Posts Tagged exoskeletons
[Abstract] A Review: Hand Exoskeleton Systems, Clinical Rehabilitation Practices, and Future Prospects
Spinal cord injury (SCI) and stroke are pathologies that often result in the loss of/decrease in hand functionality. Hand function is a critical component of everyday life and therefore, a primary focus of clinical SCI/stroke rehabilitation is hand function recovery/improvement. In recent years, there has been a surge in hand exoskeleton research due to the potential for exoskeletons to improve clinical rehabilitation efficiency through automation. However, there is a disconnect between current clinical practice and exoskeleton research, resulting in a minority of hand exoskeletons being tested on individuals with SCI and/or stroke. This review article provides a comprehensive analysis and review of hand exoskeleton studies based on clinical rehabilitation practices to bridge the knowledge gap between clinical application and laboratory research. The key findings from this paper are: 1) current hand exoskeletons can successfully complete simple ADL tasks but lack the precision for fine motor control, 2) most hand exoskeletons exhibit a low number of degrees-of-freedom compared to the human hand, which may limit movement capability, 3) the majority of hand exoskeletons lack sensing capabilities, restricting viable control methods and user interfaces, and 4) inconsistent evaluation methods across studies do not allow for accurate performance assessment for different exoskeletons. The comparative assessments performed by this survey article show that there remain deficits between clinical hand rehabilitation practices and the current state of hand exoskeletons. By delineating these shortcomings, the information presented in this work can help inform future developments in the field of assistive and rehabilitative hand exoskeletons such that the gap between research and application may be closed.
Published in: IEEE Transactions on Medical Robotics and Bionics ( Early Access )
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[Abstract] Over-ground robotic lower limb exoskeleton in neurological gait rehabilitation: User experiences and effects on walking ability
BACKGROUND: Over-ground robotic lower limb exoskeletons are safe and feasible in rehabilitation with individuals with spinal cord injury (SCI) and stroke. Information about effects on stroke rehabilitees is scarce and descriptions of learning process and user experience is lacking.
OBJECTIVE: The objectives of this study were to describe how rehabilitees learn exoskeleton use, to study effects of exoskeleton assisted walking (EAW) training, and to study rehabilitees’ user experiences.
METHODS: One-group pre-test post-test pre-experimental study involved five rehabilitees with stroke or traumatic brain injury (TBI). Participants in chronic phase underwent twice a week an 8-week training intervention with Indego exoskeleton. Process of learning to walk and the level of assistance were documented. Outcome measurements were conducted with 6-minute and 10-meter walk tests (6 MWT, 10 mWT). User experience was assessed with a satisfaction questionnaire.
RESULTS: Rehabilitees learnt to walk using the exoskeleton with the assistance from 2–3 therapists within two sessions and progressed individually. Three participants improved their results in 10 mWT, four in 6 MWT. The rehabilitees felt comfortable and safe when using and exercising with the device.
CONCLUSION: Indego exoskeleton may be beneficial to gait rehabilitation with chronic stroke or TBI rehabilitees. The rehabilitees were satisfied with the exoskeleton as a rehabilitation device.
Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices.
[Abstract] Design and Validation of a Self-aligning Index Finger Exoskeleton for Post-Stroke Rehabilitation – Full Text PDF
Rehabilitation of hand functions is necessary to improve post-stroke patients’ quality of life. There is initial evidence that hand exoskeletons should exercise flexion/extension (f/e) and abduction/adduction (a/a) of the fingers to rebuild hand functions. However, designing a self-alignment mechanism of the metacarpophalangeal (MCP) joint to improve its wearing comfort is still a challenge. In this paper, a novel index finger exoskeleton with three motors is proposed to help post-stroke patients perform finger a/a and f/e training. A spatial mechanism with passive degrees of freedom for the MCP joint is designed to realize human-robot axes self-alignment. The proposed mechanism’s kinematic compatibility is analyzed to show its self-aligning capability, and the kineto-statics analysis is performed to present the exoskeleton’s static characteristics. Finally, kinematic and static experiments have been conducted, and the results indicate that the standardized reaction forces square sum of the exoskeleton to the MCP joint can be reduced by 65.8% compared with the state-of-the-art exoskeleton. According to the experimental results, the exoskeleton can achieve the a/a and f/e training and human-robot axes self-alignment, and improve its comfortability. In the future, clinical trials will be further studied to test the exoskeleton.
[Review] Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation – Full Text
Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.
The proper functioning of the limbs of the human body plays a fundamental role in people’s health. When these limbs are temporarily or permanently affected, significant motor difficulties appear. Currently, there is a rapid growth of disability-related diseases worldwide. In a global context, the World Report on Disability  highlights that approximately 15% of the world’s population has some form of motor disability and 4% of them suffer from diseases linked to motor or neuromotor dysfunction. Any form of disability, whether mild, moderate, or severe, impairs an individual’s functional autonomy and interaction with the environment. Other underlying social or demographic factors may increase the incidence of disability , as may inadequate coverage of traditional or technology-based rehabilitation services, poor coordination of care facilities, and overburdening of existing specialists .When it comes to the most common problems that trigger neuromotor impairment, stroke is recurrent in all populations . Its various manifestations and consequences (e.g., hemiparesis/ hemiplegia, traumatic brain injury, and cerebral palsy) represent some of the main causes of disability in the upper limbs in the medium and long term [5,6].From a rehabilitation perspective, the effectiveness of a traditional treatment depends on the skill of therapists, their previous experience in treating similar cases, and their ability to formulate successful rehabilitation plans [7,8]. Usually, the assessment of the patients and their progress is not quantified in a timely, adequate, and objective manner, thereby reducing the possibility of knowing the impact of rehabilitation .Although the application of assessment and therapeutic systems based on robotic exoskeletons started in the past two decades  and has shown encouraging results in the rehabilitation of upper limbs [11,12], there are still applied research niches that deserve to be explored . The design of rehabilitation technology is usually not followed by full or partial clinical trials; thus, such developments do not find direct applicability in medical or rehabilitation centres , which is important to pave the way for an implementation in the clinical practice.This offers a new opportunity for the development of robust and reliable systems, enabling the recovery of lost motor control due to accidental injury or illness [15,16]. The use of active devices in rehabilitation was proved to be feasible , with direct benefits limited not only to patients with motor or neuromotor injuries, but also in other areas where human movement is critical and in terms of optimisation of healthcare resources .The implementation of computational techniques based on artificial intelligence (AI) embedded in robotic exoskeletons for rehabilitation, and the development of lighter, portable, and ergonomic systems [19,20], represent the main topics for the present review. An active search of recent literature to underpin the future of research into this type of technology is necessary, which led to this document.This systematic review includes articles published in Scopus, IEEE Xplore, Web of Science, and PubMed between January 2016 and November 2020. The focus of the review is on wearable and ergonomic robotic exoskeletons for the upper limbs, whose control, data collection, or processing systems are based on AI algorithms. A large number of studies report an increase in the use of different artificial neural network (ANNs) architectures, and the mixing of traditional control techniques with intelligent or adaptive optimisers, creating robust or hybrid systems.The use of AI-based techniques has been recognised as able to enrich the rehabilitation process by providing a comprehensive assessment of a patient’s performance and increasing the confidence of final users (i.e., patients and healthcare specialists) when interacting with robots for rehabilitation.As far as the mobility of these devices is concerned, the current trend is towards reductions in weight and dimensions to promote performance in activities of daily living (ADLs) and therefore increase independence, but it was necessary in some cases to reduce the degrees of freedom (DoF) in order to have more compact systems.[…]
[ARTICLE] Exoskeletal devices for hand assistance and rehabilitation: a comprehensive analysis of state-of-the-art technologies – Full Text PDF
Robots are effective tools for aiding in the restoration of hand function through rehabilitation programs or by providing in-task assistance. To date, a multitude of exoskeletal devices employing distinct technologies have been proposed, making navigating this field a challenging task. To this end,we propose a set of classification criteria to help categorise devices. In this review, a set of 97 publications representing 72 active exoskeletal devices for hand assistance and rehabilitation is analysed. Furthermore, the distribution over the years within each of the criteria is presented. Results show clear trends, such as preferring underactuated devices, electrical transducers with flexible transmission or the more recent uptake of soft technologies. Lastly, the readiness level of hand exoskeleton technology is presented in terms of the whole device and each of the identified sub-classifications. Most of the devices are still in laboratory testing phase, undergoing healthy subject trials or limited clinical trials, with very few having actually reached the market. We hope to provide researchers with a comprehensive analysis of currently employed design choices in hand exoskeletons, highlighting the most developed avenues of research and the latest emerging ones.[…]
Posted by Debbie Overman
Robotics researchers are developing exoskeletons and prosthetic legs capable of thinking and making control decisions on their own using sophisticated artificial intelligence (AI) technology. Their latest research is published in IEEE Transactions on Medical Robotics and Bionics.
The system combines computer vision and deep-learning AI to mimic how able-bodied people walk by seeing their surroundings and adjusting their movements, a media release from University of Waterloo explains.
“We’re giving robotic exoskeletons vision so they can control themselves.”
— Brokoslaw Laschowski, a PhD candidate in systems design engineering who leads a University of Waterloo research project called ExoNet
Exoskeletons legs operated by motors already exist, but users must manually control them via smartphone applications or joysticks.
“That can be inconvenient and cognitively demanding. Every time you want to perform a new locomotor activity, you have to stop, take out your smartphone and select the desired mode.”
— Laschowski, also a student member of the Waterloo Artificial Intelligence Institute (Waterloo.ai)
To address that limitation, the researchers fitted exoskeleton users with wearable cameras and are now optimizing AI computer software to process the video feed to accurately recognize stairs, doors and other features of the surrounding environment.
Next Phase of Research
The next phase of the ExoNet research project will involve sending instructions to motors so that robotic exoskeletons can climb stairs, avoid obstacles or take other appropriate actions based on analysis of the user’s current movement and the upcoming terrain, the release continues.
“Our control approach wouldn’t necessarily require human thought. Similar to autonomous cars that drive themselves, we’re designing autonomous exoskeletons and prosthetic legs that walk for themselves.”
— Brokoslaw Laschowski, who is supervised by engineering professor John McPhee, the Canada Research Chair in Biomechatronic System Dynamics
The researchers are also working to improve the energy efficiency of motors for robotic exoskeletons and prostheses by using human motion to self-charge the batteries, per the release.
[Source(s): University of Waterloo, EurekAlert]
[Abstract] A Portable Device for Hand Rehabilitation with Force Cognition: Design, Interaction and Experiment
Introducing interactive system into portable robots for hand rehabilitation has always been a crucial topic. Moreover, hand rehabilitation with force cognition can make patients participate actively and improve rehabilitation effect. In this paper, we design a portable robotic device with interactive system for patients to rehabilitate with force cognition. Firstly, an exoskeleton glove is designed with a compact mechanical structure which is controlled by a real-time feedback system. The portable device allows patients to rehabilitate not only in hospital. Next, an interactive system including virtual environment and force cognition is introduced to detect the hand motion and collision. At last, clinical tests of our portable device is carried out with 9 subjects after tendon injury to show the effective assistance with our device.