The role of morphology of the thumb in anthropomorphic grasping : a review

The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands

Design tool for automated crocheting of fabrics

In the context of developing a machine to automatically crochet fabrics, a suitable design tool tailored to the new technology and enabling its application is crucial. The paper offers first insights into the prototype of the crochet machine and presents the approach of such a design tool implemented in Python for creating, modeling and generating the machine instructions. With a graphical user interface (GUI), a flat crocheted fabric can be designed by arranging international crochet symbols for slip stitch (SL), single crochet (SC) and half double crochet (HDC). Built-in error checking mechanisms, following the rules of crochet and the machine’s constraints, will aid inexperienced crocheters in this process. Based on the resulting computer representation as an array containing short strings for the respective stitches, a topology-based 3D model at the meso scale is automatically created as a preview of the designed crocheted fabric. Also, machine instructions to automatically crochet the fabric with the crochet machine prototype are generated by mapping the computer representation of the stitches to macros of G-code and appending them in a valid order. The straightforward design tool shows the capabilities of the crochet machine and is extensible for further enhancements. Through modeling, the structure of the machine-crocheted fabrics is presented for the first time. In comparison to manually crocheted fabrics, the machine-crocheted ones exhibit a technical front and back, since stitches are formed by the machine only from one side

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

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