Advancing the Underactuated Grasping Capabilities of Single Actuator Prosthetic Hands

The last decade has seen significant advancements in upper limb prosthetics, specifically in the myoelectric control and powered prosthetic hand fields, leading to more active and social lifestyles for the upper limb amputee community. Notwithstanding the improvements in complexity and control of myoelectric prosthetic hands, grasping still remains one of the greatest challenges in robotics. Upper-limb amputees continue to prefer more antiquated body-powered or powered hook terminal devices that are favored for their control simplicity, lightweight and low cost; however, these devices are nominally unsightly and lack in grasp variety. The varying drawbacks of both complex myoelectric and simple body-powered devices have led to low adoption rates for all upper limb prostheses by amputees, which includes 35% pediatric and 23% adult rejection for complex devices and 45% pediatric and 26% adult rejection for body-powered devices [1]. My research focuses on progressing the grasping capabilities of prosthetic hands driven by simple control and a single motor, to combine the dexterous functionality of the more complex hands with the intuitive control of the more simplistic body-powered devices with the goal of helping upper limb amputees return to more active and social lifestyles. Optimization of a prosthetic hand driven by a single actuator requires the optimization of many facets of the hand. This includes optimization of the finger kinematics, underactuated mechanisms, geometry, materials and performance when completing activities of daily living. In my dissertation, I will present chapters dedicated to improving these subsystems of single actuator prosthetic hands to better replicate human hand function from simple control. First, I will present a framework created to optimize precision grasping – which is nominally unstable in underactuated configurations – from a single actuator. I will then present several novel mechanisms that allow a single actuator to map to higher degree of freedom motion and multiple commonly used grasp types. I will then discuss how fingerpad geometry and materials can better grasp acquisition and frictional properties within the hand while also providing a method of fabricating lightweight custom prostheses. Last, I will analyze the results of several human subject testing studies to evaluate the optimized hands performance on activities of daily living and compared to other commercially available prosthesis

Human-centered Electric Prosthetic (HELP) Hand

Through a partnership with Indian non-profit Bhagwan Mahaveer Viklang Sahayata Samiti, we designed a functional, robust, and and low cost electrically powered prosthetic hand that communicates with unilateral, transradial, urban Indian amputees through a biointerface. The device uses compliant tendon actuation, a small linear servo, and a wearable garment outfitted with flex sensors to produce a device that, once placed inside a prosthetic glove, is anthropomorphic in both look and feel. The prosthesis was developed such that future groups can design for manufacturing and distribution in India

Design and development of robust hands for humanoid robots

Design and development of robust hands for humanoid robot

Design and Exploration of Feedforward Haptic Feedback in Anthropomorphically-Driven Prostheses

Here, we present a wearable, anthropomorphically-driven prosthesis with a built-in haptic feedback system. The device was designed and built to accommodate specific design parameters. Two control schemes were proposed and compared in a user study with N=6 able-bodied participants performing the Box and Blocks test. The first control scheme was designed to provide an intuitive, human-like actuation and relaxation of the hand, while the other controller was designed to reduce fatigue from sustaining EMG signals. Participants performed significantly better with lower fatigue levels while using the intuitive controller as opposed to the second controller. In addition, task performance with both controllers was better than reported performance with standard myoelectric prostheses. In addition, a second experiment compared the unilateral manual dexterity of N=3 able-bodied participants under three distinct conditions: vibration haptic feedback, skin stretch haptic feedback, and no haptic feedback. These findings suggest that there is utility in wearable anthropomorphically-driven prostheses, and provide support for future studies aimed at exploring anthropomorphically-driven prostheses

Application of dexterous space robotics technology to myoelectric prostheses

Future space missions will require robots equipped with highly dexterous robotic hands to perform a variety of tasks. A major technical challenge in making this possible is an improvement in the way these dexterous robotic hands are remotely controlled or teleoperated. NASA is currently investigating the feasibility of using myoelectric signals to teleoperate a dexterous robotic hand. In theory, myoelectric control of robotic hands will require little or no mechanical parts and will greatly reduce the bulk and weight usually found in dexterous robotic hand control devices. An improvement in myoelectric control of multifinger hands will also benefit prosthetics users. Therefore, as an effort to transfer dexterous space robotics technology to prosthetics applications and to benefit from existing myoelectric technology, NASA is collaborating with the Limbs of Love Foundation, the Institute for Rehabilitation and Research, and Rice University in developing improved myoelectric control multifinger hands and prostheses. In this paper, we will address the objectives and approaches of this collaborative effort and discuss the technical issues associated with myoelectric control of multifinger hands. We will also report our current progress and discuss plans for future work

Manos Robóticas Antropomórficas: una revisión

This paper presents a review on main topic regarding to anthropomorphic robotic hands developed in the last years, taking into account the more important mechatronics designs submit on the literature, and making a comparison between them. The next chapters deepen on level of anthropomorphism and dexterity in advanced actuated hands and upper limbs prostheses, as well as a brief overview on issues such as grasping, transmission mechanisms, sensory and actuator system, and also a short introduction on under-actuated robotic hands is reported.Este artículo presenta una revisión de los principales desarrollos que se han hecho en los últimos años en manos robóticas antropomórficas. Las primeras secciones tratan temas como el grado de antropomorfismo y de destreza en las manos robóticas más avanzadas, incluyendo una comparación entre ellas. También se abordan temas como la capacidad de agarre de los efectores finales, los mecanismos de trasmisión, el sistema actuador y sensórico, así como una breve introducción al tema de manos robóticas sub-actuadas. Dirección de correspondencia: Carrera 11 # 101-80, Bogotá (Colombia)

A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses

A sense of touch is essential for amputees to reintegrate into their social and work life. The design of the next generation of the prostheses will have the ability to effectively convey the tactile information between the amputee and the artificial limbs. This work reviews non-invasive haptic feedback stimulation techniques to convey the tactile information from the prosthetic hand to the amputee’s brain. Various types of actuators that been used to stimulate the patient’s residual limb for different types of artificial prostheses in previous studies have been reviewed in terms of functionality, effectiveness, wearability and comfort. The non-invasive hybrid feedback stimulation system was found to be better in terms of the stimulus identification rate of the haptic prostheses’ users. It can be conclude that integrating hybrid haptic feedback stimulation system with the upper limb prostheses leads to improving its acceptance among users

On the development of a cybernetic prosthetic hand

The human hand is the end organ of the upper limb, which in humans serves the important function of prehension, as well as being an important organ for sensation and communication. It is a marvellous example of how a complex mechanism can be implemented, capable of realizing very complex and useful tasks using a very effective combination of mechanisms, sensing, actuation and control functions. In this thesis, the road towards the realization of a cybernetic hand has been presented. After a detailed analysis of the model, the human hand, a deep review of the state of the art of artificial hands has been carried out. In particular, the performance of prosthetic hands used in clinical practice has been compared with the research prototypes, both for prosthetic and for robotic applications. By following a biomechatronic approach, i.e. by comparing the characteristics of these hands with the natural model, the human hand, the limitations of current artificial devices will be put in evidence, thus outlining the design goals for a new cybernetic device. Three hand prototypes with a high number of degrees of freedom have been realized and tested: the first one uses microactuators embedded inside the structure of the fingers, and the second and third prototypes exploit the concept of microactuation in order to increase the dexterity of the hand while maintaining the simplicity for the control. In particular, a framework for the definition and realization of the closed-loop electromyographic control of these devices has been presented and implemented. The results were quite promising, putting in evidence that, in the future, there could be two different approaches for the realization of artificial devices. On one side there could be the EMG-controlled hands, with compliant fingers but only one active degree of freedom. On the other side, more performing artificial hands could be directly interfaced with the peripheral nervous system, thus establishing a bi-directional communication with the human brain

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