Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects

Research Status and Development Trend of Lower-Limb Squat-Assistant Wearable Devices

The accelerating population aging and increasing demand for higher work efficiency have made the research and the application of lower-limb assistive exoskeletons a primary focus in recent years. This paper reviews the research progress of lower-limb squat assistive wearable devices, with a focus on classification methods, research outcomes, and products from both domestic and international markets. It also analyzes the key technologies involved in their development, such as mechanical mechanisms, control strategies, motion sensing, and effectiveness validation. From an industrial design perspective, the paper also explores the future prospects of lower-limb squat assistive wearable devices in four key areas: multi-signal sensing, intelligent control, human-machine collaboration, and experimental validation. Finally, the paper discusses future development trends in this field.

Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human-machine-environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions.

A wearable system to assist impaired-neck patients: Design and evaluation

Patients with neurological disorders, such as amyotrophic lateral sclerosis, Parkinson's disease, and cerebral palsy, often face challenges due to head-neck immobility. The conventional treatment approach involves using a neck collar to maintain an upright head position, but this can be cumbersome and restricts head-neck movements over prolonged periods. This study introduces a wearable robot capable of providing three anatomical head motions for training and assistance. The primary contributions of this research include the design of an optimized structure and the incorporation of human-robot interaction. Based on human head motion data, our primary focus centered on developing a robot capable of accommodating a significant range of neutral head movements. To ensure safety, impedance control was employed to facilitate human-robot interaction. A human study was conducted involving 10 healthy subjects who participated in an experiment to assess the robot's assistance capabilities. Passive and active modes were used to evaluate the robot's effectiveness, taking into account head-neck movement error and muscle activity levels. Surface electromyography signals (sEMG) were collected from the splenius capitis muscles during the experiment. The results demonstrated that the robot covered nearly 85% of the overall range of head rotations. Importantly, using the robot during rehabilitation led to reduced muscle activation, highlighting its potential for assisting individuals with post-stroke movement impairments.

Modelling for design and evaluation of industrial exoskeletons: A systematic review

Industrial exoskeletons are developed to relieve workers' physical demands in the workplace and to alleviate ergonomic issues associated with work-related musculoskeletal disorders. As a safe and economical alternative to empirical/experimental methods, modelling is considered as a powerful tool for design and evaluation of industrial exoskeletons. This systematic review aims to provide a comprehensive understanding of the current literature on the design and evaluation of industrial exoskeletons through modelling. A systematic study was conducted by general keyword searches of five electronic databases over the last two decades (2003-2022). Out of the 701 records initially retrieved, 33 eligible articles were included and analyzed in the final review, presenting a variety of model inputs, model development, and model outputs used in the modelling. This systematic review study revealed that existing modelling methods can evaluate the biomechanical and physiological effects of industrial exoskeletons and provide some design parameters. However, the modelling method is currently unable to cover some of the main evaluation metrics supported by experimental assessments, such as task performance, user experience/discomfort, change in metabolic costs etc. Standard guidelines for model construction and implementation, as well as validation of human-exoskeleton interactions, remain to be established.

sEMG-Based End-to-End Continues Prediction of Human Knee Joint Angles Using the Tightly Coupled Convolutional Transformer Model

Wearable exoskeleton robots can promote the rehabilitation of patients with physical dysfunction. And improving human-computer interaction performance is a significant challenge for exoskeleton robots. The traditional feature extraction process based on surface Electromyography(sEMG) is complex and requires manual intervention, making real-time performance difficult to guarantee. In this study, we propose an end-to-end method to predict human knee joint angles based on sEMG signals using a tightly coupled convolutional transformer (TCCT) model. We first collected sEMG signals from 5 healthy subjects. Then, the envelope was extracted from the noise-removed sEMG signal and used as the input to the model. Finally, we developed the TCCT model to predict the knee joint angle after 100 ms. For the prediction performance, we used the Root Mean Square Error(RMSE), Pearson Correlation Coefficient(CC), and Adjustment R2 as metrics to evaluate the error between the actual knee angle and the predicted knee angle. The results show that the model can predict the human knee angle quickly and accurately. The mean RMSE, Adjustment R2, and (CC) values of the model are 3.79°, 0.96, and 0.98, respectively, which are better than traditional deep learning models such as Informer (4.14, 0.95, 0.98), CNN (5.56, 0.89, 0.96) and CNN-BiLSTM (3.97, 0.95, 0.98). In addition, the prediction time of our proposed model is only 11.67 ± 0.67 ms, which is less than 100 ms. Therefore, the real-time and accuracy of the model can meet the continuous prediction of human knee joint angle in practice.

Modeling the Human Gait Phases by Using Bèzier Curves to Generate Walking Trajectories for Lower-Limb Exoskeletons

The clinical usage of powered exoskeletons for the rehabilitation of patients affected by lower limb disorders has been constantly growing in the last decade. This paper presents a versatile and reliable gait pattern generator for these devices able to accommodate several gait requirements, i.e., step length, clearance, and time, and to suit a wide range of persons. In the proposed method, the human gait phases have been modeled with a set of trajectories as Bèzier curves, enabling a robotic lower-limb exoskeleton to walk in a continuous way, similarly to the physiological gait cycle. The kinematic, kinetic, and spatial requirements for each gait phase are translated into the control points of the Bèzier curves that define the trajectory for that phase. The outcome of this study has been tested on real scenarios with a group of healthy subjects wearing the TWIN lower-limb exoskeleton. They were asked to walk at different speeds, generally defined as slow, medium, and fast. The results are shown in terms of joint positions, velocities, and body-mass-normalized torques. The maximum hip and knee joint torque was observed in the support phase. While, at higher speeds the maximum hip torque was provided in the swing phase due to the mechanical properties and limits of the device. In terms of speed, all the subjects reached 0.44 m/s, which is the minimum required community ambulation.

Investigation with able-bodied subjects suggests Myosuit may potentially serve as a stair ascent training robot

Real world settings are seldomly just composed of level surfaces and stairs are frequently encountered in daily life. Unfortunately, ~ 90% of the elderly population use some sort of compensation pattern in order to negotiate stairs. Because the biomechanics required to successfully ascend stairs is significantly different from level walking, an independent training protocol is warranted. Here, we present as a preliminary investigation with 11 able-bodied subjects, prior to clinical trials, whether Myosuit could potentially serve as a stair ascent training robot. Myosuit is a soft wearable exosuit that was designed to assist the user via hip and knee extension during the early stance phase. We hypothesized that clinical studies could be carried out if the lower limb kinematics, sensory feedback via plantar force, and electromyography (EMG) patterns do not deviate from the user's physiological stair ascent patterns while reducing hip and knee extensor demand. Our results suggest that Myosuit conserves the user's physiological kinematic and plantar force patterns. Moreover, we observe approximately 20% and 30% decrease in gluteus maximus and vastus medialis EMG levels in the pull up phase, respectively. Collectively, Myosuit reduces the hip and knee extensor demand during stair ascent without any introduction of significant compensation patterns.

Neurorehabilitation robotics: how much control should therapists have?

Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.

Feedback-Error Learning for time-effective gait trajectory tracking in wearable exoskeletons

The use of exoskeletons in gait rehabilitation implies user-oriented and efficient responses of exoskeletons' controllers with adaptability for human-robot interaction. This study investigates the performance of a bioinspired hybrid control, the Feedback-Error Learning (FEL) controller, to time-effectively track user-oriented gait trajectories and adapt the exoskeletons' response to dynamic changes due to the interaction with the user. It innovates with a controller benchmarking analysis. FEL combines a proportional-integral-derivative (PID) feedback controller with a three-layer neural network feedforward controller that learns the inverse dynamics of the exoskeleton based on real-time feedback commands. FEL validation involved able-bodied subjects walking with knee and ankle exoskeletons at different gait speeds while considering gait disturbances. Results showed that the FEL control accurately (tracking error <7%) and timely (delay <30 ms) tracked gait trajectories. The feedforward controller learned the inverse dynamics of the exoskeletons in a time compliant for clinical use and adapted to variations in the gait trajectories, such as speed and position range, while the feedback controller compensated for random disturbances. FEL was more accurate and time-effective controller for tracking gait trajectories than a PID control (error <27%, delay <260 ms) and a lookup table feedforward combined with PID control (error <17%, delay >160 ms). These findings aligned with FEL's time-effectiveness favors its use in wearable exoskeletons for repetitive gait training.

Planar Model for Vibration Analysis of Cable Rehabilitation Robots

Cable robots are widely used in the field of rehabilitation. These robots differ from other cable robots because the cables are rather short and are usually equipped with magnetic hooks to improve the ease of use. The vibrations of rehabilitation robots are dominated by the effects of the hooks and payloads, whereas the cables behave as massless springs. In this paper, a 2D model of the cables of a robot that simulates both longitudinal and transverse vibrations is developed and experimentally validated. Then the model is extended to simulate the vibrations of an actual 3D robot in the symmetry planes. Finally, the calculated modal properties (natural frequencies and modes of vibration) are compared with the typical spectrum of excitation due to the cable’s motion. Only the first transverse mode can be excited during the rehabilitation exercise.

Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach

Joint dynamic properties play essential roles in a wide range of biomechanical movement control. This paper develops a device with a novel mechatronic design to apply small-amplitude perturbations to the human knee. Surface Electromyography is employed to record such information; at the same time, force and position sensors collect measurements to be sent to identify human joint dynamics. For classification and estimation of force, support vector machine and support vector regression techniques are applied, respectively. We devise a genetic algorithm for parameter optimization and feature extraction within the proposed methods to improve the estimation accuracy. These are then analyzed and compared to the output of our estimation model to provide a reliable comparison. Our extensive experimental results reveal a high estimation accuracy for lower limb muscles to regulate robot impedance parameters. Although the identification method sounds similar to traditional ones, knee joint properties can be estimated by the machine learning approach from the surface Electromyography without perturbations.

Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ > 0.85) between target and estimation (R¯2 = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.

Biomechanical Analysis Suggests Myosuit Reduces Knee Extensor Demand during Level and Incline Gait

An FDA-approved soft wearable robot, the Myosuit, which was designed to provide hip and knee extension torque has recently been commercialized. While studies have reported reductions in metabolic costs, increased gait speeds, and improvements in clinical test scores, a comprehensive analysis of electromyography (EMG) signals and joint kinematics is warranted because the recruitment of appropriate muscle groups during physiological movement patterns facilitates effective motor learning. Here, we compared the lower limb joint kinematics and EMG patterns while wearing the Myosuit with that of unassisted conditions when performing level overground and incline treadmill gait. The level overground gait sessions (seven healthy subjects) were performed at self-selected speeds and the incline treadmill gait sessions (four healthy subjects) were performed at 2, 3, 4, and 5 km/h. In order to evaluate how the user is assisted, we conducted a biomechanical analysis according to the three major gait tasks: weight acceptance (WA), single-limb support, and limb advancement. The results from the gait sessions suggest that Myosuit not only well preserves the users' natural patterns, but more importantly reduce knee extensor demand during the WA phase for both level and incline gait.

Rehabilitation robotics after stroke: a bibliometric literature review

INTRODUCTION

Stroke is the leading cause of long-term disability in developed countries. Due to population aging, the number of people requiring rehabilitation after stroke is going to rise in the coming decades. Robot-mediated neurorehabilitation has the potential to improve clinical outcomes of rehabilitation treatments. A statistical analysis of the literature aims to focus on the main trend of this topic.

AREAS COVERED

A bibliometric survey on post-stroke robotic rehabilitation was performed through a database collection of scientific publications in the field of rehabilitation robotics. By covering the last 20 years, 17429 sources were collected. Relevant patterns and statistics concerning the main research areas were analyzed. Leading journals and conferences which publish and disseminate knowledge in the field were identified. A detailed nomenclature study was carried out. The time trends of the research field were captured. Opinions and predictions of future trends that are expected to shape the near future of the field were discussed.

EXPERT OPINION

Data analysis reveals the continuous expansion of the research field over the last two decades, which is expected to rise considerably in near future. More attention will be paid to the lower limbs rehabilitation and disease/design specific applications in early-stage patients.

Non-age-related gait kinematics and kinetics in the elderly

BACKGROUND

The change of gait kinematics and kinetics along aging were reported to indicate age-related gait patterns. However, few studies focus on non-age-related gait analysis. This study aims to explore the non-age-related gait kinematics and kinetics by comparing gait analysis outcomes among the healthy elderly and young subjects.

METHODS

Gait analysis at self-paced was conducted on 12 healthy young subjects and 8 healthy elderly subjects. Kinematic and kinetic features of ankle, knee and hip joints were analyzed and compared in two groups. The degree of variation between the young and elderly in each kinematic or kinetic feature was calculated from pattern distance and percentage of significant difference. The k-means clustering and Elbow Method were applied to select and validate non-age-related features. The average waveforms with standard deviation were plotted for the comparison of the results.

RESULTS

A total of five kinematic and five kinetic features were analyzed on ankle, knee and hip joints in healthy young and elderly groups. The degrees of variation in ankle moment, knee angle, hip flexion angle, and hip adduction moment were 0.1074, 0.1593, 0.1407, and 0.1593, respectively. The turning point was where the k value equals two. The clustering centers were 0.1417 and 0.3691, and the two critical values closest to the cutoff were 0.1593 and 0.3037. The average waveforms of the kinematic or kinetic features mentioned above were highly overlapped with a minor standard deviation between the healthy young and elderly but showed larger variations between the healthy and abnormal.

CONCLUSIONS

The cluster with a minor degree of variation in kinematic and kinetic features between the young and elderly were identified as non-age-related, including ankle moment, knee angle, hip flexion angle, and hip adduction moment. Non-age-related gait kinematics and kinetics are essential indicators for gait with normal function, which is essential in the evaluation of mobility and functional ability of the elderly, and data fusion of the assistant device.

Effects of Robot-Assisted Gait Training with Body Weight Support on Gait and Balance in Stroke Patients

This study investigated the effects of robot-assisted gait training with body weight support on gait and balance in stroke patients. The study participants comprised 24 patients diagnosed with stroke. Patients were randomly assigned to four groups of six: robot A, B, C, and non-robot. The body weight support (BWS) for the harness of the robot was set to 30% of the patient’s body weight in robot group A, 50% in robot group B, and 70% in robot group C. All experimental groups received robot-assisted gait training and general physical therapy. The non-robot group underwent gait training using a p-bar, a treadmill, and general physical therapy. The intervention was performed for 30 min a day, five times a week, for 6 weeks. All participants received the intervention after the pre-test. A post-test was performed after all of the interventions were completed. Gait was measured using a 10 m Walking test (10MWT) and the timed up and go (TUG) test. Balance was assessed using the Berg Balance Scale (BBS). Robot groups A, B, and C showed significantly better 10MWT results than did the non-robot group (p < 0.5). TUG was significantly shorter in robot groups A, B, and C than in the non-robot group (p < 0.5). The BBS scores for robot group A improved significantly more than did those for robot groups B and C and the non-robot group (p < 0.5), indicating that robot-assisted gait training with body weight support effectively improved the gait of stroke patients.

A Robot-Assisted Therapy to Increase Muscle Strength in Hemiplegic Gait Rehabilitation

This study examines the feasibility of using a robot-assisted therapy methodology based on the Bobath concept to perform exercises applied in conventional therapy for gait rehabilitation in stroke patients. The aim of the therapy is to improve postural control and movement through exercises based on repetitive active-assisted joint mobilization, which is expected to produce strength changes in the lower limbs. As therapy progresses, robotic assistance is gradually reduced and the patient's burden increases with the goal of achieving a certain degree of independence. The relationship between force and range of motion led to the analysis of both parameters of interest. The study included 23 volunteers who performed 24 sessions, 2 sessions per week for 12 weeks, each lasting about 1 h. The results showed a significant increase in hip abduction and knee flexion strength on both sides, although there was a general trend of increased strength in all joints. However, the range of motion at the hip and ankle joints was reduced. The usefulness of this platform for transferring exercises from conventional to robot-assisted therapies was demonstrated, as well as the benefits that can be obtained in muscle strength training. However, it is suggested to complement the applied therapy with exercises for the maintenance and improvement of the range of motion.

Comparative effects of passive and active mode robot-assisted gait training on brain and muscular activities in sub-acute and chronic stroke

BACKGROUND

Robot-assisted gait training (RAGT) was initially developed based on the passive controlled (PC) mode, where the target or ideal locomotor kinematic trajectory is predefined and a patient basically 'rides' the robot instead of actively participating in the actual locomotor relearning process. A new insightful contemporary neuroscience and mechatronic evidence suggest that robotic-based locomotor relearning can be best achieved through active interactive (AI) mode rather than PC mode.

OBJECTIVE

The purpose of this study was to compare the pattern of gait-related cortical activity, specifically gait event-related spectral perturbations (ERSPs), and muscle activity from the tibialis anterior (TA) and clinical functional tests in subacute and chronic stroke patients during robot-assisted gait training (RAGT) in passive controlled (PC) and active interactive (AI) modes.

METHODS

The present study involves a two-group pretest-posttest design in which two groups (i.e., PC-RAGT group and AI-RAGT group) of 14 stroke subjects were measured to assess changes in ERSPs, the muscle activation of TA, and the clinical functional tests, following 15- 18 sessions of intervention according to the protocol of each group.

RESULTS

Our preliminary results demonstrated that the power in the μ band (8- 12 Hz) was increased in the leg area of sensorimotor cortex (SMC) and supplementary motor area (SMA) at post-intervention as compared to pre-intervention in both groups. Such cortical neuroplasticity change was associated with TA muscle activity during gait and functional independence in functional ambulation category (FAC) and motor coordination in Fugl- Meyer Assessment for lower extremity (FMA-LE) test as well as spasticity in the modified Ashworth scale (MAS) measures.

CONCLUSIONS

We have first developed a novel neuroimaging experimental paradigm which distinguished gait event related cortical involvement between pre- and post-intervention with PC-RAGT and AI-RAGT in individuals with subacute and chronic hemiparetic stroke.

Exoskeleton robots for lower limb assistance: A review of materials, actuation, and manufacturing methods

The field of robot-assisted physical rehabilitation and robotics technology for providing support to the elderly population is rapidly evolving. Lower limb robot aided rehabilitation and assistive technology have been a focus for the engineering community during the last three decades as several robotic lower limb exoskeletons have been proposed in the literature as well as some being commercially available. Numerous manufacturing techniques and materials have been developed for lower limb exoskeletons during the last two decades, resulting in the design of a variety of robot exoskeletons for gait assistance for elderly and disabled people. One of the most important aspects of developing exoskeletons is the selection of the most appropriate proper material. The material selection strongly influences the overall weight and performance of the exoskeleton robot. The most suitable fabrication method for material is also an important parameter for the development of lower limb robot exoskeletons. In addition to the materials and manufacturing methods, the actuation method plays a vital role in the development of these robot exoskeletons. Even though various materials, manufacturing methods and actuators are reported in the literature for these lower limb robot exoskeletons, there are still avenues of improvement in these three domains. In this review, we have examined various lower limb robotic exoskeletons, concentrating on the three main aspects of material, manufacturing, and actuation. We have focused on the advantages and drawbacks of various materials and manufacturing practices as well as actuation methods. A discussion on future directions of research is provided for the engineering community covering the material, manufacturing and actuation methods.

The Kickstart Walk Assist System for improving balance and walking function in stroke survivors: a feasibility study

BACKGROUND

Compared with traditional physical therapy for stroke patients, lower extremity exoskeletons can provide patients with greater endurance and more repeatable and controllable training, which can reduce the therapeutic burden of the therapist. However, most exoskeletons are expensive, heavy or require active power to be operated. Therefore, a lighter, easy to wear, easy to operate, low-cost technology for stroke rehabilitation would be a welcome opportunity for stroke survivors, caregivers and clinicians. One such device is the Kickstart Walk Assist system and the purpose of this study was to determine feasibility of using this unpowered exoskeleton device in a sample of stroke survivors.

METHODS

Thirty stroke survivors were enrolled in the study and experienced walking with the Kickstart exoskeleton device that provided spring-loaded assistance during gait. After 5 days of wearing the exoskeleton, participants were evaluated in the two states of wearing and not wearing the exoskeleton. Outcome measures included: (a) spatio-temporal gait measures, (b) balance measures and (c) exoskeleton-use feedback questionnaire.

RESULTS

In comparison to not wearing the device, when participants wore the Kickstart walking system, weight bearing asymmetry was reduced. The time spent on the 10-m walk test was also reduced, but there was no difference in the timed-up-and-go test (TUGT). Gait analysis data showed reduction in step time and double support time. Stroke survivors were positive about the Kickstart walking system's ability to improve their balance, speed and gait. In addition, their confidence level and willingness to use the device was also positive.

CONCLUSIONS

These findings show the feasibility of using the Kickstart walking system for improving walking performance in stroke survivors. Our future goal is to perform a longer duration study with more comprehensive pre- and post-testing in a larger sample of stroke survivors. Trial registration Chinese Clinical Trial Registry, ChiCTR2000032665. Registered 5 May 2020-Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=53288.

Lower limb rehabilitation robotics: The current understanding and technology

BACKGROUND

With the increasing rate of ambulatory disabilities and rise in the elderly population, advance methods to deliver the rehabilitation and assistive services to patients have become important. Lower limb robotic therapeutic and assistive aids have been found to improve the rehabilitation outcome.

OBJECTIVE

The article aims to present the updated understanding in the field of lower limb rehabilitation robotics and identify future research avenues.

METHODS

Groups of keywords relating to assistive technology, rehabilitation robotics, and lower limb were combined and searched in EMBASE, IEEE Xplore Digital Library, Scopus, Web of Science and Google Scholar database.

RESULTS

Based on the literature collected from the databases we provide an overview of the understanding of robotics in rehabilitation and state of the art devices for lower limb rehabilitation. Technological advancements in rehabilitation robotic architecture (sensing, actuation and control) and biomechanical considerations in design have been discussed. Finally, a discussion on the major advances, research directions, and challenges is presented.

CONCLUSIONS

Although the use of robotics has shown a promising approach to rehabilitation and reducing the burden on caregivers, extensive and innovative research is still required in both cognitive and physical human-robot interaction to achieve treatment efficacy and efficiency.

A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.

Assessment of an Assistive Control Approach Applied in an Active Knee Orthosis Plus Walker for Post-Stroke Gait Rehabilitation

The goal of this study is the assessment of an assistive control approach applied to an active knee orthosis plus a walker for gait rehabilitation. The study evaluates post-stroke patients and healthy subjects (control group) in terms of kinematics, kinetics, and muscle activity. Muscle and gait information of interest were acquired from their lower limbs and trunk, and a comparison was conducted between patients and control group. Signals from plantar pressure, gait phase, and knee angle and torque were acquired during gait, which allowed us to verify that the stance control strategy proposed here was efficient at improving the patients’ gaits (comparing their results to the control group), without the necessity of imposing a fixed knee trajectory. An innovative evaluation of trunk muscles related to the maintenance of dynamic postural equilibrium during gait assisted by our active knee orthosis plus walker was also conducted through inertial sensors. An increase in gait cycle (stance phase) was also observed when comparing the results of this study to our previous work. Regarding the kinematics, the maximum knee torque was lower for patients when compared to the control group, which implies that our orthosis did not demand from the patients a knee torque greater than that for healthy subjects. Through surface electromyography (sEMG) analysis, a significant reduction in trunk muscle activation and fatigability, before and during the use of our orthosis by patients, was also observed. This suggest that our orthosis, together with the assistive control approach proposed here, is promising and could be considered to complement post-stroke patient gait rehabilitation.

A New Trajectory Determination Method for Robot-Assisted Ankle Ligament Rehabilitation

Keeping ligament strain at an appropriate range is beneficial for avoiding unexpected injuries and enhancing treatment efficacy. This study proposes a new trajectory determination method specifically for the robot-assisted ankle ligament rehabilitation. The input of this method is a set of strain constraints of certain ligaments and the output is the detailed training trajectory. Simulations were conducted with two cases (one-ligament injury and three-ligaments injury). While this method has not been experimentally tested, on condition of an accurate ligament kinematics assessment, ligament strain can be guaranteed to be within the specified range following the derived trajectory. This method can help design injury-specific treatment protocols and has potential in improving the effectiveness of robot-assisted ankle rehabilitation. Future work will verify the validity and the practicality, and consider the improvement of the method.

Effects of robot-assisted gait training alongside conventional therapy on the development of walking in children with cerebral palsy

PURPOSE

To investigate the effects of robot-assisted gait training (RAGT) alongside conventional therapy on the standing and walking abilities of children with cerebral palsy (CP).

METHODS

The study sample consisted of children (aged 4-18 years) with CP whose gross motor function classification system (GMFCS) was at levels I-V. In total, 75 children with CP were evaluated and 38 patients completed the study. Patients were divided into two groups as GMFCS levels I-III (Group 1) and levels IV-V (Group 2). RAGT (30 min/session) and conventional physiotherapy (30 min/session) were applied together in the treatment. The treatment duration was 60 min per session, 3 or 4 sessions per week, for a total of 30 sessions over 8-10 weeks. 10-meter walk test (10MWT), 6-min walk test (6MinWT), gross motor functional measurement 66 (GMFM66) -D, and -E tests were performed.

RESULTS

We showed that in both groups of CP patients (mild-moderate and severe), meaningful improvements were seen in the standing (D) and walking (E) sections of GMFM-66 after treatment. When we compared the post-treatment changes in 10-m walk test, 6-min walk test, GMFM66-D, and -E between Groups 1 and 2, we noted that the improvements were statistically significant in favor of Group 1 (p< 0.01).

CONCLUSION

RAGT in combination with a conventional treatment program was significantly associated with improvements in the standing and walking abilities of children with mild to moderate CP (GMFCS levels I-III).

Academic Review and Perspectives on Robotic Exoskeletons

Since the first robotic exoskeleton was developed in 1960, this research field has attracted much interest from both the academic and industrial communities resulting in scientific publications, prototype developments and commercialized products. In this article, to document the progress in and current status of this field, we performed a bibliometric analysis. This analysis evaluated the publications in the field of robotic exoskeletons from 1990 to July 2019 that were retrieved from the Science Citation Index Expanded database. The bibliometric analyses were presented in terms of author keywords, year, country, institution, journal, author, and the citation. Results show that currently the United States has taken the leading position in this field and has built the largest collaborative network with other countries. The Massachusetts Institute of Technology (MIT) made the greatest contribution to the field of robotic exoskeleton investigations in terms of the number of publications, average citations per publication and the h-index. In addition, the Journal of NeuroEngineering and Rehabilitation ranks first among the top 20 academic journals in terms of the number of publications related to robotic exoskeletons during the period investigated. Author keyword analysis indicates that most research has focused on rehabilitation robotics. Biomedical engineering, rehabilitation and the neurosciences are the most common disciplines conducting research in this area according to the Web of Science (WoS). Our study comprehensively assesses the current research status and collaboration network of robotic exoskeletons, thus helping researchers steer their projects or locate potential collaborators.

Study on motion performance of robot-aided passive rehabilitation exercises using novel dynamic motion planning strategy

The motion rehabilitation training robot is developed to help patients with motion dysfunction recover their motor function by providing a large amount of repetitive robot-aided exercise. To achieve stable and smooth robot-aided exercises for stroke patients, a motion control method with a novel dynamic motion planning strategy is proposed. The physical state of the training limb is assessed real time during the rehabilitation exercises. The dynamic motion planning strategy is developed by employing a suitable interpolation method dynamically corresponding to the physical state of the training limb to plan a trajectory tracking system that completely utilizes different interpolation characteristics to manage the movement in accordance with the time-varying physical state of the training limb. Concurrently, a position-based impedance control is adopted to achieve compliant movement. Functional (quantitative and qualitative) and clinical experiments are conducted on a four-degree-of-freedom whole-arm manipulator upper limb rehabilitation robot to verify the effectiveness of the control method designed with the dynamic motion planning strategy. The results indicate that the proposed control strategy can exhibit better performances in terms of the stability and smoothness.

A review of cable-driven rehabilitation devices

Purpose: To conduct a survey on the research and development of cable-driven rehabilitation devices (CDRDs). Method: This review searches in the databases of PubMed, IEEE Xplore Digital Library, Science Direct, and Google Scholar using various combinations of the following keywords: cable, wire, rehabilitation, assistance, therapy, training, robot, elastic, and pneumatic. Searches in the above databases for references cited by the above-searched references are also conducted to include a larger context of CDRDs. Results: CDRDs are classified into four categories, namely, serial exoskeleton-based, parallel exoskeleton-based, serial end-effector-based, and parallel end-effector-based CDRDs. Each category of CDRDs are further grouped based on the part of the human body to be rehabilitated. All four categories of CDRDs are examined and compared and their advantages and shortcomings are discussed based on popular rehabilitation requirements on weight, adaptability, versatility, misalignment, and safety. Open issues of CDRDs are also discussed.Conclusions: Each category of CDRDs has its own advantages and shortcomings. The selection of a CDRD highly depends on the specific application. Regarding the convenience of setting up a CDRD for rehabilitation, parallel CDRDs usually have better adaptability than serial ones. However, uncertainties come with parallel CDRDs, which makes the control of parallel CDRDs more challenging. Moreover, the strategy of inherent safety has a great potential to further improve the safety of CDRDs.Implications for rehabilitationCDRDs (and general RRDs) can deliver high-intensity training while therapists usually cannot. With up-to-date human-robot interaction techniques (e.g., virtual reality), CDRDs are more interesting and motivating to trainees than conventional manual rehabilitation therapies. CDRDs also provide financial benefits in the long-run. Currently existing RRDs available for clinical practice are mainly designed for the rehabilitation of shoulders, elbows, and knees. Parallel exoskeleton-based CDRDs can also be used for the rehabilitation of many other parts of trainees. Thus, CDRDs extend the coverage of RRDs in rehabilitation. Owing to their simple structures and light weights, CDRDs can be portable and used for rehabilitation at home. In this way, CDRDs can improve the duration and intensity of rehabilitation for those with limited access to rehabilitation institutes. As well known, the higher intensity of training leads to a higher rate of recovery.

Development of KIINCE: A kinetic feedback-based robotic environment for study of neuromuscular coordination and rehabilitation of human standing and walking

Introduction

The objective of this article is to introduce the robotic platform KIINCE and its emphasis on the potential of kinetic objectives for studying and training human walking and standing. The device is motivated by the need to characterize and train lower limb muscle coordination to address balance deficits in impaired walking and standing.

Methods

The device measures the forces between the user and his or her environment, particularly the force of the ground on the feet (F) that reflects lower limb joint torque coordination. In an environment that allows for exploration of the user’s capabilities, various forms of real-time feedback guide neural training to produce F appropriate for remaining upright. Control of the foot plate motion is configurable and may be user driven or prescribed. Design choices are motivated from theory of motor control and learning as well as empirical observations of F during walking and standing.

Results

Preliminary studies of impaired individuals demonstrate the feasibility and potential utility of patient interaction with kinetic immersive interface for neuromuscular coordination enhancement.

Conclusion

Applications include study and rehabilitation of standing and walking after injury, amputation, and neurological insult, with an initial focus on stroke discussed here.

Human–robot interactive control based on reinforcement learning for gait rehabilitation training robot

A human–robot interactive control is proposed to govern the assistance provided by a lower limb exoskeleton robot to patients in the gait rehabilitation training. The rehabilitation training robot with two lower limb exoskeletons is driven by the pneumatic proportional servo system and has two rotational degrees of freedom of each lower limb. An adaptive admittance model is adopted considering its suitability for human–robot interaction. The adaptive law of the admittance parameters is designed with Sigmoid function and the reinforcement learning algorithm. Individualized admittance parameters suitable for patients are obtained by reinforcement learning. Experiments in passive and active rehabilitation training modes were carried out to verify the proposed control method. The passive rehabilitation training experimental results verify the effectiveness of the inner-loop position control strategy, which can meet the demands of gait tracking accuracy in rehabilitation training. The active rehabilitation training experimental results demonstrate that the personal adaption and active compliance are provided by the interactive controller in the robot-assistance for patients. The combined effects of flexibility of pneumatic actuators and compliance provided by the controller contribute to the training comfort, safety, and therapeutic outcome in the gait rehabilitation.

An Adaptive and Hybrid End-Point/Joint Impedance Controller for Lower Limb Exoskeletons

Assist-as-needed (AAN) algorithms for the control of lower extremity rehabilitation robots can promote active participation of patients during training while adapting to their individual performances and impairments. The implementation of such controllers requires the adaptation of a control parameter (often the robot impedance) based on a performance (or error) metric. The choice of how an adaptive impedance controller is formulated implies different challenges and possibilities for controlling the patient's leg movement. In this paper, we analyze the characteristics and limitations of controllers defined in two commonly used formulations: joint and end-point space, exploring especially the implementation of an AAN algorithm. We propose then, as a proof-of-concept, an AAN impedance controller that combines the strengths of working in both spaces: a hybrid joint/end-point impedance controller. This approach gives the possibility to adapt the end-point stiffness in magnitude and direction in order to provide a support that targets the kinematic deviations of the end-point with the appropriate force vector. This controller was implemented on a two-link rehabilitation robot for gait training-the Lokomat®Pro V5 (Hocoma AG, Switzerland) and tested on 5 able-bodied subjects and 1 subject with Spinal Cord Injury. Our experiments show that the hybrid controller is a feasible approach for exoskeleton devices and that it could exploit the benefits of the end-point controller in shaping a desired end-point stiffness and those of the joint controller to promote the correct angular changes in the trajectories of the joints. The adaptation algorithm is able to adapt the end-point stiffness based on the subject's performance in different gait phases, i.e., the robot can render a higher stiffness selectively in the direction and gait phases where the subjects perform with larger kinematic errors. The proposed approach can potentially be generalized to other robotic applications for rehabilitation or assistive purposes.

Influence of the amount of body weight support on lower limb joints' kinematics during treadmill walking at different gait speeds: Reference data on healthy adults to define trajectories for robot assistance

Several robotic devices have been developed for the rehabilitation of treadmill walking in patients with movement disorders due to injuries or diseases of the central nervous system. These robots induce coordinated multi-joint movements aimed at reproducing the physiological walking or stepping patterns. Control strategies developed for robotic locomotor training need a set of predefined lower limb joint angular trajectories as reference input for the control algorithm. Such trajectories are typically taken from normative database of overground unassisted walking. However, it has been demonstrated that gait speed and the amount of body weight support significantly influence joint trajectories during walking. Moreover, both the speed and the level of body weight support must be individually adjusted according to the rehabilitation phase and the residual locomotor abilities of the patient. In this work, 10 healthy participants (age range: 23-48 years) were asked to walk in movement analysis laboratory on a treadmill at five different speeds and four different levels of body weight support; besides, a trial with full body weight support, that is, with the subject suspended on air, was performed at two different cadences. The results confirm that lower limb kinematics during walking is affected by gait speed and by the amount of body weight support, and that on-air stepping is radically different from treadmill walking. Importantly, the results provide normative data in a numerical form to be used as reference trajectories for controlling robot-assisted body weight support walking training. An electronic addendum is provided to easily access to such reference data for different combinations of gait speeds and body weight support levels.

User-Adaptive Assistance of Assistive Knee Braces for Gait Rehabilitation

Patients suffering from neurological and orthopedic diseases or injuries usually have mobility impairment problems, and they require customized rehabilitation training to recover. In recent years, robotic assistive devices have been widely studied for gait rehabilitation. In this paper, methods to determine user-adaptive assistance of assistive knee braces (AKBs) in gait rehabilitation are investigated. A fuzzy expert system, which takes a patient's physical condition and gait analysis results as inputs, is proposed to configure suitable levels of different assistive functions of the AKB. During gait rehabilitation, the AKB generates a reference knee trajectory according to the patient's individual gait pattern, and the interaction force is controlled through a hybrid impedance controller considering the individual assistive function configuration. The proposed methods are verified through clinical pilot studies of a patient with lower limb weakness. Experimental results show that AKB with the proposed control strategies can provide effective assistance to improve the patient's gait performance during gait rehabilitation.

Dynamic Balance Gait for Walking Assistance Exoskeleton

Purpose

Powered lower-limb exoskeleton has gained considerable interests, since it can help patients with spinal cord injury(SCI) to stand and walk again. Providing walking assistance with SCI patients, most exoskeletons are designed to follow predefined gait trajectories, which makes the patient walk unnaturally and feels uncomfortable. Furthermore, exoskeletons with predefined gait trajectories cannot always maintain balance walking especially when encountering disturbances.

Design/Methodology/Approach

This paper proposed a novel gait planning approach, which aims to provide reliable and balance gait during walking assistance. In this approach, we model the exoskeleton and patient together as a linear inverted pendulum (LIP) and obtain the patients intention through orbital energy diagram. To achieve dynamic gait planning of exoskeleton, the dynamic movement primitive (DMP) is utilized to model the gait trajectory. Meanwhile, the parameters of DMP are updated dynamically during one step, which aims to improve the ability of counteracting external disturbance.

Findings

The proposed approach is validated in a human-exoskeleton simulation platform, and the experimental results show the effectiveness and advantages of the proposed approach.

Originality/Value

We decomposed the issue of obtain dynamic balance gait into three parts: (1) based on the sensory information of exoskeleton, the intention estimator is designed to estimate the intention of taking a step; (2) at the beginning of each step, the discrete gait planner utilized the obtained gait parameters such as step length S and step duration T and generate the trajectory of swing foot based on (S, T); (3) during walking process, continuous gait regulator is utilized to adjust the gait generated by discrete gait planner to counteract disturbance.

A narrative review of gait training after stroke and a proposal for developing a novel gait training device that provides minimal assistance

BACKGROUND

Gait impairment is common in stroke survivors. Recovery of walking ability is one of the most pressing objectives in stroke rehabilitation.

OBJECTIVES

Of this report are to briefly review recent progress in gait training after stroke including the use of partial body weight-supported treadmill training (PBWSTT) and robot-assisted step training (RAST), and propose a minimal assistance strategy that may overcome some of limitations of current RAST.

METHODS

The literature review emphasizes a dilemma that recent randomized clinical trials did not support the use of RAST. The unsatisfactory results of current RAST clinical trials may be partially due to a lack of careful analysis of movement deficiencies and their relevance to gait training task specificity after stroke. Normal movement pattern is implied to be part of task specificity in the current RAST. Limitations of such task specificity are analyzed.

RESULTS

Based on the review, we redefine an alternative set of gait training task specificity that represents a minimal assistance strategy in terms of assisted body movements and amount of assistance. Specifically, assistances are applied only to hip flexion and ankle dorsiflexion of the affected lower limb during swing phase. Furthermore, we propose a conceptual design of a novel device that may overcome limitations of current RAST in gait training after stroke. The novel device uses a pulling cable, either manually operated by a therapist or automated by a servomotor, to provide assistive forces to help hip flexion and ankle dorsiflexion of the affected lower limb during gait training.

CONCLUSION

The proposed minimal assistance strategy may help to design better devices for gait or other motor training.

A review in gait rehabilitation devices and applied control techniques

PURPOSE

The aim of this review is to analyse the different existing technologies for gait rehabilitation, focusing mainly in robotic devices. Those robots help the patient to recover a lost function due to neurological gait disorders, accidents or after injury. Besides, they facilitate the identification of normal and abnormal features by registering muscle activity providing the doctor important data where he can observe the evolution of the patient.

METHOD

A deep literature review was realized using selected keywords considering not only the most common medical and engineering databases, but also other available sources that provide information on commercial and scientific gait rehabilitation devices. The founded literature for this review corresponds to control techniques for gait rehabilitation robots, since the early seventies to the present year.

RESULTS

Different control strategies for gait analysis in rehabilitation devices have been developed and implemented such as position control, force and impedance control, haptic simulation, and control of EMG signals. These control techniques are used to analyze the force of the patient during therapy, compensating it with the force generated by the mechanism in the rehabilitation device. It is observed that the largest number of studies reported, focuses on the impedance control technique. Leading to include new control techniques and validate them using the necessary protocols with ill patients, obtaining reliable results that allows a progressive and active rehabilitation.

CONCLUSIONS

With this exhaustive review, we can conclude that the degree of complexity of the rehabilitation device influences in short and long-term therapeutic results since the movements become more controlled. However, there is still a lot of work in the sense of motion control in order to perform trajectories that are more alike the natural movements of humans. There are many control techniques in other areas, which seek to improve the performance of the process. These techniques may possibly be applicable in gait rehabilitation devices, obtaining controllers that are more efficient and that adapts to different people and the necessities that entail every disease. Implications for Rehabilitation Rehabilitation helps people to improve the activities of their daily life, allowing them to observe their progress in the functional abilities as the months pass by with intensive and repetitive therapies. There is a mobility issue when the patient needs to move to the hospital or to the laboratory, which is not always feasible. For overcoming it, patients use the equipment at home to perform their daily therapy. However, they need the sufficient knowledge about its operation, also about the therapeutic movements, the therapy duration and the movement speed. Besides, is necessary to place the equipment in a proper and lively environment that helps to forget or reduce pain while the patient moves his joints progressively. The purpose of robotic rehabilitation devices is to generate repetitive and progressive movements, according to the motor disability. There are training trajectories to follow, which motivate patients to generate active movements. The benefits of robotic rehabilitation depend on the ability of each patient to adapt to the speed and load variations generated by the device, improving and reinforcing motor functions in therapy, especially in patients with advanced disabilities in early rehabilitation. Multi-joint rehabilitation devices are more effective than single-joint rehabilitation devices because they involve a higher number of muscles in the therapy. The greater the number of degrees of freedom (DoF) of the device, it cushions its effect in the patient because the inertia is reduced and higher torques are generated. The assistive technological devices allows to explore different rehabilitation techniques that motivate the patient in therapy, increasing appropriately the energy and pressure in the blood which is reflected in gradually recovering his ability to walk.

Towards Optimal Platform-Based Robot Design for Ankle Rehabilitation: The State of the Art and Future Prospects

This review aims to compare existing robot-assisted ankle rehabilitation techniques in terms of robot design. Included studies mainly consist of selected papers in two published reviews involving a variety of robot-assisted ankle rehabilitation techniques. A free search was also made in Google Scholar and Scopus by using keywords “ankle^∗,” and “robot^∗,” and (“rehabilitat^∗” or “treat^∗”). The search is limited to English-language articles published between January 1980 and September 2016. Results show that existing robot-assisted ankle rehabilitation techniques can be classified into wearable exoskeleton and platform-based devices. Platform-based devices are mostly developed for the treatment of a variety of ankle musculoskeletal and neurological injuries, while wearable ones focus more on ankle-related gait training. In terms of robot design, comparative analysis indicates that an ideal ankle rehabilitation robot should have aligned rotation center as the ankle joint, appropriate workspace, and actuation torque, no matter how many degrees of freedom (DOFs) it has. Single-DOF ankle robots are mostly developed for specific applications, while multi-DOF devices are more suitable for comprehensive ankle rehabilitation exercises. Other factors including posture adjustability and sensing functions should also be considered to promote related clinical applications. An ankle rehabilitation robot with reconfigurability to maximize its functions will be a new research point towards optimal design, especially on parallel mechanisms.

Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users' anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase.

Towards human-knee orthosis interaction based on adaptive impedance control through stiffness adjustment

Rehabilitation interventions involving powered, wearable lower limb orthoses that can provide high-challenging locomotor tasks for repetitive training sessions, mainly when assist-as-needed strategies, such as adaptive impedance control, are designed. In this study, the adaptive behavior was ensured by software control of the robotic stiffness involved in the human-knee orthosis interaction in function of the gait cycle and speed. To estimate the stiffness, we analyzed the interaction torque-angle characteristics with experimental data. The speed-stiffness dependency was more evident when high stiffness values are demanded by the user's effort. Experimental evidence from five healthy subjects highlight that the adaptive control strategy provides a more comfortable, natural motion, and kinematic freedom as compared to the trajectory tracking control, allowing the user to contribute to the gait training. Future insights cover the implementation of gravitational compensation and real-time estimation and control of all inner dynamic properties of the impedance control law.

Compensating for telecommunication delays during robotic telerehabilitation

Rehabilitation robotic systems may afford better care and telerehabilitation may extend the use and benefits of robotic therapy to the home. Data transmissions over distance are bound by intrinsic communication delays which can be significant enough to deem the activity unfeasible. Here we describe an approach that combines unilateral robotic telerehabilitation and serious games. This approach has a modular and distributed design that permits different types of robots to interact without substantial code changes. We demonstrate the approach through an online multiplayer game. Two users can remotely interact with each other with no force exchanges, while a smoothing and prediction algorithm compensates motions for the delay in the Internet connection. We demonstrate that this approach can successfully compensate for data transmission delays, even when testing between the United States and Brazil. This paper presents the initial experimental results, which highlight the performance degradation with increasing delays as well as improvements provided by the proposed algorithm, and discusses planned future developments.