A Preliminary Study on Robot-Assisted Ankle Rehabilitation for the Treatment of Drop Foot

This paper involves the use of a compliant ankle rehabilitation robot (CARR) for the treatment of drop foot. The robot has a bio-inspired design by employing four Festo Fluidic muscles (FFMs) that mimic skeletal muscles actuating three rotational degrees of freedom (DOFs). A trajectory tracking controller was developed in joint task space to track the predefined trajectory of the end effector. This controller was achieved by controlling individual FFM length based on inverse kinematics. Three patients with drop foot participated in a preliminary study to evaluate the potential of the CARR for clinical applications. Ankle stretching exercises along ankle dorsiflexion and plantarflexion (DP) were delivered for treating drop foot. All patients gave positive feedback in using this ankle robot for the treatment of drop foot, although some limitations exist. The proposed controller showed satisfactory accuracy in trajectory tracking, with all root mean square deviation (RMSD) values no greater than 0.0335 rad and normalized root mean square deviation (NRMSD) values less than 6.7%. These preliminary findings support the potentials of the CARR for clinical applications. Future work will investigate the effectiveness of the robot for treating drop foot on a large sample of subjects

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

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

Model based open-loop posture control of a parallel ankle assessment and rehabilitation robot

Ankle injuries are very common in daily life out of musculoskeletal or neurological reasons. Traditional ankle therapy usually requires cooperative and intensive efforts from therapists and patients. Robot-assisted ankle rehabilitation techniques have been actively researched in the past few decades. However, limitations exist such as inappropriate robot design, limited range of motion (ROM) and torque generation capability, or lack of measurement of the interaction between robots and patients. This paper proposes a novel ankle assessment and rehabilitation robot (AARR) that could perform a three-dimensional robotic training with real-time ankle assessment. This study focuses on the control of the robot for ankle rehabilitation rather than assessment. A preliminary test on a healthy subject was conducted using the AARR with a model based open-loop controller. Results show that the robotic training is continuous although the trajectory tracking accuracy is not high. It is concluded that the robotic design has potential for ankle rehabilitation. This model based open-loop controller provides continuous robotic training and is suitable for low precision tracking requirement. It can also serve advanced control schemes as the forward feedback

Robot-Aided Systems for Improving the Assessment of Upper Limb Spasticity: A Systematic Review

This article belongs to the Special Issue Sensors Technology for Medical Robotics.Spasticity is a motor disorder that causes stiffness or tightness of the muscles and can interfere with normal movement, speech, and gait. Traditionally, the spasticity assessment is carried out by clinicians using standardized procedures for objective evaluation. However, these procedures are manually performed and, thereby, they could be influenced by the clinician’s subjectivity or expertise. The automation of such traditional methods for spasticity evaluation is an interesting and emerging field in neurorehabilitation. One of the most promising approaches is the use of robot-aided systems. In this paper, a systematic review of systems focused on the assessment of upper limb (UL) spasticity using robotic technology is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the morphology of devices, the data acquisition systems, the outcome generation method, and the focus of intervention (assessment and/or training). Finally, a series of guidelines and challenges that must be considered when designing and implementing fully-automated robot-aided systems for the assessment of UL spasticity are summarized

Review of control strategies for robotic movement training after neurologic injury

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies

Bioinspired robotic rehabilitation tool for lower limb motor learning after stroke

Mención Internacional en el título de doctorEsta tesis doctoral presenta, tras repasar la marcha humana, las principales patologíıas y condiciones que la afectan, y los distintos enfoques de rehabilitación con la correspondiente implicación neurofisiológica, el camino de investigación que desemboca en la herramienta robótica de rehabilitación y las terapias que se han desarrollado en el marco de los proyectos europeos BioMot: Smart Wearable Robots with Bioinspired Sensory-Motor Skills y HANK: European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients, y probado bajo el paraguas del proyecto europeo ASTONISH: Advancing Smart Optical Imaging and Sensing for Health y el proyecto nacional ASSOCIATE: A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury.This doctoral thesis presents, after reviewing human gait, the main pathologies and conditions that affect it, and the different rehabilitation approaches with the corresponding neurophysiological implications, the research journey that leads to the development of the rehabilitation robotic tool, and the therapies that have been designed, within the framework of the European projects BioMot: Smart Wearable Robots with Bioinspired Sensory-Motor Skills and HANK: European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients and tested under the umbrella of the European project ASTONISH: Advancing Smart Optical Imaging and Sensing for Health and the national project ASSOCIATE: A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury.This work has been carried out at the Neural Rehabilitation Group (NRG), Cajal Institute, Spanish National Research Council (CSIC). The research presented in this thesis has been funded by the Commission of the European Union under the BioMot project - Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT - 611695); under HANK Project - European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients (Grant Agreements number H2020-EU.2. - PRIORITY ’Industrial leadership’ and H2020-EU.3. - PRIORITY ’Societal challenges’ - 699796); also under the ASTONISH Project - Advancing Smart Optical Imaging and Sensing for Health (Grant Agreement number H2020-EU.2.1.1.7. - ECSEL - 692470); with financial support of Spanish Ministry of Economy and Competitiveness (MINECO) under the ASSOCIATE project - A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury (Grant Agreement number 799158449-58449-45-514); and with grant RYC-2014-16613, also by Spanish Ministry of Economy and Competitiveness.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fernando Javier Brunetti Fernández.- Secretario: Dorin Sabin Copaci.- Vocal: Antonio Olivier

Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury

Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject-specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot's impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197

After Stroke Movement Impairments: A Review of Current Technologies for Rehabilitation

This chapter presents a review of the rehabilitation technologies for people who have suffered a stroke, comparing and analyzing the impact that these technologies have on their recovery in the short and long term. The problematic is presented, and motor impairments for upper and lower limbs are characterized. The goal of this chapter is to show novel trends and research for the assistance and treatment of motor impairment caused by strokes

A Feasibility Study of Robot-Assisted Ankle Training Triggered by Combination of SSVEP Recognition and Motion Characteristics

In order to inspire subjects exerting more energy and pay more attention to SSVEP-based ankle training, this study introduce motion intention detection both in the first half cycle of single trainings and at the beginning of the training. This study also propose a novel method to recognize motion intention of subjects through merging the motion characteristics of the ankle training into the identification of SSVEP signals. Five healthy subjects participate in the training, and all can accomplish the training with the success rate of more than 80%. The proposed hybrid method can increase success rate from 50% to 80% comparing with the identification of SSVEP signals