exoskeleton interactions: Kinetic analysis based on muscle activation

During the development and assessment of an exoskeleton, many different analyzes need to be performed. The most frequently used evaluate the changes in muscle activations, metabolic consumption, kinematics, and kinetics. Since human-exoskeleton interactions are based on the exchange of forces and torques, the latter of these, kinetic analyzes, are essential and provide indispensable evaluation indices. Kinetic analyzes, however, require access to, and use of, complex experimental apparatus, involving many instruments and implicating lengthy data analysis processes. The proposed methodology in this paper, which is based on data collected via EMG and motion capture systems, considerably reduces this burden by calculating kinetic parameters, such as torque and power, without needing ground reaction force measurements. This considerably reduces the number of instruments used, allows the calculation of kinetic parameters even when the use of force sensors is problematic, does not need any dedicated software, and will be shown to have high statistical validity. The method, in fact, combines data found in the literature with those collected in the laboratory, allowing the analysis to be carried out over a much greater number of cycles than would normally be collected with force plates, thus enabling easy access to statistical analysis. This new approach evaluates the kinetic effects of the exoskeleton with respect to changes induced in the user's kinematics and muscular activation patterns and provides indices that quantify the assistance in terms of torque (AMI) and power (API). Following the User-Center Design approach, which requires driving the development process as feedback from the assessment process, this aspect is critical. Therefore, by enabling easy access to the assessment process, the development of exoskeletons could be positively affected.

Personalizing exoskeleton assistance while walking in the real world

Personalized exoskeleton assistance provides users with the largest improvements in walking speed1 and energy economy2-4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s-1. Human movements encode information that can be used to personalize assistive devices and enhance performance.