The design and mathematical modelling of novel extensor bending pneumatic artificial muscles (EBPAMs) for soft exoskeletons

This article presents the development of a power augmentation and rehabilitation exoskeleton based on a novel actuator. The proposed soft actuators are extensor bending pneumatic artificial muscles. This type of soft actuator is derived from extending McKibben artificial muscles by reinforcing one side to prevent extension. This research has experimentally assessed the performance of this new actuator and an output force mathematical model for it has been developed. This new mathematical model based on the geometrical parameters of the extensor bending pneumatic artificial muscle determines the output force as a function of the input pressure. This model is examined experimentally for different actuator sizes. After promising initial experimental results, further model enhancements were made to improve the model of the proposed actuator. To demonstrate the new bending actuators a power augmentation and rehabilitation soft glove has been developed. This soft hand exoskeleton is able to fit any adult hand size without the need for any mechanical system changes or calibration. EMG signals from the human hand have been monitored to prove the performance of this new design of soft exoskeleton. This power augmentation and rehabilitation wearable robot has been shown to reduce the amount of muscles effort needed to perform a number of simple grasps

Wrist rehabilitation exoskeleton robot based on pneumatic soft actuators

The aim of this paper is to describe the design of a soft, wearable splint for wrist joint rehabilitation, based on pneumatic soft actuators. The extensor bending and the contraction types of pneumatic soft actuators have been adopted in this study. These actuators are shown to be appropriate by examining their characteristics. The main contributions of this study are developing a safe, lightweight, soft and small actuator for direct human interaction, designing a novel single portable wearable soft robot capable of performing all wrist rehabilitation movements, and using low-cost materials to create the device. Three modes of rehabilitation exercises in the exoskeleton are involved: Flexion/Extension, Radial/Ulnar deviation, and circular movements

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

Novel soft bending actuator based power augmentation hand exoskeleton controlled by human intention

This article presents the development of a soft material power augmentation wearable robot using novel bending soft artificial muscles. This soft exoskeleton was developed as a human hand power augmentation system for healthy or partially hand disabled individuals. The proposed prototype serves healthy manual workers by decreasing the muscular effort needed for grasping objects. Furthermore, it is a power augmentation wearable robot for partially hand disabled or post-stroke patients, supporting and augmenting the fingers’ grasping force with minimum muscular effort in most everyday activities. This wearable robot can fit any adult hand size without the need for any mechanical system changes or calibration. Novel bending soft actuators are developed to actuate this power augmentation device. The performance of these actuators has been experimentally assessed. A geometrical kinematic analysis and mathematical output force model have been developed for the novel actuators. The performance of this mathematical model has been proven experimentally with promising results. The control system of this exoskeleton is created by hybridization between cascaded position and force closed loop intelligent controllers. The cascaded position controller is designed for the bending actuators to follow the fingers in their bending movements. The force controller is developed to control the grasping force augmentation. The operation of the control system with the exoskeleton has been experimentally validated. EMG signals were monitored during the experiments to determine that the proposed exoskeleton system decreased the muscular efforts of the wearer

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

Design Methodology for Soft Wearable Devices—The MOSAR Case

This paper proposes a methodology from the conception to the manufacture of soft wearable devices (SWD). This methodology seeks to unify medical, therapeutic and engineering guidelines for research, development and innovation. The aforementioned methodology is divided into two stages (A and B) and four phases. Stage A only includes phase 1 to identify the main necessity for a patient that will define the target of its associated device. Stage B encompasses phases 2, 3 and 4. The development of three models (virtual, mathematical and experimental physical) of the required device is addressed in phase 2. Phase 3 concerns the control and manufacture of the experimental physical model (EPM). Phase 4 focuses on the EPM experimental validation. As a result of this methodology, 13 mobility, 11 usability and 3 control iterative design criteria for SWD are reported. Moreover, more than 50 products are provided on a technological platform with modular architectures that facilitate SWD diversification. A case study related to a soft mobilizer for upper limb rehabilitation is reported. Nevertheless, this methodology can be implemented in different areas and accelerates the transition from development to innovation

Geometry-based customization of bending modalities for 3D-printed soft pneumatic actuators

In this work, we propose a novel type of 3D-printed soft pneumatic actuator that allows geometry-based customization of bending modalities. While motion in the 3D-space has been achieved for several types of soft actuators, only 2D-bending has been previously modelled and characterized within the scope of 3D-printed soft pneumatic actuators. We developed the first type of 3D-printed soft pneumatic actuator which, by means of the unique feature of customizable cubes at an angle with the longitudinal axis of the structure, is capable of helical motion. Thus, we characterize its mechanical behavior and formulate mathematical and FEA models to validate the experimental results. Variation to the pattern of the inclination angle along the actuator is then demonstrated to allow for complex 3D-bending modalities and the main applications in the fields of object manipulation and wearable robotics are finally discussed