62 research outputs found

    A neuromechanics-based powered ankle exoskeleton to assist walking post-stroke: a feasibility study

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    Background: In persons post-stroke, diminished ankle joint function can contribute to inadequate gait propulsion. To target paretic ankle impairments, we developed a neuromechanics-based powered ankle exoskeleton. Specifically, this exoskeleton supplies plantarflexion assistance that is proportional to the user’s paretic soleus electromyography (EMG) amplitude only during a phase of gait when the stance limb is subjected to an anteriorly directed ground reaction force (GRF). The purpose of this feasibility study was to examine the short-term effects of the powered ankle exoskeleton on the mechanics and energetics of gait. Methods: Five subjects with stroke walked with a powered ankle exoskeleton on the paretic limb for three 5 minute sessions. We analyzed the peak paretic ankle plantarflexion moment, paretic ankle positive work, symmetry of GRF propulsion impulse, and net metabolic power. Results: The exoskeleton increased the paretic plantarflexion moment by 16% during the powered walking trials relative to unassisted walking condition (p \u3c .05). Despite this enhanced paretic ankle moment, there was no significant increase in paretic ankle positive work, or changes in any other mechanical variables with the powered assistance. The exoskeleton assistance appeared to reduce the net metabolic power gradually with each 5 minute repetition, though no statistical significance was found. In three of the subjects, the paretic soleus activation during the propulsion phase of stance was reduced during the powered assistance compared to unassisted walking (35% reduction in the integrated EMG amplitude during the third powered session). Conclusions: This feasibility study demonstrated that the exoskeleton can enhance paretic ankle moment. Future studies with greater sample size and prolonged sessions are warranted to evaluate the effects of the powered ankle exoskeleton on overall gait outcomes in persons post-stroke

    Active robotic training improves locomotor function in a stroke survivor

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    Abstract Background Clinical outcomes after robotic training are often not superior to conventional therapy. One key factor responsible for this is the use of control strategies that provide substantial guidance. This strategy not only leads to a reduction in volitional physical effort, but also interferes with motor relearning. Methods We tested the feasibility of a novel training approach (active robotic training) using a powered gait orthosis (Lokomat) in mitigating post-stroke gait impairments of a 52-year-old male stroke survivor. This gait training paradigm combined patient-cooperative robot-aided walking with a target-tracking task. The training lasted for 4-weeks (12 visits, 3 × per week). The subject’s neuromotor performance and recovery were evaluated using biomechanical, neuromuscular and clinical measures recorded at various time-points (pre-training, post-training, and 6-weeks after training). Results Active robotic training resulted in considerable increase in target-tracking accuracy and reduction in the kinematic variability of ankle trajectory during robot-aided treadmill walking. These improvements also transferred to overground walking as characterized by larger propulsive forces and more symmetric ground reaction forces (GRFs). Training also resulted in improvements in muscle coordination, which resembled patterns observed in healthy controls. These changes were accompanied by a reduction in motor cortical excitability (MCE) of the vastus medialis, medial hamstrings, and gluteus medius muscles during treadmill walking. Importantly, active robotic training resulted in substantial improvements in several standard clinical and functional parameters. These improvements persisted during the follow-up evaluation at 6 weeks. Conclusions The results indicate that active robotic training appears to be a promising way of facilitating gait and physical function in moderately impaired stroke survivors.http://deepblue.lib.umich.edu/bitstream/2027.42/112853/1/12984_2011_Article_375.pd

    Effectiveness of robot-assisted therapy on ankle rehabilitation – a systematic review

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    Objective The aim of this study was to provide a systematic review of studies that investigated the effectiveness of robot-assisted therapy on ankle motor and function recovery from musculoskeletal or neurologic ankle injuries. Methods Thirteen electronic databases of articles published from January, 1980 to June, 2012 were searched using keywords ‘ankle*’, ‘robot*’, ‘rehabilitat*’ or ‘treat*’ and a free search in Google Scholar based on effects of ankle rehabilitation robots was also conducted. References listed in relevant publications were further screened. Eventually, twenty-nine articles were selected for review and they focused on effects of robot-assisted ankle rehabilitation. Results Twenty-nine studies met the inclusion criteria and a total of 164 patients and 24 healthy subjects participated in these trials. Ankle performance and gait function were the main outcome measures used to assess the therapeutic effects of robot-assisted ankle rehabilitation. The protocols and therapy treatments were varied, which made comparison among different studies difficult or impossible. Few comparative trials were conducted among different devices or control strategies. Moreover, the majority of study designs met levels of evidence that were no higher than American Academy for Cerebral Palsy (CP) and Developmental Medicine (AACPDM) level IV. Only one study used a Randomized Control Trial (RCT) approach with the evidence level being II. Conclusion All the selected studies showed improvements in terms of ankle performance or gait function after a period of robot-assisted ankle rehabilitation training. The most effective robot-assisted intervention cannot be determined due to the lack of universal evaluation criteria for various devices and control strategies. Future research into the effects of robot-assisted ankle rehabilitation should be carried out based on universal evaluation criteria, which could determine the most effective method of intervention. It is also essential to conduct trials to analyse the differences among different devices or control strategies

    Joint Trajectory Generation and High-level Control for Patient-tailored Robotic Gait Rehabilitation

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    This dissertation presents a group of novel methods for robot-based gait rehabilitation which were developed aiming to offer more individualized therapies based on the specific condition of each patient, as well as to improve the overall rehabilitation experience for both patient and therapist. A novel methodology for gait pattern generation is proposed, which offers estimated hip and knee joint trajectories corresponding to healthy walking, and allows the therapist to graphically adapt the reference trajectories in order to fit better the patient's needs and disabilities. Additionally, the motion controllers for the hip and knee joints, mobile platform, and pelvic mechanism of an over-ground gait rehabilitation robotic system are also presented, as well as some proposed methods for assist as needed therapy. Two robot-patient synchronization approaches are also included in this work, together with a novel algorithm for online hip trajectory adaptation developed to reduce obstructive forces applied to the patient during therapy with compliant robotic systems. Finally, a prototype graphical user interface for the therapist is also presented

    Application of wearable sensors in actuation and control of powered ankle exoskeletons: a Comprehensive Review

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

    How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

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    Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

    Peak Trailing Limb Angle and Propulsion Symmetry in Individuals with Below Knee Amputation

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    Background: Individuals with lower extremity amputation often present with kinematic and kinetic gait asymmetries and often have difficulty achieving symmetrical walking using their prescribed prosthesis. To understand the impact of limb loss on gait measures, studies often compare individuals with lower limb amputation to healthy control participants or compare the amputated limb to the uninvolved limb while completing a specified task like steady state walking. Commonly implemented treatments for individuals with lower limb amputation are based upon the assumption that equal use of both legs (symmetry) while completing bipedal tasks (e.g., walking) would be beneficial, matching the behavior seen in healthy control individuals. Underlying kinematic or kinetic symmetry, as well as a potential relationship of the two biomechanical gait variables in individuals with below knee amputation have not been thoroughly evaluated during steady state treadmill walking. Methods: We explored potential underlying (a)symmetries in peak trailing limb angle (kinematic) and peak anterior ground reaction force (kinetic) in individuals with below knee amputation walking at self-selected walking speed on a treadmill without upper extremity support. We then implemented real-time visual feedback to alter symmetry and examine the potential relationship between peak trailing limb angle and peak anterior ground reaction force. Later, we recruited and tested healthy control individuals with and without a solid ankle foot orthosis (SAFO) walking at their self-selected walking speed on a treadmill and exposed them to a similar visual feedback program to alter their baseline (a)symmetry. Population: We enrolled eleven of the planned twenty-four individuals with unilateral below knee amputation and fourteen healthy control participants without any lower extremity pathology or gait abnormality. Results: We found that individuals with below knee amputation do have peak trailing limb and anterior ground reaction force asymmetries and unencumbered healthy control individuals demonstrate symmetry of the same outcome measures while walking on a treadmill at self-selected walking speed. The use of real time visual feedback yielded statistically significant differences in peak trailing limb angle in healthy control participants without a solid ankle foot orthosis (p=0.04), peak and impulse anterior ground reaction forces when wearing a solid ankle foot orthosis (p=0.04). Statistically significant correlation between peak trailing limb angle and peak anterior ground reaction force were found in individuals with below knee amputation at baseline (p=0.0004), with real time visual feedback for peak trailing limb angle (p\u3c0.0001), and peak anterior ground reaction force (p=0.0002). Conclusions: Real time visual feedback is one intervention used to alter walking symmetry. Our results do not demonstrate an overwhelming response to real time visual feedback by individuals with below knee amputation or their healthy control counterparts and should be interpreted with caution. This work does provide meaningful information for further studies and interventions to alter symmetry during steady state walking and begins to explore the potential relationship between peak trailing limb angel and peak anterior ground reaction force production during self-selected treadmill walking in individuals with below knee amputation as well as otherwise healthy control individuals

    On improving control and efficiency of a portable pneumatically powered ankle-foot orthosis

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    Ankle foot orthoses (AFOs) are widely used as assistive and/or rehabilitation devices to correct gait of people with lower leg neuromuscular dysfunction and muscle weakness. An AFO is an external device worn on the lower leg and foot that provides mechanical assistance at the ankle joint. Active AFOs are powered devices that provide assistive torque at the ankle joint. We have previously developed the Portable Powered Ankle-Foot Orthosis (PPAFO), which uses pneumatic power via compressed CO2 to provide untethered ankle torque assistance. My dissertation work focused on the development of control strategies for the PPAFO that are robust, applicable to different gait patterns, functional in different gait modes, and energy efficient. Three studies addressing these topics are presented in this dissertation: (1) estimation of the system state during the gait cycle for actuation control; (2) gait mode recognition and control (e.g., stair and ramp descent/ascent); and (3) system analysis and improvement of pneumatic energy efficiency. Study 1 presents the work on estimating the gait state for powered AFO control. The proposed scheme is a state estimator that reliably detects gait events while using only a limited array of sensor data (ankle angle and contact forces at the toe and heel). Our approach uses cross-correlation between a window of past measurements and a learned model to estimate the configuration of the human walker, and detects gait events based on this estimate. The proposed state estimator was experimentally validated on five healthy subjects and with one subject that had neuromuscular impairment. The results highlight that this new approach reduced the root-mean-square error by up to 40% for the impaired subject and up to 49% for the healthy subjects compared to a simplistic direct event controller. Moreover, this approach was robust to perturbations due to changes in walking speed and control actuation. Study 2 proposed a gait mode recognition and control solution to identify a change in walking environment such as stair and ramp ascent/descent. Since portability is a key to the success of the PPAFO as a gait assist device, it is critical to recognize and control for multiple gait modes (i.e., level walking, stair ascent/descent and ramp ascent/descent). While manual mode switching is implemented on most devices, we propose an automatic gait mode recognition scheme by tracking the 3D position of the PPAFO from an inertial measurement unit (IMU). Experimental results indicate that the controller was able to identify the position, orientation and gait mode in real time, and properly control the actuation. The overall recognition success rate was over 97%. Study 3 addressed improving operational runtime by analyzing the system efficiency and proposing an energy harvesting and recycling scheme to save fuel. Through a systematic analysis, the overall system efficiency was determined by deriving both the system operational efficiency and the system component efficiency. An improved pneumatic operation utilized an accumulator to harvest and then recycle the exhaust energy from a previous actuation to power the subsequent actuation. The overall system efficiency was improved from 20.5% to 29.7%, a fuel savings of 31%. Work losses across pneumatic components and solutions to improve them were quantified and discussed. Future work including reducing delay in recognition, exploring faulty recognition, additional options for harvesting human energy, and learning control were proposed

    Assist-as-needed EMG-based control strategy for wearable powered assistive devices

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    Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Robotic-based gait rehabilitation and assistance using Wearable Powered Assistive Devices (WPADs), such as orthosis and exoskeletons, has been growing in the rehabilitation area to recover and augment the motor function of neurologically impaired subjects. These WPADs should provide a personalized assistance, since physical condition and muscular fatigue modify from patient to patient. In this field, electromyography (EMG) signals have been used to control WPADs given their ability to infer the user’s motion intention. However, in cases of motor disability conditions, EMG signals present lower magnitudes when compared to EMG signals under healthy conditions. Thus, the use of WPADs managed by EMG signals may not have potential to provide the assistance that the patient requires. The main goal of this dissertation aims the development of an Assisted-As-Needed (AAN) EMG-based control strategy for a future insertion in a Smart Active Orthotic System (SmartOs). To achieve this goal, the following elements were developed and validated: (i) an EMG system to acquire muscle activity signals from the most relevant muscles during the motion of the ankle joint; (ii) machine learning-based tool for ankle joint torque estimation to serve as reference in the AAN EMG-based control strategy; and (iii) a tool for real EMG-based torque estimation using Tibialis Anterior (TA) and Gastrocnemius Lateralis (GASL) muscles and real ankle joint angles. EMG system showed satisfactory pattern correlations with a commercial system. The reference ankle joint torque was generated based on predicted reference ankle joint kinematics, walking speed information (from 1 to 4 km/h) and anthropometric data (body height from 1.51 m to 1.83 m and body mass from 52.0 kg to 83.7 kg), using five machine learning algorithms: Support Vector Regression (SVR), Random Forest (RF), Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN provided the best performance, predicting the reference ankle joint torque with fitting curves ranging from 74.7 to 89.8 % and Normalized Root Mean Square Errors (NRMSEs) between 3.16 and 8.02 %. EMG-based torque estimation beneficiates of a higher number of muscles, since EMG data from TA and GASL are not enough to estimate the real ankle joint torque.A assistência e reabilitação robótica usando dispositivos de assistência ativos vestíveis (WPADs), como ortóteses e exosqueletos, tem crescido na área da reabilitação com o fim de recuperar e aumentar a função motora de sujeitos com alterações neurológicas. Estes dispositivos devem fornecer uma assistência personalizada, uma vez que a condição física e a fadiga muscular variam de paciente para paciente. Nesta área, sinais de eletromiografia (EMG) têm sido usados para controlar WPADs, dada a sua capacidade de inferir a intenção de movimento do utilizador. Contudo, em casos de deficiência motora, os sinais de EMG apresentam menor amplitude quando comparados com sinais de EMG em condições saudáveis e, portanto, o uso de WPADs geridos por sinais de EMG pode não oferecer a assistência que o paciente necessita. O principal objetivo desta dissertação visa o desenvolvimento de uma estratégia de controlo baseada em EMG capaz de fornecer assistência quando necessário, para futura integração num sistema ortótico ativo e inteligente (SmartOs). Para atingir este objetivo foram desenvolvidos e validados os seguintes elementos: (i) sistema de EMG para adquirir sinais de atividade muscular dos músculos mais relevantes no movimento da articulação do tornozelo; (ii) ferramenta de machine learning para estimação do binário da articulação do tornozelo para servir como referência na estratégia de controlo; e (iii) ferramenta de estimação do binário real do tornozelo considerando sinais de EMG dos músculos Tibialis Anterior (TA) e Gastrocnemius Lateralis (GASL) e ângulo real do tornozelo. O sistema de EMG apresentou correlações satisfatórias com um sistema comercial. O binário de referência para o tornozelo foi gerado com base no ângulo de referência da mesma articulação, velocidade de marcha (de 1 até 4 km/h) e dados antropométricos (alturas de 1.51 m até 1.83 e massas de 52.0 kg até 83.7 kg), usando cinco algoritmos de machine learning: Support Vector Machine, Random Forest, Multilayer Perceptron, Long-Short Term Memory e Convolutional Neural Network. CNN apresentou a melhor performance, prevendo binários de referência do tornozelo com um fit entre 74.7 e 89.8 % e Normalized Root Mean Square Errors (NRMSE) entre 3.16 e 8.02 %. A estimativa do torque com base em sinais de EMG requer a inclusão de um maior número de músculos, uma vez que sinais de EMG dos músculos TA e GASL não foram suficientes
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