7,221 research outputs found
JNER at 15 years: analysis of the state of neuroengineering and rehabilitation.
On JNER's 15th anniversary, this editorial analyzes the state of the field of neuroengineering and rehabilitation. I first discuss some ways that the nature of neurorehabilitation research has evolved in the past 15 years based on my perspective as editor-in-chief of JNER and a researcher in the field. I highlight increasing reliance on advanced technologies, improved rigor and openness of research, and three, related, new paradigms - wearable devices, the Cybathlon competition, and human augmentation studies - indicators that neurorehabilitation is squarely in the age of wearability. Then, I briefly speculate on how the field might make progress going forward, highlighting the need for new models of training and learning driven by big data, better personalization and targeting, and an increase in the quantity and quality of usability and uptake studies to improve translation
A flexible sensor technology for the distributed measurement of interaction pressure
We present a sensor technology for the measure of the physical human-robot interaction pressure developed in the last years at Scuola Superiore Sant'Anna. The system is composed of flexible matrices of opto-electronic sensors covered by a soft silicone cover. This sensory system is completely modular and scalable, allowing one to cover areas of any sizes and shapes, and to measure different pressure ranges. In this work we present the main application areas for this technology. A first generation of the system was used to monitor human-robot interaction in upper- (NEUROExos; Scuola Superiore Sant'Anna) and lower-limb (LOPES; University of Twente) exoskeletons for rehabilitation. A second generation, with increased resolution and wireless connection, was used to develop a pressure-sensitive foot insole and an improved human-robot interaction measurement systems. The experimental characterization of the latter system along with its validation on three healthy subjects is presented here for the first time. A perspective on future uses and development of the technology is finally drafted
Comfort-Centered Design of a Lightweight and Backdrivable Knee Exoskeleton
This paper presents design principles for comfort-centered wearable robots
and their application in a lightweight and backdrivable knee exoskeleton. The
mitigation of discomfort is treated as mechanical design and control issues and
three solutions are proposed in this paper: 1) a new wearable structure
optimizes the strap attachment configuration and suit layout to ameliorate
excessive shear forces of conventional wearable structure design; 2) rolling
knee joint and double-hinge mechanisms reduce the misalignment in the sagittal
and frontal plane, without increasing the mechanical complexity and inertia,
respectively; 3) a low impedance mechanical transmission reduces the reflected
inertia and damping of the actuator to human, thus the exoskeleton is
highly-backdrivable. Kinematic simulations demonstrate that misalignment
between the robot joint and knee joint can be reduced by 74% at maximum knee
flexion. In experiments, the exoskeleton in the unpowered mode exhibits 1.03 Nm
root mean square (RMS) low resistive torque. The torque control experiments
demonstrate 0.31 Nm RMS torque tracking error in three human subjects.Comment: 8 pages, 16figures, Journa
Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)
This paper presents the application of neural networks for analysing voltage stability of load buses in electric
power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing
voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural
network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and
adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM
using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power
system. All of the neural network-based models used in this research is developed using MATLAB
Optimization and design of a cable driven upper arm exoskeleton
This paper presents the design of a wearable upper arm exoskeleton that can be used to assist and train arm movements of stroke survivors or subjects with weak musculature. In the last ten years, a number of upper-arm training devices have emerged. However, due to their size and weight, their use is restricted to clinics and research laboratories. Our proposed wearable exoskeleton builds upon our extensive research experience in wire driven manipulators and design of rehabilitative systems. The exoskeleton consists of three main parts: (i) an inverted U-shaped cuff that rests on the shoulder, (ii) a cuff on the upper arm, and (iii) a cuff on the forearm. Six motors, mounted on the shoulder cuff, drive the cuffs on the upper arm and forearm, using cables. In order to assess the performance of this exoskeleton, prior to use on humans, a laboratory test-bed has been developed where this exoskeleton is mounted on a model skeleton, instrumented with sensors to measure joint angles and transmitted forces to the shoulder. This paper describes design details of the exoskeleton and addresses the key issue of parameter optimization to achieve useful workspace based on kinematic and kinetic models.</jats:p
Computational neurorehabilitation: modeling plasticity and learning to predict recovery
Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity
Healthcare Robotics
Robots have the potential to be a game changer in healthcare: improving
health and well-being, filling care gaps, supporting care givers, and aiding
health care workers. However, before robots are able to be widely deployed, it
is crucial that both the research and industrial communities work together to
establish a strong evidence-base for healthcare robotics, and surmount likely
adoption barriers. This article presents a broad contextualization of robots in
healthcare by identifying key stakeholders, care settings, and tasks; reviewing
recent advances in healthcare robotics; and outlining major challenges and
opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201
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