Neuromuscular Controller Embedded in a Powered Ankle Exoskeleton: Effects on Gait, Clinical Features and Subjective Perspective of Incomplete Spinal Cord Injured Subjects

Consumer views of functional electrical stimulation and robotic exoskeleton in SCI rehabilitation: A mini review

BACKGROUND

Functional electrical stimulation (FES) and robotic exoskeletons represent emerging technologies with significant potential for restoring critical physical functions such as standing and walking-functions that are most susceptible after spinal cord injury (SCI). However, the further development and successful integration of these technologies into clinical practice and daily life require a deep understanding of consumer perspectives.

OBJECTIVE

This review synthesizes consumer perspectives from a diverse range of technology stakeholders, including medical service providers, researchers, and persons affected by SCI-those living with SCI and their caregivers. By capturing this diverse range of perspectives, the review aims to describe the real-world implications, challenges, and expectations associated with FES and robotic exoskeleton technologies.

METHODS

Relevant literature was primarily identified through a search in EBSCO, SCOPUS, and Web of Science. The authors supplemented the search by reviewing reference lists including appropriate articles identified by the authors. The PICO question guiding this process was defined as P (persons with SCI and caregivers, researchers, clinicians, and developers), I (use of FES or robotic exoskeletons), C (technology users compared to non-users), and O (stakeholder perspectives and experiences). Each identified article underwent a thorough appraisal, after which findings were summarized to present consumers' viewpoints on FES and robotic exoskeleton technologies.

RESULTS

The review focuses on key areas such as perceived benefits, limitations, implementation barriers, and consumer expectations. The benefits identified are multifaceted, extending from physical improvements, such as enhanced mobility and muscle strength, to psychological gains including increased confidence and sense of independence. However, these technologies also face perceived limitations, often related to accessibility, cost, and usability challenges. Beyond technical issues, implementation barriers are related to factors like insurance coverage and the need for specialized training for both users and providers. Consumer expectations include hope for technological advancements, increased accessibility and affordability, and a desire for more personalized and adaptable solutions tailored to the unique needs of individuals with SCI.

CONCLUSION

This comprehensive overview of consumer perspectives offers insights into the needs and preferences of the end-users, which are essential for creating user-centric technology and effectively translating research findings into clinical practice.

User experience of lower extremity exoskeletons and its improvement methodologies: A narrative review

In this review, user experience (UX) of recent lower limb exoskeletons (LLEs) and its improvement methodologies are investigated. First, statistics based on standardised and custom UX evaluations are presented. It is indicated that, LLE users have positive UX, especially in the aspects of safety, dimension and effectiveness. Further, overall, UX levels of ankle and hip-knee exoskeletons are higher than those of other exoskeleton types; unilateral LLEs have higher mean UX levels than that of the bilateral ones. Then, design practices for improving UX are studied; the focused points are burden reduction and improvement of device fit. The former is achieved through lightweight design and approaches that reduce device's moment of inertia (MOI) at mechanical joints. Works on the latter refer to the endeavours to enhance static and dynamic fit; they mainly rely on the optimisations of human-robot interface (HRS) and endeavours to rectify misalignment of axes of mechanical and anatomic joints, respectively. The following section is control approaches to enhance wearing comfort level; it is mainly focused on adaptive, interaction and compensation-based controls. Finally, existing problems and future directions are stated and prospected respectively.

Identifying essential factors for energy-efficient walking control across a wide range of velocities in reflex-based musculoskeletal systems

Humans can generate and sustain a wide range of walking velocities while optimizing their energy efficiency. Understanding the intricate mechanisms governing human walking will contribute to the engineering applications such as energy-efficient biped robots and walking assistive devices. Reflex-based control mechanisms, which generate motor patterns in response to sensory feedback, have shown promise in generating human-like walking in musculoskeletal models. However, the precise regulation of velocity remains a major challenge. This limitation makes it difficult to identify the essential reflex circuits for energy-efficient walking. To explore the reflex control mechanism and gain a better understanding of its energy-efficient maintenance mechanism, we extend the reflex-based control system to enable controlled walking velocities based on target speeds. We developed a novel performance-weighted least squares (PWLS) method to design a parameter modulator that optimizes walking efficiency while maintaining target velocity for the reflex-based bipedal system. We have successfully generated walking gaits from 0.7 to 1.6 m/s in a two-dimensional musculoskeletal model based on an input target velocity in the simulation environment. Our detailed analysis of the parameter modulator in a reflex-based system revealed two key reflex circuits that have a significant impact on energy efficiency. Furthermore, this finding was confirmed to be not influenced by setting parameters, i.e., leg length, sensory time delay, and weight coefficients in the objective cost function. These findings provide a powerful tool for exploring the neural bases of locomotion control while shedding light on the intricate mechanisms underlying human walking and hold significant potential for practical engineering applications.

DiSCIoser: unlocking recovery potential of arm sensorimotor functions after spinal cord injury by promoting activity-dependent brain plasticity by means of brain-computer interface technology: a randomized controlled trial to test efficacy

BACKGROUND

Traumatic cervical spinal cord injury (SCI) results in reduced sensorimotor abilities that strongly impact on the achievement of daily living activities involving hand/arm function. Among several technology-based rehabilitative approaches, Brain-Computer Interfaces (BCIs) which enable the modulation of electroencephalographic sensorimotor rhythms, are promising tools to promote the recovery of hand function after SCI. The "DiSCIoser" study proposes a BCI-supported motor imagery (MI) training to engage the sensorimotor system and thus facilitate the neuroplasticity to eventually optimize upper limb sensorimotor functional recovery in patients with SCI during the subacute phase, at the peak of brain and spinal plasticity. To this purpose, we have designed a BCI system fully compatible with a clinical setting whose efficacy in improving hand sensorimotor function outcomes in patients with traumatic cervical SCI will be assessed and compared to the hand MI training not supported by BCI.

METHODS

This randomized controlled trial will include 30 participants with traumatic cervical SCI in the subacute phase randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and the hand MI training not supported by BCI. Both interventions are delivered (3 weekly sessions; 12 weeks) as add-on to standard rehabilitation care. A multidimensional assessment will be performed at: randomization/pre-intervention and post-intervention. Primary outcome measure is the Graded Redefined Assessment of Strength, Sensibility and Prehension (GRASSP) somatosensory sub-score. Secondary outcome measures include the motor and functional scores of the GRASSP and other clinical, neuropsychological, neurophysiological and neuroimaging measures.

DISCUSSION

We expect the BCI-based intervention to promote meaningful cortical sensorimotor plasticity and eventually maximize recovery of arm functions in traumatic cervical subacute SCI. This study will generate a body of knowledge that is fundamental to drive optimization of BCI application in SCI as a top-down therapeutic intervention, thus beyond the canonical use of BCI as assistive tool.

TRIAL REGISTRATION

Name of registry: DiSCIoser: improving arm sensorimotor functions after spinal cord injury via brain-computer interface training (DiSCIoser).

TRIAL REGISTRATION NUMBER

NCT05637775; registration date on the ClinicalTrial.gov platform: 05-12-2022.

A qualitative study to elicit user requirements for lower limb wearable exoskeletons for gait rehabilitation in spinal cord injury

OBJECTIVE

We aim to determine a comprehensive set of requirements, perceptions, and expectations that people with spinal cord injury (SCI) and the clinicians in charge of their rehabilitation have regarding the use of wearable robots (WR) for gait rehabilitation.

BACKGROUND

There are concerns due to the limited user acceptance of WR for gait rehabilitation. Developers need to emphasize understanding the needs and constraints of all stakeholders involved, including the real-life dynamics of rehabilitation centers.

METHODS

15 people with SCI, 9 without experience with WR and 6 with experience with these technologies, and 10 clinicians from 3 rehabilitation centers in Spain were interviewed. A directed content analysis approach was used.

RESULTS

78 codes grouped into 9 categories (physical results, usability, psychology-related codes, technical characteristics, activities, acquisition issues, context of use, development of the technologies and clinical rehabilitation context) were expressed by at least 20% of the users interviewed, of whom 16 were not found in the literature. The agreement percentage between each group and subgroup included in the study, calculated as the number of codes that more than 20% of both groups expressed, divided over the total amount of codes any of those two groups agreed on (≥ 20%), showed limited agreement between patients and clinicians (50.00%) and between both types of patients (55.77%). The limited accessibility and availability of lower limb exoskeletons for gait rehabilitation arose in most of the interviews.

CONCLUSIONS

The limited agreement percentage between patients and clinicians indicates that including both types of users in the design process of these technologies is important, given that their requirements are complementary. Engaging users with prior technology experience is recommended, as they often exhibit strong internal consensus and articulate well-defined requirements. This study adds up the knowledge available in the literature and the new codes found in our data, which enlighten important aspects that ought to be addressed in the field to develop technologies that respond to users' needs, are usable and feasible to implement in their intended contexts.

APPLICATION

The set of criteria summarized in our study will be useful to guide the design, development, and evaluation of WR for gait rehabilitation to meet user's needs and allow them to be implemented in their intended context of use.

Novel Design and Implementation of a Neuromuscular Controller on a Hip Exoskeleton for Partial Gait Assistance

Exoskeletons intended for partial assistance of walking should be able to follow the gait pattern of their users, via online adaptive control strategies rather than imposing predefined kinetic or kinematic profiles. NeuroMuscular Controllers (NMCs) are adaptive strategies inspired by the neuromuscular modeling methods that seek to mimic and replicate the behavior of the human nervous system and skeletal muscles during gait. This study presents a novel design of a NMC, applied for the first time to partial assistance hip exoskeletons. Rather than the two-phase (stance/swing) division used in previous designs for the modulation of reflexes, a 5-state finite state machines is designed for gait phase synchronisation. The common virtual muscle model is also modified by assuming a stiff tendon, allowing for a more analytical computation approach for the muscle state resolution. As a first validation, the performance of the controller was tested with 9 healthy subjects walking at different speeds and slopes on a treadmill. The generated torque profiles show similarity to biological torques and optimal assistance profiles in the literature. Power output profiles of the exoskeleton indicate good synchronization with the users' intended movements, reflected in predominantly positive work by the assistance. The results also highlight the adaptability of the controller to different users and walking conditions, without the need for extensive parameter tuning.

Subject-Specific and COM Acceleration-Enhanced Reflex Neuromuscular Model to Predict Ankle Responses in Perturbed Gait

Subject-specific musculoskeletal models generate more accurate joint torque estimates from electromyography (EMG) inputs in relation to experimentally obtained torques. Similarly, reflex Neuromuscular Models (NMMs) that employ COM states in addition to musculotendon information generate muscle activations to musculoskeletal models that better predict ankle torques during perturbed gait. In this study, the reflex NMM of locomotion of one subject is identified by employing an EMG-calibrated musculoskeletal model in unperturbed and perturbed gait. A COM acceleration-enhanced reflex NMM is identified. Subject-specific musculoskeletal models improve torque tracking of the ankle joint in unperturbed and perturbed conditions. COM acceleration-enhanced reflex NMM improves ankle torque tracking especially in early stance and during backward perturbation. Results found herein can guide the implementation of reflex controllers in active prosthetic and orthotic devices.

Advances on mechanical designs for assistive ankle-foot orthoses

Assistive ankle-foot orthoses (AAFOs) are powerful solutions to assist or rehabilitate gait on humans. Existing AAFO technologies include passive, quasi-passive, and active principles to provide assistance to the users, and their mechanical configuration and control depend on the eventual support they aim for within the gait pattern. In this research we analyze the state-of-the-art of AAFO and classify the different approaches into clusters, describing their basis and working principles. Additionally, we reviewed the purpose and experimental validation of the devices, providing the reader with a better view of the technology readiness level. Finally, the reviewed designs, limitations, and future steps in the field are summarized and discussed.

The neuromechanics of animal locomotion: From biology to robotics and back

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator-like controllers, which, in turn, are informed by sensory feedback. Reciprocally, neuroscience has benefited from tools and intuitions in robotics to reveal how embodiment, physical interactions with the environment, and sensory feedback help sculpt animal behavior. We illustrate and discuss exemplar studies of this dialog between robotics and neuroscience. We also reveal how the increasing biorealism of simulations and robots is driving these two disciplines together, forging an integrative science of autonomous behavioral control with many exciting future opportunities.

Satisfaction analysis of overground gait exoskeletons in people with neurological pathology. a systematic review

BACKGROUND

People diagnosed with neurological pathology may experience gait disorders that affect their quality of life. In recent years, research has been carried out on a variety of exoskeletons in this population. However, the satisfaction perceived by the users of these devices is not known. Therefore, the objective of the present study is to evaluate the satisfaction perceived by users with neurological pathology (patients and professionals) after the use of overground exoskeletons.

METHODS

A systematic search of five electronic databases was conducted. In order to be included in this review for further analysis, the studies had to meet the following criteria: [1] the study population was people diagnosed with neurological pathology; [2] the exoskeletons had to be overground and attachable to the lower limbs; and [3]: the studies were to include measures assessing either patient or therapist satisfaction with the exoskeletons.

RESULTS

Twenty-three articles were selected, of which nineteen were considered clinical trials. Participants diagnosed with stroke (n = 165), spinal cord injury (SCI) (n = 102) and multiple sclerosis (MS) (n = 68). Fourteen different overground exoskeleton models were analysed. Fourteen different methods of assessing patient satisfaction with the devices were found, and three ways to evaluate it in therapists.

CONCLUSION

Users' satisfaction with gait overground exoskeletons in stroke, SCI and MS seems to show positive results in safety, efficacy and comfort of the devices. However, the worst rated aspects and therefore those that should be optimized from the users' point of view are ease of adjustment, size and weight, and ease of use.

Recent advances in wearable actuated ankle-foot orthoses: Medical effects, design, and control

This paper presents a survey on recent advances of wearable actuated ankle-foot orthoses (AAFOs). First of all, their medical functions are investigated. From the short-term aspect, they lead to rectification of pathological gaits, reduction of metabolic cost, and improvement of gait performance. After AAFO-based walking training with sufficient time, free walking performance can be enhanced. Then, key design factors are studied. First, primary design parameters are investigated. Second, common actuators are analysed. Third, human-robot interaction (HRI), ergonomics, safety, and application places, are considered. In the following section, control technologies are reviewed from the aspects of rehabilitation stages, gait feature quantities, and controller characteristics. Finally, existing problems are discussed; development trends are prospected.

sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning

Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a sliding window with long-term memory. Firstly, the key state modeled by GMM-HMMs is set as the condition. Then, the sliding window is used to dynamically look for the key state transition. The prediction results are given while finding the key state transition. To extend continuous multigesture action prediction, we use model pruning to improve reusability. Eight subjects participated in the experiment, and the results show that the average accuracy of continuous two-hand actions is 97% with a 70 ms time delay, which is better than LSTM (94.15%, 308 ms) and GRU (93.83%, 300 ms). In supplementary experiments with continuous four-hand actions, over 85% prediction accuracy is achieved with an average time delay of 90 ms.

Implementation of Robotic Ankle–Foot Orthosis With an Impedance-Based Assist-as-Needed Control Strategy

In this paper, a robotic ankle–foot orthosis (AFO) is developed for individuals with a paretic ankle, and an impedance-based assist-as-needed controller is designed for the robotic AFO to provide adaptive assistance. First, a description of the robotic AFO hardware design is presented. Next, the design of the finite state machine is introduced, followed by an introduction to the modeling of the robotic AFO. Additionally, the control of the robotic AFO is presented. An impedance-based high-level controller that is composed of an ankle impedance based torque generation controller and an impedance controller is designed for the high-level control. A compensated low-level controller that is composed of a braking controller and a proportional-derivative controller with a compensation part is designed for the low-level control. Finally, a pilot study with eight healthy participants is conducted, and the experimental results demonstrate that with the proposed control algorithm, the robotic AFO has the potential for ankle rehabilitation by providing adaptive assistance. In the assisted condition with a high level of assistance, reductions of 8% and 20.1% of the root mean square of the tibialis anterior and lateral soleus activities are observed, respectively.

A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control

Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging's region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ( < .001) and increased the prediction coefficient of determination by 20.13% ( < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy.

Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds

Background

Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy.

Objective

The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging.

Methods

Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds.

Results

On average, the normalized moment prediction root mean square error was reduced by 14.58 % (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p=0.012$$\end{document}p=0.012) and 36.79 % (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction.

Conclusions

The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01061-z.

Kinematic Analysis of Exoskeleton-Assisted Community Ambulation: An Observational Study in Outdoor Real-Life Scenarios

(1) Background: In neurorehabilitation, Wearable Powered Exoskeletons (WPEs) enable intensive gait training even in individuals who are unable to maintain an upright position. The importance of WPEs is not only related to their impact on walking recovery, but also to the possibility of using them as assistive technology; however, WPE-assisted community ambulation has rarely been studied in terms of walking performance in real-life scenarios. (2) Methods: This study proposes the integration of an Inertial Measurement Unit (IMU) system to analyze gait kinematics during real-life outdoor scenarios (regular, irregular terrains, and slopes) by comparing the ecological gait (no-WPE condition) and WPE-assisted gait in five able-bodied volunteers. The temporal parameters of gait and joint angles were calculated from data collected by a network of seven IMUs. (3) Results: The results showed that the WPE-assisted gait had less knee flexion in the stance phase and greater hip flexion in the swing phase. The different scenarios did not change the human–exoskeleton interaction: only the low-speed WPE-assisted gait was characterized by a longer double support phase. (4) Conclusions: The proposed IMU-based gait assessment protocol enabled quantification of the human–exoskeleton interaction in terms of gait kinematics and paved the way for the study of WPE-assisted community ambulation in stroke patients.

Coordination Between Partial Robotic Exoskeletons and Human Gait: A Comprehensive Review on Control Strategies

Lower-limb robotic exoskeletons have become powerful tools to assist or rehabilitate the gait of subjects with impaired walking, even when they are designed to act only partially over the locomotor system, as in the case of unilateral or single-joint exoskeletons. These partial exoskeletons require a proper method to synchronize their assistive actions and ensure correct inter-joint coordination with the user’s gait. This review analyzes the state of the art of control strategies to coordinate the assistance provided by these partial devices with the actual gait of the wearers. We have analyzed and classified the different approaches independently of the hardware implementation, describing their basis and principles. We have also reviewed the experimental validations of these devices for impaired and unimpaired walking subjects to provide the reader with a clear view of their technology readiness level. Eventually, the current state of the art and necessary future steps in the field are summarized and discussed.

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

Whole Body Center of Mass Feedback in a Reflex-Based Neuromuscular Model Predicts Ankle Strategy During Perturbed Walking

Active prosthetic and orthotic devices have the potential to increase quality of life for individuals with impaired mobility. However, more research into human-like control methods is needed to create seamless interaction between device and user. In forward simulations the reflex-based neuromuscular model (RNM) by Song and Geyer shows promising similarities with real human gait in unperturbed conditions. The goal of this work was to validate and, if needed, extend the RNM to reproduce human kinematics and kinetics during walking in unperturbed and perturbed conditions. The RNM was optimized to reproduce joint torque, calculated with inverse dynamics, from kinematic and force data of unperturbed and perturbed treadmill walking of able-bodied human subjects. Torques generated by the RNM matched closely with torques found from inverse dynamics analysis on human data for unperturbed walking. However, for perturbed walking the modulation of the ankle torque in the RNM was opposite to the modulation observed in humans. Therefore, the RNM was extended with a control module that activates and inhibits muscles around the ankle of the stance leg, based on changes in whole body center of mass velocity. The added module improves the ability of the RNM to replicate human ankle torque response in response to perturbations. This reflex-based neuromuscular model with whole body center of mass velocity feedback can reproduce gait kinetics of unperturbed and perturbed gait, and as such holds promise as a basis for advanced controllers of prosthetic and orthotic devices.

Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model

Many rehabilitative exoskeletons use non-invasive surface electromyography (sEMG) to measure human volitional intent. However, signals from adjacent muscle groups interfere with sEMG measurements. Further, the inability to measure sEMG signals from deeply located muscles may not accurately measure the volitional intent. In this work, we combined sEMG and ultrasound (US) imaging-derived signals to improve the prediction accuracy of voluntary ankle effort. We used a multivariate linear model (MLM) that combines sEMG and US signals for ankle joint net plantarflexion (PF) moment prediction during the walking stance phase. We hypothesized that the proposed sEMG-US imaging-driven MLM would result in more accurate net PF moment prediction than sEMG-driven and US imaging-driven MLMs. Synchronous measurements including reflective makers coordinates, ground reaction forces, sEMG signals of lateral/medial gastrocnemius (LGS/MGS), and soleus (SOL) muscles, and US imaging of LGS and SOL muscles were collected from five able-bodied participants walking on a treadmill at multiple speeds. The ankle joint net PF moment benchmark was calculated based on inverse dynamics, while the net PF moment prediction was determined by the sEMG-US imaging-driven, sEMG-driven, and US imaging-driven MLMs. The findings show that the sEMG-US imaging-driven MLM can significantly improve the prediction of net PF moment during the walking stance phase at multiple speeds. Potentially, the proposed sEMG-US imaging-driven MLM can be used as a superior joint motion intent model in advanced and intelligent control strategies for rehabilitative exoskeletons.

A simulation-based framework with a proprioceptive musculoskeletal model for evaluating the rehabilitation exoskeleton system

BACKGROUND AND OBJECTIVE

Various rehabilitation exoskeletons have been designed to help people regain normal gait from stroke effects. However, the evaluation and further optimization of these exoskeletons are not convenient and usually need complicated experimental works. The present study aims to establish a simulation-based method with a proprioceptive musculoskeletal model to conveniently evaluate the efficiency of a self-developed exoskeleton for further optimization.

METHODS

Three volunteers who suffer from dyskinesia due to stroke were recruited for gait experiments with and without the self-develop exoskeleton. The corresponding simulations were implemented based on the proprioceptive model, the exoskeleton model, and the input kinematic data obtained from the experiments. The joint angles, muscle activations, and metabolic costs as well as the proprioceptor feedback stimulation were extracted for comparative analysis.

RESULT

Several positive effects of the exoskeleton were noted based on the simulation results when using it to aid the patients' rehabilitation during the gait training. The CORA scores of the patients' joint angle to the normal data increased by 11.6~37.8% with the assistance of the exoskeleton. The wave frequency of proprioceptive feedback stimulation that can be directly correlated to the neural rehabilitation obviously inclined during a gait cycle. The muscle activations were also rearranged to better support the patient's walk when using the exoskeleton, while the metabolic costs were reduced for all the patients.

CONCLUSION

In summary, the present simulation-based method can be practical for pre-evaluation and optimization of various exoskeleton design in the future.

Human biomechanics perspective on robotics for gait assistance: challenges and potential solutions

Technological advancements in robotic devices have the potential to transform human mobility through gait assistance. However, the integration of physical hardware and software control algorithms with users to assist with impaired gait poses several challenges, such as allowing the user to adopt a variety of gaits and the process for evaluating the efficacy and performance of these assistive devices. Here, I discuss some of the challenges in the development of assistive devices and the use of biomechanical concepts and tools for control and test validation. Several potential solutions are proposed through the case study of one project that aimed to provide gait assistance for individuals with a spinal cord injury. Further challenges and future directions are discussed, with emphasis that diverse perspectives and approaches in gait assistance will accelerate engineering solutions towards regaining mobility.

Review of control strategies for lower-limb exoskeletons to assist gait

Background

Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user.

Methods

In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller.

Results

291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper.

Conclusions

By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00906-3.

Load Auditory Feedback Boosts Crutch Usage in Subjects With Central Nervous System Lesions: A Pilot Study

Background: Crutches are the most common walking aids prescribed to improve mobility in subjects with central nervous system (CNS) lesions. To increase adherence to the appropriate level of crutch usage, providing load-related auditory feedback (aFB) may be a useful approach. We sensorized forearm crutches and developed a custom software to provide aFB information to both user and physical therapist (PhT).

Aim: Evaluate aFB effects on load control during gait by a self-controlled case series trial.

Methods: A single experimental session was conducted enrolling 12 CNS lesioned participants. Load on crutch was recorded during 10 Meter Walk Test performed with and without aFB. In both cases, crutch load data, and gait speed were recorded. Usability and satisfaction questionnaires were administered to participants and PhTs involved.

Results: Reliable data were obtained from eight participants. Results showed that compared to the no FB condition, aFB yielded a significant reduction in the mean load on the crutches during gait (p = 0.001). The FB did not influence gait speed or fatigue (p > 0.05). The experience questionnaire data indicated a positive experience regarding the use of aFB from both participants' and PhTs' perspectives.

Conclusion: aFB significantly improves compliance with crutch use and does not affect gait speed or fatigue by improving the load placed on crutches. The FB is perceived by users as helpful, safe, and easy to learn, and does not interfere with attention or concentration while walking. Furthermore, the PhTs consider the system to be useful, easy to learn and reliable.

Optimizing Exoskeleton Assistance for Faster Self-Selected Walking

Self-selected walking speed is an important aspect of mobility. Exoskeletons can increase walking speed, but the mechanisms behind these changes and the upper limits on performance are unknown. Human-in-the-loop optimization is a technique for identifying exoskeleton characteristics that maximize the benefits of assistance, which has been critical to achieving large improvements in energy economy. In this study, we used human-in-the-loop optimization to test whether large improvements in self-selected walking speed are possible through ankle exoskeleton assistance. Healthy participants (N =10) were instructed to walk at a comfortable speed on a self-paced treadmill while wearing tethered ankle exoskeletons. An algorithm sequentially applied different patterns of exoskeleton torque and estimated the speed-optimal pattern, which was then evaluated in separate trials. With torque optimized for speed, participants walked 42% faster than in normal shoes (1.83 ms^-1 vs. 1.31 ms^-1; Tukey HSD, p = 4 ×10^-8 ), with speed increases ranging from 6% to 91%. Participants walked faster with speed-optimized torque than with torque optimized for energy consumption (1.55 ms^-1) or torque chosen to induce slow walking (1.18 ms^-1). Gait characteristics with speed-optimized torque were highly variable across participants, and changes in metabolic cost of transport ranged from a 31% decrease to a 78% increase, with a decrease of 2% on average. These results demonstrate that ankle exoskeletons can facilitate large increases in self-selected walking speed, which could benefit older adults and others with reduced walking speed.

Neuromechanics and Energetics of Walking With an Ankle Exoskeleton Using Neuromuscular-Model Based Control: A Parameter Study

Powered ankle exoskeletons that apply assistive torques with optimized timing and magnitude can reduce metabolic cost by ∼10% compared to normal walking. However, finding individualized optimal control parameters is time consuming and must be done independently for different walking modes (e.g., speeds, slopes). Thus, there is a need for exoskeleton controllers that are capable of continuously adapting torque assistance in concert with changing locomotor demands. One option is to use a biologically inspired, model-based control scheme that can capture the adaptive behavior of the human plantarflexors during natural gait. Here, based on previously demonstrated success in a powered ankle-foot prosthesis, we developed an ankle exoskeleton controller that uses a neuromuscular model (NMM) comprised of a Hill type musculotendon driven by a simple positive force feedback reflex loop. To examine the effects of NMM reflex parameter settings on (i) ankle exoskeleton mechanical performance and (ii) users' physiological response, we recruited nine healthy, young adults to walk on a treadmill at a fixed speed of 1.25 m/s while donning bilateral tethered robotic ankle exoskeletons. To quantify exoskeleton mechanics, we measured exoskeleton torque and power output across a range of NMM controller Gain (0.8-2.0) and Delay (10-40 ms) settings, as well as a High Gain/High Delay (2.0/40 ms) combination. To quantify users' physiological response, we compared joint kinematics and kinetics, ankle muscle electromyography and metabolic rate between powered and unpowered/zero-torque conditions. Increasing NMM controller reflex Gain caused increases in average ankle exoskeleton torque and net power output, while increasing NMM controller reflex Delay caused a decrease in net ankle exoskeleton power output. Despite systematic reduction in users' average biological ankle moment with exoskeleton mechanical assistance, we found no NMM controller Gain or Delay settings that yielded changes in metabolic rate. Post hoc analyses revealed weak association at best between exoskeleton and biological mechanics and changes in users' metabolic rate. Instead, changes in users' summed ankle joint muscle activity with powered assistance correlated with changes in their metabolic energy use, highlighting the potential to utilize muscle electromyography as a target for on-line optimization in next generation adaptive exoskeleton controllers.

Symbitron Exoskeleton: Design, Control, and Evaluation of a Modular Exoskeleton for Incomplete and Complete Spinal Cord Injured Individuals

In this paper, we present the design, control, and preliminary evaluation of the Symbitron exoskeleton, a lower limb modular exoskeleton developed for people with a spinal cord injury. The mechanical and electrical configuration and the controller can be personalized to accommodate differences in impairments among individuals with spinal cord injuries (SCI). In hardware, this personalization is accomplished by a modular approach that allows the reconfiguration of a lower-limb exoskeleton with ultimately eight powered series actuated (SEA) joints and high fidelity torque control. For SCI individuals with an incomplete lesion and sufficient hip control, we applied a trajectory-free neuromuscular control (NMC) strategy and used the exoskeleton in the ankle-knee configuration. For complete SCI individuals, we used a combination of a NMC and an impedance based trajectory tracking strategy with the exoskeleton in the ankle-knee-hip configuration. Results of a preliminary evaluation of the developed hardware and software showed that SCI individuals with an incomplete lesion could naturally vary their walking speed and step length and walked faster compared to walking without the device. SCI individuals with a complete lesion, who could not walk without support, were able to walk with the device and with the support of crutches that included a push-button for step initiation Our results demonstrate that an exoskeleton with modular hardware and control allows SCI individuals with limited or no lower limb function to receive tailored support and regain mobility.