A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion&-extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm&-exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient's intention.The research was funded by RoboHealth (DPI2013-47944-C4-3-R) and the EDAM (DPI2016-75346-R) Spanish research projects

A hybrid haptic stimulation prosthetic wearable device to recover the missing sensation of the upper limb amputees

A hybrid haptic feedback stimulation system that is capable in sensing the contact pressure, the surface texture, and the temperature, simultaneously, was designed for a prosthetic hand to provide a tactile sensation to amputation patients. In addition, the haptic system was developed to enable the prosthetic’s users to implement withdrawal reflexes due to the thermal noxious stimulus in a quick manner. The re-sensation is achieved by non-invasively stimulating the skin of the patients’ residual limbs, based on the type and the level of tactile signals provided by the sensory system of the prostheses. Accordingly, three stages of design and development were performed to satisfy the research methodology. A vibrotactile prosthetic device, which is designed for the detection of contact pressure and surface texture in upper extremity, represents. While, the design of a novel wearable hybrid pressure-vibration haptic feedback stimulation device for conveying the tactile information regarding the contact pressure between the prosthetic hand and the grasped objects represents the second methodology stage. Lastly, the third stage was achieved by designing a novel hybrid pressure-vibration-temperature feedback stimulation system to provide a huge information regarding the prostheses environment to the users without brain confusing or requiring long pre-training. The main contribution of this work is the development and evaluation of the first step of a novel approach for a lightweight, 7 Degrees-Of-Freedom (DOF) tactile prosthetic arm to perform an effective as well as fast object manipulation and grasping. Furthermore, this study investigates the ability to convey the tactile information about the contact pressure, surface texture, and object temperature to the amputees with high identification accuracy by mean of using the designed hybrid pressure-vibration-temperature feedback wearable device. An evaluation of sensation and response has been conducted on forty healthy volunteers to evaluate the ability of the haptic system to stimulate the human nervous system. The results in term of Stimulus Identification Rate (SIR) show that all the volunteers were correctly able to discriminate the sensation of touch, start of touch, end of touch, and grasping objects. While 94%, 96%, 97%, and 95.24% of the entire stimuli were successfully identified by the volunteers during the experiments of slippage, pressure level, surface texture, and temperature, respectively. The position tracking controller system was designed to synchronize the movements of the volunteers’ elbow joints and the prosthetic’s elbow joint to record the withdrawal reflexes. The results verified the ability of the haptic system to excite the human brain at the abnormal noxious stimulus and enable the volunteers to perform a quick withdrawal reflex within 0.32 sec. The test results and the volunteers' response established evidence that amputees are able to recover their sense of the contact pressure, the surface texture, and the object temperature as well as to perform thermal withdrawal reflexes using the solution developed in this work

Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis

Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputees’ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness. Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all users’ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputees’ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjects’ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks. Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience. Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

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

Investigation into the control of an upper-limb myoelectric prosthesis

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN053608 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

Providing feedback from a myoelectric hand prosthesis to the user by the electrical stimulation

Transradijalna amputacija je dramatičan događaj koji rezultuje trajnim gubitkom motornih i senzorskih funkcija šake. Primenom mioelektričnih proteza moguće je u određenoj meri nadoknaditi izgubljene motorne funkcije. Iako su danas komercijalno dostupne različite multifunkcionalne proteze, veliki broj pacijenata ne koristi mioelektrične proteze, a kao jedan od glavnih nedostataka navode izostanak senzorskih povratnih informacija sa proteze. Zatvaranje povratne sprege bi direktno uticalo na poboljšanje kvaliteta mioelektrične kontrole i u velikoj meri omogućilo korisniku da oseća i prihvati mehanički sistem kao deo svog tela. Jedan od mogućih pristupa ovom problemu je senzorska supstitucija, odnosno prikupljanje relevantnih informacija sa proteze i njihovo prenošenje korisniku aktiviranjem preostalih senzorskih struktura. Često korišćena neinvazivna metoda supstitucije jeste električna stimulacija povorkama strujnih impulsa malih amplituda, kojima se preko površinskih elektroda aktiviraju aferentni nervi u ostatku podlaktice. Modulacijom parametara stimulacije (npr. amplituda, frekvencija, impulsna širina) je moguće aktivirati različite senzorske receptore u koži koji rezultuju osećajima različitog kvaliteta i jačine, i na taj način preneti informacije sa proteze. Povratne informacije se mogu preneti korisniku i promenom lokacije stimulusa, odnosno prostornom modulacijom, koja zahteva primenu višekanalnog interfejsa...Transradial hand amputation is a dramatic event that results in permanent loss of motor and sensory functions. Myoelectric prostheses can be used to restore the missing motor functions to a certain degree. Although there are numerous commercially available multifunctional prostheses, many patients reject myoelectric prostheses, and state that one of major drawbacks is the lack of sensory feedback from the prosthesis. Closing the loop would directly improve the quality of myoelectric control, but also help users to feel and accept the mechanical system as part of their own body. One approach to this problem is sensory substitution, based on gathering relevant information from the prothesis and transmitting it to the user by activating the remaining sensory structures. Commonly used non-invasive substitution method is electrical stimulation with small amplitude current pulses, which activate afferent nerves in the remaining of the forearm via surface electrodes. Through the modulation of stimulation parameters (e.g. amplitude, frequency, pulse width), sensations of different quality and intensity can be elicited by activating different sensory receptors in the skin, thus transmitting the information from the prosthesis. Feedback can also be provided to the user by changing the location of the stimulus (spatial modulation) which requires the use of a multi-channel interface..