Nonlinear control strategy for a cost effective myoelectric prosthetic hand

The loss of a limb tremendously impacts the life of the affected individual. In the past decades, researchers have been developing artificial limbs that may return some of the missing functions and cosmetics. However, the development of dexterous mechanisms capable of mimicking the function of the human hand is a complex venture. Even though myoelectric prostheses have advanced, several issues remain to be solved before an artificial limb may be comparable to its human counterpart. Moreover, the high cost of advanced limbs prevents their widespread use among the low-income population. This dissertation presents a strategy for the low-level of control of a cost effective robotic hand for prosthetic applications. The main purpose of this work is to reduce the high cost associated with limb replacement. The presented strategy uses an electromyographic signal classifier, which detects user intent by classifying 4 different wrist movements. This information is supplied as 4 different pre-shapes of the robotic hand to the low-level of control for safely and effectively performing the grasping tasks. Two proof-of-concept prototypes were implemented, consisting on five-finger underactuated hands driven by inexpensive DC motors and equipped with low-cost sensors. To overcome the limitations and nonlinearities of inexpensive components, a multi-stage control methodology was designed for modulating the grasping force based on slippage detection and nonlinear force control. A multi-stage control methodology for modulating the grasping force based on slippage detection and nonlinear force control was designed. The two main stages of the control strategy are the force control stage and the detection stage. The control strategy uses the force control stage to maintain a constant level of force over the object. The results of the experiments performed over this stage showed a rising time of less than 1 second, force overshoot of less than 1 N and steady state error of less than 0.15 N. The detection stage is used to monitor any sliding of the object from the hand. The experiments performed over this stage demonstrated a delay in the slip detection process of less than 200 milliseconds. The initial force, and the amount of force incremented after sliding is detected, were adjusted to reduce object displacement. Experiments were then performed to test the control strategy on situations often encountered in the ADL. The results showed that the control strategy was able to detect the dynamic changes in mass of the object and to successfully adjust the grasping force to prevent the object from dropping. The evaluation of the proposed control strategy suggests that this methodology can overcome the limitation of inexpensive sensors and actuators. Therefore, this control strategy may reduce the cost of current myoelectric prosthesis. We believe that the work presented here is a major step towards the development of a cost effective myoelectric prosthetic hand

ReHand - a portable assistive rehabilitation hand exoskeleton

This dissertation presents a synthesis of a novel underactuated exoskeleton (namely ReHand2) thought and designed for a task-oriented rehabilitation and/or for empower the human hand. The first part of this dissertation shows the current context about the robotic rehabilitation with a focus on hand pathologies, which influence the hand capability. The chapter is concluded with the presentation of ReHand2. The second chapter describes the human hand biomechanics. Starting from the definition of human hand anatomy, passing through anthropometric data, to taxonomy on hand grasps and finger constraints, both from static and dynamic point of view. In addition, some information about the hand capability are given. The third chapter analyze the current state of the art in hand exoskeleton for rehabilitation and empower tasks. In particular, the chapter presents exoskeleton technologies, from mechanisms to sensors, passing though transmission and actuators. Finally, the current state of the art in terms of prototype and commercial products is presented. The fourth chapter introduces the concepts of underactuation with the basic explanation and the classical notation used typically in the prosthetic field. In addition, the chapter describe also the most used differential elements in the prosthetic, follow by a statical analysis. Moreover typical transmission tree at inter-finger level as well as the intra- finger underactuation are explained . The fifth chapter presents the prototype called ReHand summarizing the device description and explanation of the working principle. It describes also the kinetostatic analysis for both, inter- and the intra-finger modules. in the last section preliminary results obtained with the exoskeleton are shown and discussed, attention is pointed out on prototype’s problems that have carry out at the second version of the device. The sixth chapter describes the evolution of ReHand, describing the kinematics and dynamics behaviors. In particular, for the mathematical description is introduced the notation used in order to analyze and optimize the geometry of the entire device. The introduced model is also implemented in Matlab Simulink environment. Finally, the chapter presents the new features. The seventh chapter describes the test bench and the methodologies used to evaluate the device statical, and dynamical performances. The chapter presents and discuss the experimental results and compare them with simulated one. Finally in the last chapter the conclusion about the ReHand project are proposed as well as the future development. In particular, the idea to test de device in relevant environments. In addition some preliminary considerations about the thumb and the wrist are introduced, exploiting the possibility to modify the entire layout of the device, for instance changing the actuator location

Design of an underactuated compliant gripper for surgery using nitinol

Design of an Underactuated Complimant Gripper for surgery Using Nitinol -- Joint Design -- Underactuated Finger Design -- Optimization of the Transmission Mechanism -- Optimization of the Driving Mechanism -- Finite Element Simulation

Pattern recognition-based real-time myoelectric control for anthropomorphic robotic systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatū, New Zealand

All copyrighted Figures have been removed but may be accessed via their source cited in their respective captions.Advanced human-computer interaction (HCI) or human-machine interaction (HMI) aims to help humans interact with computers smartly. Biosignal-based technology is one of the most promising approaches in developing intelligent HCI systems. As a means of convenient and non-invasive biosignal-based intelligent control, myoelectric control identifies human movement intentions from electromyogram (EMG) signals recorded on muscles to realise intelligent control of robotic systems. Although the history of myoelectric control research has been more than half a century, commercial myoelectric-controlled devices are still mostly based on those early threshold-based methods. The emerging pattern recognition-based myoelectric control has remained an active research topic in laboratories because of insufficient reliability and robustness. This research focuses on pattern recognition-based myoelectric control. Up to now, most of effort in pattern recognition-based myoelectric control research has been invested in improving EMG pattern classification accuracy. However, high classification accuracy cannot directly lead to high controllability and usability for EMG-driven systems. This suggests that a complete system that is composed of relevant modules, including EMG acquisition, pattern recognition-based gesture discrimination, output equipment and its controller, is desirable and helpful as a developing and validating platform that is able to closely emulate real-world situations to promote research in myoelectric control. This research aims at investigating feasible and effective EMG signal processing and pattern recognition methods to extract useful information contained in EMG signals to establish an intelligent, compact and economical biosignal-based robotic control system. The research work includes in-depth study on existing pattern recognition-based methodologies, investigation on effective EMG signal capturing and data processing, EMG-based control system development, and anthropomorphic robotic hand design. The contributions of this research are mainly in following three aspects: Developed precision electronic surface EMG (sEMG) acquisition methods that are able to collect high quality sEMG signals. The first method was designed in a single-ended signalling manner by using monolithic instrumentation amplifiers to determine and evaluate the analog sEMG signal processing chain architecture and circuit parameters. This method was then evolved into a fully differential analog sEMG detection and collection method that uses common commercial electronic components to implement all analog sEMG amplification and filtering stages in a fully differential way. The proposed fully differential sEMG detection and collection method is capable of offering a higher signal-to-noise ratio in noisy environments than the single-ended method by making full use of inherent common-mode noise rejection capability of balanced signalling. To the best of my knowledge, the literature study has not found similar methods that implement the entire analog sEMG amplification and filtering chain in a fully differential way by using common commercial electronic components. Investigated and developed a reliable EMG pattern recognition-based real-time gesture discrimination approach. Necessary functional modules for real-time gesture discrimination were identified and implemented using appropriate algorithms. Special attention was paid to the investigation and comparison of representative features and classifiers for improving accuracy and robustness. A novel EMG feature set was proposed to improve the performance of EMG pattern recognition. Designed an anthropomorphic robotic hand construction methodology for myoelectric control validation on a physical platform similar to in real-world situations. The natural anatomical structure of the human hand was imitated to kinematically model the robotic hand. The proposed robotic hand is a highly underactuated mechanism, featuring 14 degrees of freedom and three degrees of actuation. This research carried out an in-depth investigation into EMG data acquisition and EMG signal pattern recognition. A series of experiments were conducted in EMG signal processing and system development. The final myoelectric-controlled robotic hand system and the system testing confirmed the effectiveness of the proposed methods for surface EMG acquisition and human hand gesture discrimination. To verify and demonstrate the proposed myoelectric control system, real-time tests were conducted onto the anthropomorphic prototype robotic hand. Currently, the system is able to identify five patterns in real time, including hand open, hand close, wrist flexion, wrist extension and the rest state. With more motion patterns added in, this system has the potential to identify more hand movements. The research has generated a few journal and international conference publications

A Methodology Towards Comprehensive Evaluation of Shape Memory Alloy Actuators for Prosthetic Finger Design

Presently, DC motors are the actuator of choice within intelligent upper limb prostheses. However, the weight and dimensions associated with suitable DC motors are not always compatible with the geometric restrictions of a prosthetic hand; reducing available degrees of freedom and ultimately rendering the prosthesis uncomfortable for the end-user. As a result, the search is on-going to find a more appropriate actuation solution that is lightweight, noiseless, strong and cheap. Shape memory alloy (SMA) actuators offer the potential to meet these requirements. To date, no viable upper limb prosthesis using SMA actuators has been developed. The primary reasons lie in low force generation as a result of unsuitable actuator designs, and significant difficulties in control owing to the highly nonlinear response of SMAs when subjected to joule heating. This work presents a novel and comprehensive methodology to facilitate evaluation of SMA bundle actuators for prosthetic finger design. SMA bundle actuators feature multiple SMA wires in parallel. This allows for increased force generation without compromising on dynamic performance. The SMA bundle actuator is tasked with reproducing the typical forces and contractions associated with the human finger in a prosthetic finger design, whilst maintaining a high degree of energy efficiency. A novel approach to SMA control is employed, whereby an adaptive controller is developed and tuned using the underlying thermo-mechanical principles of operation of SMA wires. A mathematical simulation of the kinematics and dynamics of motion provides a platform for designing, optimizing and evaluating suitable SMA bundle actuators offline. This significantly reduces the time and cost involved in implementing an appropriate actuation solution. Experimental results show iii that the performance of SMA bundle actuators is favourable for prosthesis applications. Phalangeal tip forces are shown to improve significantly through bundling of SMA wire actuators, while dynamic performance is maintained owing to the design and implementation of the selected control strategy. The work is intended to serve as a roadmap for fellow researchers seeking to design, implement and control SMA bundle actuators in a prosthesis design. Furthermore, the methodology can also be adopted to serve as a guide in the evaluation of other non-conventional actuation technologies in alternative applications

Design of a cybernetic hand for perception and action

Strong motivation for developing new prosthetic hand devices is provided by the fact that low functionality and controllability—in addition to poor cosmetic appearance—are the most important reasons why amputees do not regularly use their prosthetic hands. This paper presents the design of the CyberHand, a cybernetic anthropomorphic hand intended to provide amputees with functional hand replacement. Its design was bio-inspired in terms of its modular architecture, its physical appearance, kinematics, sensorization, and actuation, and its multilevel control system. Its underactuated mechanisms allow separate control of each digit as well as thumb–finger opposition and, accordingly, can generate a multitude of grasps. Its sensory system was designed to provide proprioceptive information as well as to emulate fundamental functional properties of human tactile mechanoreceptors of specific importance for grasp-and-hold tasks. The CyberHand control system presumes just a few efferent and afferent channels and was divided in two main layers: a high-level control that interprets the user’s intention (grasp selection and required force level) and can provide pertinent sensory feedback and a low-level control responsible for actuating specific grasps and applying the desired total force by taking advantage of the intelligent mechanics. The grasps made available by the high-level controller include those fundamental for activities of daily living: cylindrical, spherical, tridigital (tripod), and lateral grasps. The modular and flexible design of the CyberHand makes it suitable for incremental development of sensorization, interfacing, and control strategies and, as such, it will be a useful tool not only for clinical research but also for addressing neuroscientific hypotheses regarding sensorimotor control

The Development of a Brain Controlled Robotic Prosthetic Hand

An anthropomorphic, brain controlled, under actuated, Prosthetic hand has been designed and developed for upper extremity amputees. The hands function is based on micro servo actuation and the use of coupling links between parts of the finger. The control of a prosthetic hand is what differentiates this project from the others. It is the intent of this project to increase the sense of belonging between prosthesis and amputee by controlling the designed device by the brain of the amputee. The platform has been designed to use multiple force sensors to improve control. The project is a feasibility study and will be used to test whether a multi-functional and intuitive prosthetic hand is attainable. The control of the hand will be driven through a neural interface and controlled by a micro-board. This paper focuses on the mechanical design of the hand and the processes used to control the hand using signals emitted from the brain, to increase the sense of belonging between the amputee and prosthetic device. The hand has been developed as a foundation for future research into brain controlled prosthetics at the University of Waikato

지능형 연성 복합재료와 형상기억합금으로 구성된 소프트 로봇 손의 개발

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 안성훈.전통적으로 로봇 매니퓰레이터에 대한 여러 연구들은 정교한 잡기 동작을 얻기 위해 조인트, 링크, 기어와 모터 등과 같은 강성 요소를 주로 사용하였다. 이러한 시스템은 정확, 정교한 움켜짐 동작을 얻을 수 있으나, 이러한 움직임은 통제된 환경 아래에서 구현이 가능하다. 한편, 새롭게 떠오르는 연구 의 한 분야인 소프트 로봇틱스는 유연하고 변형이 가능한 재료를 로봇 구조에 적용하는 방식을 이용하여 연구되어 왔다. 이러한 연구들은 높은 유연성과 적응성을 특징으로 가질 수 있으며, 이를 통해 간단한 설계를 이용하여 자연의 움직임을 구현할 수 있다. 본 연구에서는 지능형 연성 복합재와 형상기억합금을 이용한 굽힘 구동기에 새로운 방식의 힘줄 구동 메커니즘을 적용하였다. 첫째로, 사람 손의 인대를 모사한 슬라이딩 메커니즘을 형상기억합금을 통해 적용한 인공 손가락을 설계하였다. 형상기억합금 와이어와 지능형 연성 복합재로 이루어진 굽힘 구동기에 굽힘 성능을 강화하기 위한 연성 경첩 구조를 적용하였다. 다음으로, 구동기 성능을 평가하고 연성 복합재의 구성에 대한 최적 설계를 결정하기 위한 실험을 수행하였다. 마지막으로, 힘줄 구동 메커니즘의 지능형 연성 복합재로 이루어진 손가락들이 적용된 소프트 로봇 손의 프로토타입을 제작하였고, 여러 모양의 물체에 대한 움켜짐 성능을 평가하였다.Conventionally, various researches on robotic manipulator or hand have been developed with rigid components, such as joints, links, gears and motors, to obtain sophisticated grasping capabilities. Rigid robotic system performs a task with precise and articulated motion with integrated modeling and feedback control system, but it can be conducted under well-controlled environment. On the other hand, soft robotics, as an emerging research field, has been studied using soft and deformable materials for robot structures. It is possible to build system which obtains high compliance and adaptability with simply integrated mechanism, so biological behavior can be realized with compact design. In this research, we developed a novel design of tendon-driven bending actuator using smart soft composite (SSC) and shape memory alloy (SMA). Firstly, artificial finger was designed with SMA wire sliding mechanism which intimates human flexor in hand. This bending actuator, which composed of SMA wire and SSC, has a soft hinge to improve bending performance of the actuator. Then, experiment of actuator was conducted to evaluate capabilities and determine the optimal actuator design according to composition of soft composite. Finally, a prototype of soft robotic hand was developed with the tendon-driven SSC fingers and thumb, and grasping capabilities was shown for various shape of objects.Chapter 1. Introduction 1 1.1 Background 1 1.2 Soft Robotics 2 1.3 Goal of Research 4 Chapter 2. Materials 5 2.1 Shape Memory Alloy (SMA) 5 2.2 Smart Soft Composite (SSC) 7 Chapter 3. Design 10 3.1 Tendon-driven SSC Bending Actuator 10 3.2 Fabrication 13 Chapter 4. Experiment 14 4.1 Experimental Setting 14 4.2 Result 16 Chapter 5. Soft Robotic Hand 19 5.1 Design 19 5.2 Grasping Performance 21 Chapter 6. Conclusion 24 Bibliography 25 Abstract in Korean 30Maste

Adaptive and reconfigurable robotic gripper hands with a meso-scale gripping range

Grippers and robotic hands are essential and important end-effectors of robotic manipulators. Developing a gripper hand that can grasp a large variety of objects precisely and stably is still an aspiration even though research in this area has been carried out for several decades. This thesis provides a development approach and a series of gripper hands which can bridge the gap between micro-gripper and macro-gripper by extending the gripping range to the mesoscopic scale (meso-scale). Reconfigurable topology and variable mobility of the design offer versatility and adaptability for the changing environment and demands. By investigating human grasping behaviours and the unique structures of human hand, a CFB-based finger joint for anthropomorphic finger is developed to mimic a human finger with a large grasping range. The centrodes of CFB mechanism are explored and a contact-aided CFB mechanism is developed to increase stiffness of finger joints. An integrated gripper structure comprising cross four-bar (CFB) and remote-centre-of-motion (RCM) mechanisms is developed to mimic key functionalities of human hand. Kinematics and kinetostatic analyses of the CFB mechanism for multimode gripping are conducted to achieve passive-adjusting motion. A novel RCM-based finger with angular, parallel and underactuated motion is invented. Kinematics and stable gripping analyses of the RCM-based multi-motion finger are also investigated. The integrated design with CFB and RCM mechanisms provides a novel concept of a multi-mode gripper that aims to tackle the challenge of changing over for various sizes of objects gripping in mesoscopic scale range. Based on the novel designed mechanisms and design philosophy, a class of gripper hands in terms of adaptive meso-grippers, power-precision grippers and reconfigurable hands are developed. The novel features of the gripper hands are one degree of freedom (DoF), self-adaptive, reconfigurable and multi-mode. Prototypes are manufactured by 3D printing and the grasping abilities are tested to verify the design approach.EPSR