Wearable Lower-Limb Exoskeleton for Children With Cerebral Palsy: A Systematic Review of Mechanical Design, Actuation Type, Control Strategy, and Clinical Evaluation

Optimization of Exoskeleton Trajectory Toward Minimizing Human Joint Torques

The reference trajectory, serving as the sole kinematic guidance, is crucial for exoskeleton robot systems. This study introduces a method for generating an optimal trajectory for lower-limb exoskeletons, aiming at reducing human power during walking. Initially, the human joint angles were computed from measured data by a neighborhood field optimization (NFO). Subsequently, inverse dynamic analysis including seven-link dynamic model of human-exoskeleton coupling and corresponding ground reaction forces optimization were constructed, which was surrogated by a back propagation neural network (BPNN) to accelerate successive analyses. The exoskeleton trajectory, generated by perturbing human movement described by Fourier series, was optimized using a NFO algorithm with a revised initial generation strategy and boundary update function to minimize human joint torques. This approach was found to provide more accurate predictions of human trajectory and ground reaction forces compared to traditional methods, achieving a root mean square error (RMSE) within 5 mm and 3 kN respectively, making it suitable for computational applications. The generated trajectory preserves individual walking patterns and anticipates human motion with a mean leading value of 4.6%, effectively reducing joint torque across various gait phases. This research contributes significantly to the analysis of human-exoskeleton interactions and offers valuable insights for designing energy-efficient exoskeletons.

ChMER: an exoskeleton robot with active body weight support walker based on compliant actuation for children with cerebral palsy

Introduction

Lower limb exoskeleton robots for young children with cerebral palsy (CP) are crucial to support earlier rehabilitation that is more beneficial than later. For safety reasons, pediatric exoskeletons are usually equipped with body weight support (BWS) devices to help young patients maintain balance. However, existing pediatric exoskeletons tend to use stiff joint actuation and passive BWS with limited compliance.

Method

This paper proposes a novel mobile exoskeleton robot for young children (3- ∼ 6-years-old) with CP based on intrinsically compliant actuation. A compact kinematic chain that integrates an exoskeleton, an active BWS system, and a walker is proposed. Furthermore, with the actuation design optimization of the kinematic chain, the robot can walk alone stably in passive rehabilitation and provide high compliance in active rehabilitation. The exoskeleton adopts actuation similar to the quasi-direct drive paradigm to acquire high mechanical compliance and uses a secondary planetary reducer to ensure high output torque. Assistive torque control is achieved through proprioceptive sensing instead of torque sensors. The BWS system uses a series elastic actuator to accurately generate the weight support force and significantly reduce the fluctuation of the support force compared to the passive BWS.

Results and discussion

Finally, control frameworks for passive and active rehabilitation are implemented to validate the robot performance. The experimental results demonstrate that our robot can support safe and compliant rehabilitation.

Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography

The estimation of multijoint angles is of great significance in the fields of lower limb rehabilitation, motion control, and exoskeleton robotics. Accurate joint angle estimation helps assess joint function, assist in rehabilitation training, and optimize robotic control strategies. However, estimating multijoint angles in different movement patterns, such as walking, obstacle crossing, squatting, and knee flexion-extension, using surface electromyography (sEMG) signals remains a challenge. In this study, a model is proposed for the continuous motion estimation of multijoint angles in the lower limb (CB-TCN: temporal convolutional network + convolutional block attention module + temporal convolutional network). The model integrates temporal convolutional networks (TCNs) with convolutional block attention modules (CBAMs) to enhance feature extraction and improve prediction accuracy. The model effectively captures temporal features in lower limb movements, while enhancing attention to key features through the attention mechanism of CBAM. To enhance the model's generalization ability, this study adopts a sliding window data augmentation method to expand the training samples and improve the model's adaptability to different movement patterns. Through experimental validation on 8 subjects across four typical lower limb movements, walking, obstacle crossing, squatting, and knee flexion-extension, the results show that the CB-TCN model outperforms traditional models in terms of accuracy and robustness. Specifically, the model achieved R2 values of up to 0.9718, RMSE as low as 1.2648°, and NRMSE values as low as 0.05234 for knee angle prediction during walking. These findings indicate that the model combining TCN and CBAM has significant advantages in predicting lower limb joint angles. The proposed approach shows great promise for enhancing lower limb rehabilitation and motion analysis.

Comprehensive multi-metric analysis of user experience and performance in adaptive and non-adaptive lower-limb exoskeletons

Among control methods for robotic exoskeletons, biologically inspired control based on central pattern generators (CPGs) offer a promising approach to generate natural and robust walking patterns. Compared to other approaches, like model-based and machine learning-based control, the biologically inspired control provides robustness to perturbations, requires less computational power, and does not need system models or large learning datasets. While it has shown effectiveness, a comprehensive evaluation of its user experience is lacking. Thus, this study addressed this gap by investigating the performance of a state-of-the-art adaptive CPG-based exoskeleton control system (intelligent mode) under a multi-metric analysis (involving three-dimensional gait analysis, muscle activity, oxygen consumption, user comfort, and exoskeleton performance scores) and comparing it to a standard commercial exoskeleton control system (default mode). A cross-over design with randomized allocation in Thai healthy and independently walking adults ensured participants experienced both modes. All participants were assigned into two groups to receive an alternate sequence of walking with the intelligent mode or the default mode of the lower-limb exoskeleton Exo-H3 at high and normal speed. From eight participants, the intelligent mode-driven exoskeleton (adaptive exoskeleton) showed a significantly lower velocity, stride, and step lengths than the default mode-driven exoskeleton (non-adaptive exoskeleton). This setup significantly increased anterior pelvic tilt during mid-swing at normal speed (3.69 ± 1.77 degrees, p = 0.001) and high speed (2.52 ± 1.69 degrees, p = 0.004), hip flexion during stance phase with ankle dorsiflexion, and used less oxygen consumption at high speed (-2.03 ± 2.07 ml/kg/min) when compared to the default one. No significant differences of muscle activity, user comfort and exoskeleton performance scores between the two modes. Further exoskeletal modification in terms of hardware and control is still needed to improve the temporal spatial, kinematics, user comfort, and exoskeleton performance.

Changes in Kinematic and Spatiotemporal Gait Parameters With a Biarticular Lower Limb Exosuit for Adolescents With Crouch Gait During Level Walking and Stair Climbing

Crouch gait is a prevalent walking abnormality among children with cerebral palsy, characterized by excessive knee and hip flexion during walking. This condition often limits children's engagement in physical activities and daily life. Current exoskeleton solutions targeting the knee joint in this population are either tethered or bulky, hindering practical integration into daily routines. In this cross-sectional study, we evaluated the impact of a biarticular cable-driven exosuit, originally designed for adults, on the gait pattern of adolescents with crouch gait. Participants completed level walking and stair climbing trials under three conditions: without the exosuit (noMyo), with the exosuit inactive (MyoOff), and with the exosuit active (MyoOn). Kinematic and spatiotemporal gait metrics were analyzed using 3D motion capture. Five male adolescents with mild to moderate crouch participated. Results revealed significant improvements in mean knee and hip extension during the assisted phase (5 to 50% of the gait) with MyoOn compared to noMyo, increasing by 6 (range: 0 - 12) and 12 (range: 4 - 24) degrees, respectively, during level walking. During stair climbing, knee and hip extension improved in the stance phase of the trailing leg in the MyoOn condition compared to MyoOff. Only the hip angles improved in the MyoOn condition compared to noMyo. Spatiotemporal metrics showed no improvement. Stride length shortened significantly in both MyoOn and MyoOff. These findings demonstrate the exosuit's potential to address extension deficits in crouch gait, although its weight may limit improvements in spatiotemporal gait characteristics. Developing a lighter, child-specific version could expand accessibility to a broader pediatric population.

Review on Portable-Powered Lower Limb Exoskeletons

Advancements in science and technology have driven the growing use of robots in daily life, with Portable-Powered Lower Limb Exoskeletons (PPLLEs) emerging as a key innovation. The selection of mechanisms, control strategies, and sensors directly influences the overall performance of the exoskeletons, making it a crucial consideration for research and development. This review examines the current state of PPLLE research, focusing on the aspects of mechanisms, control strategies, and sensors. We discuss the current research status of various technologies, their technological compatibility, and respective benefits comprehensively. Key findings highlight effective designs and strategies, as well as future challenges and opportunities. Finally, we summarize the overall status of PPLLE research and attempt to shed light on the future potential directions of research and development.

Artificial intelligence and machine learning approaches in cerebral palsy diagnosis, prognosis, and management: a comprehensive review

Cerebral palsy (CP) is a group of disorders that alters patients' muscle coordination, posture, and movement, resulting in a wide range of deformities. Cerebral palsy can be caused by various factors, both prenatal and postnatal, such as infections or injuries that damage different parts of the brain. As brain plasticity is more prevalent during childhood, early detection can help take the necessary course of management and treatments that would significantly benefit patients by improving their quality of life. Currently, cerebral palsy patients receive regular physiotherapies, occupational therapies, speech therapies, and medications to deal with secondary abnormalities arising due to CP. Advancements in artificial intelligence (AI) and machine learning (ML) over the years have demonstrated the potential to improve the diagnosis, prognosis, and management of CP. This review article synthesizes existing research on AI and ML techniques applied to CP. It provides a comprehensive overview of the role of AI-ML in cerebral palsy, focusing on its applications, benefits, challenges, and future prospects. Through an extensive examination of existing literature, we explore various AI-ML approaches, including but not limited to assessment, diagnosis, treatment planning, and outcome prediction for cerebral palsy. Additionally, we address the ethical considerations, technical limitations, and barriers to the widespread adoption of AI-ML for CP patient care. By synthesizing current knowledge and identifying gaps in research, this review aims to guide future endeavors in harnessing AI-ML for optimizing outcomes and transforming care delivery in cerebral palsy rehabilitation.

Upper limb exoskeleton rehabilitation robot inverse kinematics modeling and solution method based on multi-objective optimization

The inverse kinematics problem of exoskeleton rehabilitation robots is challenging due to the lack of a standard analytical model, resulting in a complex and varied solution process. This complexity is especially pronounced in redundant upper limb exoskeleton robots, where inefficient solutions hinder the robot's ability to adapt to the kinematic shape of the upper limb. This paper proposes a modeling and solution method based on multi-objective optimization to address the inverse kinematics of upper limb exoskeleton robots. We analyzed and validated this method using a redundant upper limb exoskeleton rehabilitation robot system developed by ourselves. First, we established a multi-objective inverse kinematics solution model by defining the end-position function, joint motion comfort function, system energy consumption function, motion safety, and human-like constraints. Then, the solution was designed based on the Improved Equilibrium Optimization (IEO) algorithm and validated its computational performance in terms of optimization ability, accuracy, and robustness. Finally, we experimentally tested the inverse kinematics solution model with the IEO algorithm on discrete objectives and continuous training trajectories using a redundant upper limb exoskeleton rehabilitation robot system. The results show that by incorporating the joint comfort function, system energy consumption function, and human-like constraints into the inverse kinematics model, we can not only quickly solve the inverse kinematics of redundant upper limb exoskeleton robots but also significantly improve the robot's motion shape. Furthermore, it has better solution accuracy and stronger robustness than other algorithms when solving this inverse kinematics model based on the IEO algorithm.

Evaluation of the Working Mechanism of a Newly Developed Powered Ankle-Foot Orthosis

Ankle-foot orthoses (AFOs) are commonly prescribed to children with cerebral palsy (CP). The conventional AFO successfully controls the first and second ankle rocker, but it fails to correct the third ankle rocker, which negatively effects push-off power. The current study evaluated a new powered AFO (PAFO) design, developed to address the shortcomings of the conventional AFO. Eight children with spastic CP (12.4 ± 3.4 years; GMFCS I-III; 4/4-♂/♀; 3/5-bi/unilateral) were included. Sagittal kinematic and kinetic data were collected from 20 steps during barefoot walking, with conventional AFOs and PAFOs. In the PAFO-condition, an actuation unit was attached to a hinged AFO and through push-pull cables to a backpack that was carried by the child and provided patient-specific assistance-as-needed. SnPM-analysis indicated gait cycle sections that differed significantly between conditions. For the total group, differences between the three conditions were found in ankle kinematics (49.6-66.1%, p = 0.006; 88.0-100%, p = 0.011) and angular velocity (0.0-6.0%, p = 0.001; 45.1-51.1%, p = 0.006; 62.2-73.0%, p = 0.001; 81.2-93.0%, p = 0.001). Individual SnPM-analysis revealed a greater number of significant gait cycle sections for kinematics and kinetics of the ankle, knee, and hip. These individual results were heterogeneous and specific per gait pattern. In conclusion, the new PAFO improved the ankle range-of-motion, angular velocity, and power during push-off in comparison to the conventional AFO.

Wearable Technology and Its Influence on Motor Development and Biomechanical Analysis

The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.

Physiotherapy-assisted overground exoskeleton use: mixed methods feasibility study protocol quantifying the user experience, as well as functional, neural, and muscular outcomes in children with mobility impairments

Background

Early phase research suggests that physiotherapy paired with use of robotic walking aids provides a novel opportunity for children with severe mobility challenges to experience active walking. The Trexo Plus is a pediatric lower limb exoskeleton mounted on a wheeled walker frame, and is adjustable to fit a child's positional and gait requirements. It guides and powers the child's leg movements in a way that is individualized to their movement potential and upright support needs, and can provide progressive challenges for walking within a physiotherapy-based motor learning treatment paradigm.

Methods

This protocol outlines a single group mixed-methods study that assesses the feasibility of physiotherapy-assisted overground Trexo use in school and outpatient settings during a 6-week physiotherapy block. Children ages 3-6 years (n = 10; cerebral palsy or related disorder, Gross Motor Function Classification System level IV) will be recruited by circle of care invitations to participate. Study indicators/outcomes will focus on evaluation of: (i) clinical feasibility, safety, and acceptability of intervention; (ii) pre-post intervention motor/functional outcomes; (iii) pre-post intervention brain structure characterization and resting state brain connectivity; (iv) muscle activity characterization during Trexo-assisted gait and natural assisted gait; (v) heart rate during Trexo-assisted gait and natural assisted gait; and (vi) user experience and perceptions of physiotherapists, children, and parents.

Discussion

This will be the first study to investigate feasibility indicators, outcomes, and experiences of Trexo-based physiotherapy in a school and outpatient context with children who have mobility challenges. It will explore the possibility of experience-dependent neuroplasticity in the context of gait rehabilitation, as well as associated functional and muscular outcomes. Finally, the study will address important questions about clinical utility and future adoption of the device from the physiotherapists' perspective, comfort and engagement from the children's perspective, and the impressions of parents about the value of introducing this technology as an early intervention.

Clinical trial registration

https://clinicaltrials.gov, identifier NCT05463211.

A randomized cross-over study protocol to evaluate long-term gait training with a pediatric robotic exoskeleton outside the clinical setting in children with movement disorders

Individuals with neuromuscular disorders display a combination of motor control deficits and lower limb weakness contributing to knee extension deficiency characterized by exaggerated stance phase knee flexion. There is a lack of evidence for long-term improvement of knee extension deficiency with currently available clinical treatment programs. Our previous work testing a wearable robotic exoskeleton with precisely timed assistive torque applied at the knee showed immediate increases in knee extension during walking for children with cerebral palsy, which continued to improve over an acute practice period. When we applied interleaved assistance and resistance to knee extension, we observed improvements in knee extension and increased muscle activation indicating the potential for muscle strengthening when used over time. There is a need for additional, high-quality trials to assess the impact of dosage, intensity and volume of training necessary to see persistent improvement in lower limb function for these patient populations. This randomized crossover study (ClinicalTrials.gov: NCT05726591) was designed to determine whether 12 weeks of overground gait training with a robotic exoskeleton outside of the clinical setting, following an initial in clinic accommodation period, has a beneficial effect on walking ability, muscle activity and overall motor function. Participants will be randomized to either complete the exoskeleton intervention or continue their standard therapy for 12 weeks first, followed by a crossover to the other study component. The primary outcome measure is change in peak knee extension angle during walking; secondary outcome measures include gait speed, strength, and validated clinical scales of motor function and mobility. Assessments will be completed before and after the intervention and at 6 weeks post-intervention, and safety and compliance will be monitored throughout. We hypothesize that the 12-week exoskeleton intervention outside the clinical setting will show greater improvements in study outcome measures than the standard therapy.

Clinical Uptake of Pediatric Exoskeletons: Pilot Study Using the Consolidated Framework for Implementation Research

OBJECTIVE

While the design and clinical evidence base of robot-assisted gait training devices has been advancing, few studies investigate user experiences with accessing and using such devices in pediatric rehabilitation. This pilot study aims to further the understanding of barriers encountered by clinicians and caregivers when implementing a robot-assisted gait training device.

DESIGN

A qualitative descriptive study was conducted at a local outpatient pediatric therapy center with a robot-assisted gait training exoskeleton. Six caregivers and six clinicians participated in semistructured interviews with brief surveys. The surveys were summarized with descriptive statistics. The interviews were analyzed using directed content analysis guided by the Consolidated Framework for Implementation Research.

RESULTS

The five most mentioned Consolidated Framework for Implementation Research constructs were knowledge and beliefs, relative advantage, child attributes, complexity, and access to knowledge and information. Caregivers experienced obstacles to accessing and trialing robot-assisted gait training devices. Clinicians expressed concerns regarding the feasibility of incorporating robot-assisted gait training into their clinic and preferred lower-tech gait training techniques.

CONCLUSIONS

While some aspects of access and usability may be addressed by device design and technological advancements, overcoming other barriers will require a deeper understanding of the roles of scientific evidence, personal beliefs, and current therapy workflows in the uptake of robotic interventions.

Design and motion control of exoskeleton robot for paralyzed lower limb rehabilitation

Introduction

Patients suffering from limb movement disorders require more complete rehabilitation treatment, and there is a huge demand for rehabilitation exoskeleton robots. Flexible and reliable motion control of exoskeleton robots is very important for patient rehabilitation.

Methods

This paper proposes a novel exoskeleton robotic system for lower limb rehabilitation. The designed lower limb rehabilitation exoskeleton robot mechanism is mainly composed of the hip joint mechanism, the knee joint mechanism and the ankle joint mechanism. The forces and motion of the exoskeleton robot were analyzed in detail to determine its design parameters. The robot control system was developed to implement closed-loop position control and trajectory planning control of each joint mechanism.

Results

Multiple experiments and tests were carried out to verify robot's performance and practicality. In the robot angular response experiments, the joint mechanism could quickly adjust to different desired angles, including 15°, 30°, 45°, and 60°. In the trajectory tracking experiments, the exoskeleton robot could complete tracking movements of typical actions such as walking, standing up, sitting down, go upstairs and go downstairs, with a maximum tracking error of ±5°. Robotic wearing tests on normal people were performed to verify the assistive effects of the lower limb rehabilitation exoskeleton at different stages.

Discussion

The experimental results indicated that the exoskeleton robot has excellent reliability and practicality. The application of this exoskeleton robotic system will help paralyzed patients perform some daily movements and sports.

Design and validation of a pediatric gait assistance exoskeleton system with fast non-singular terminal sliding mode controller

Existing exoskeletons for pediatric gait assistance have limitations in anthropometric design, structure weight, cost, user safety features, and adaptability to diverse users. Additionally, creating precise models for pediatric rehabilitation is difficult because the rapid anthropometric changes in children result in unknown model parameters. Furthermore, external disruptions, like unpredictable movements and involuntary muscle contractions, add complexity to the control schemes that need to be managed. To overcome these limitations, this study aims to develop an affordable stand-aided lower-limb exoskeleton specifically for pediatric subjects (8-12 years, 25-40 kg, 128-132 cm) in passive-assist mode. The authors modified a previously developed model (LLESv1) for improved rigidity, reduced mass, simplified motor arrangement, variable waist size, and enhanced mobility. A computer-aided design of the new exoskeleton system (LLESv2) is presented. The developed prototype of the exoskeleton appended with a pediatric subject (age: 12 years old, body mass: 40 kg, body height: 132 cm) is presented with real-time hardware architecture. Thereafter, an improved fast non-singular terminal sliding mode (IFNSTSM) control scheme is proposed, incorporating a double exponential reaching law for expedited error convergence and enhanced stability. The Lyapunov stability warrants the control system's performance despite uncertainties and disturbances. In contrast to fast non-singular terminal sliding mode (FNSTSM) control and time-scaling sliding mode (TSSM) control, experimental validation demonstrates the effectiveness of IFNSTSM control by a respective average of 5.39% and 42.1% in tracking desired joint trajectories with minimal and rapid finite time converging errors. Moreover, the exoskeleton with the proposed IFNSTSM control requires significantly lesser control efforts than the exoskeleton using contrast FNSTSM control. The Bland-Altman analysis indicates that although there is a minimal mean difference in variables when employing FNSTSM and IFNSTSM controllers, the latter exhibits significant performance variations as the mean of variables changes. This research contributes to affordable and effective pediatric gait assistance, improving rehabilitation outcomes and enhancing mobility support.

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.

Design and Control of a Single-Leg Exoskeleton with Gravity Compensation for Children with Unilateral Cerebral Palsy

Children with cerebral palsy (CP) experience reduced quality of life due to limited mobility and independence. Recent studies have shown that lower-limb exoskeletons (LLEs) have significant potential to improve the walking ability of children with CP. However, the number of prototyped LLEs for children with CP is very limited, while no single-leg exoskeleton (SLE) has been developed specifically for children with CP. This study aims to fill this gap by designing the first size-adjustable SLE for children with CP aged 8 to 12, covering Gross Motor Function Classification System (GMFCS) levels I to IV. The exoskeleton incorporates three active joints at the hip, knee, and ankle, actuated by brushless DC motors and harmonic drive gears. Individuals with CP have higher metabolic consumption than their typically developed (TD) peers, with gravity being a significant contributing factor. To address this, the study designed a model-based gravity-compensator impedance controller for the SLE. A dynamic model of user and exoskeleton interaction based on the Euler-Lagrange formulation and following Denavit-Hartenberg rules was derived and validated in Simscape^™ and Simulink^® with remarkable precision. Additionally, a novel systematic simplification method was developed to facilitate dynamic modelling. The simulation results demonstrate that the controlled SLE can improve the walking functionality of children with CP, enabling them to follow predefined target trajectories with high accuracy.

Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications

Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5 to 1.85 m/s. Four FCNNs (a generalised-speed model, a low-speed model, a high-speed model, and a low-high-speed model) are evaluated to assess their predictive performance on gait speeds included in the training speed range and on speeds that have been excluded from it. The evaluation involves short-term (one-step-ahead) predictions and long-term (200-time-step) recursive predictions. The results show that the performance of the low- and high-speed models, measured using the mean absolute error (MAE), decreased by approximately 43.7% to 90.7% when tested on the excluded speeds. Meanwhile, when tested on the excluded medium speeds, the performance of the low-high-speed model improved by 2.8% for short-term predictions and 9.8% for long-term predictions. These findings suggest that FCNNs are capable of interpolating to speeds within the maximum and minimum training speed ranges, even if not explicitly trained on those speeds. However, their predictive performance decreases for gaits at speeds beyond or below the maximum and minimum training speed ranges.

The Roles of Trust and Its Antecedent Variables in Healthcare Consumers' Acceptance of Online Medical Consultation during the COVID-19 Pandemic in China

Online medical consultation (OMC) is generating considerable interest among researchers and practitioners due to the mandatory quarantine measures implemented during the COVID-19 pandemic in China. However, the acceptance rate of OMC has declined over time. This paper aims to empirically investigate OMC acceptance using a proposed research model by integrating the technology acceptance model (TAM) with trust and its antecedent variables. A quantitative self-administered cross-sectional survey was conducted to collect data from 260 healthcare consumers. A partial least squares structural equation modeling method was used to examine the data. Results revealed that healthcare consumers' behavioral intention was influenced by attitudes, while perceived usefulness and trust significantly influenced behavioral intention through attitude as a mediator. In addition, perceived risk, perceived privacy protection, network externalities, cognitive reputation, and interactivity directly influenced trust. Overall, the research model explained 50% of the variance in attitude and 71% of the variance in behavioral intention. The study's findings should provide useful insights into making effective design, development, and implementation decisions for OMC services.

Robot-Assisted Gait Training with Trexo Home: Users, Usage and Initial Impacts

Robotic gait training has the potential to improve secondary health conditions for people with severe neurological impairment. The purpose of this study was to describe who is using the Trexo robotic gait trainer, how much training is achieved in the home and community, and what impacts are observed after the initial month of use. In this prospective observational single-cohort study, parent-reported questionnaires were collected pre- and post-training. Of the 70 participants, the median age was 7 years (range 2 to 24), 83% had CP, and 95% did not walk for mobility. Users trained 2–5 times/week. After the initial month, families reported a significant reduction in sleep disturbance (p = 0.0066). Changes in bowel function, positive affect, and physical activity were not statistically significant. These findings suggest that families with children who have significant mobility impairments can use a robotic gait trainer frequently in a community setting and that sleep significantly improves within the first month of use. This intervention holds promise as a novel strategy to impact multi-modal impairments for this population. Future work should include an experimental study design over a longer training period to begin to understand the relationship between training volume and its full potential.

A Bibliometric Analysis of Wearable Device Research Trends 2001-2022-A Study on the Reversal of Number of Publications and Research Trends in China and the USA

In recent years, Wearable Devices have been used in a wide variety of applications and fields, but because they span so many different disciplines, it is difficult to ascertain the intellectual structure of this entire research domain. No review encompasses the whole research domain related to Wearable Devices. In this study, we collected articles on wearable devices from 2001 to 2022 and quantitatively organized them by bibliometric analysis to clarify the intellectual structure of this research domain as a whole. The cluster analysis, co-occurrence analysis, and network centrality analysis were conducted on articles collected from the Web of Science. As a result, we identified one cluster that represents applied research and two clusters that represent basic research in this research domain. Furthermore, focusing on the top two countries contributing to this research domain, China and the USA., it was confirmed that China is extremely inclined toward basic research and the USA. toward applied research, indicating that applied and basic research are in balance. The basic intellectual structure of this cross-sectional research domain was identified. The results summarize the current state of research related to Wearable Devices and provide insight into trends.

Interleaved Assistance and Resistance for Exoskeleton Mediated Gait Training: Validation, Feasibility and Effects

Strength and selective motor control are primary determinants of pathological gait in children with cerebral palsy (CP) and other neuromotor disorders. Emerging evidence suggests robotic application of task-specific resistance to functional movements may provide the opportunity to strengthen muscles and improve neuromuscular function during walking in children with CP. Such a strategy could be most beneficial to children who are more severely affected by the pathology but their ability to overcome such resistance and maintain functional ambulation remains unclear. The goal of this study was to design, validate and evaluate initial feasibility and effects of a novel exoskeleton strategy that provides interleaved assistance and resistance to knee extension during overground walking. One participant with CP (GMFCS III) was recruited and completed ten total visits, nine walking with the exoskeleton. Our results validated the controller's ability to parse the gait cycle into five discrete phases (mean accuracy 91%) and provide knee extension assistance during stance and resistance during swing. Following acclimation to the interleaved strategy, peak knee extension was significantly improved in both the left (mean 7.9 deg) and right (15.2 deg) limbs when walking with the exoskeleton. Knee extensor EMG during late swing phase increased to 2.7 (left leg) and 1.7 (right leg) times the activation level during baseline exoskeleton walking without resistance. These results indicate that this interleaved strategy warrants further investigation in a longitudinal intervention study, particularly in individuals who may be more severely affected such that they are unable to ambulate overground using an exoskeleton training strategy that only deploys targeted resistance to limb motion.