Design, implementation, and evaluation of a variable stiffness transradial hand prosthesis

We present the design, implementation, and experimental evaluation of a low-cost, customizable, easy-to-use transradial hand prosthesis capable of adapting its compliance. Variable stiffness actuation (VSA) of the prosthesis is based on antagonistically arranged tendons coupled to nonlinear springs driven through a Bowden cable based power transmission. Bowden cable based antagonistic VSA can, not only regulate the stiffness and the position of the prosthetic hand but also enables a light-weight and low-cost design, by the opportunistic placement of motors, batteries, and controllers on any convenient location on the human body, while nonlinear springs are conveniently integrated inside the forearm. The transradial hand prosthesis also features tendon driven underactuated compliant fingers that allow natural adaption of the hand shape to wrap around a wide variety of object geometries, while the modulation of the stiffness of their drive tendons enables the prosthesis to perform various tasks with high dexterity. The compliant fingers of the prosthesis add inherent robustness and flexibility, even under impacts. The control of the variable stiffness transradial hand prosthesis is achieved by an sEMG based natural human-machine interface

A 3-DoF robotic platform for the rehabilitation and assessment of reaction time and balance skills of MS patients

The central nervous system (CNS) exploits anticipatory (APAs) and compensatory (CPAs) postural adjustments to maintain the balance. The postural adjustments comprising stability of the center of mass (CoM) and the pressure distribution of the body influence each other if there is a lack of performance in either of them. Any predictable or sudden perturbation may pave the way for the divergence of CoM from equilibrium and inhomogeneous pressure distribution of the body. Such a situation is often observed in the daily lives of Multiple Sclerosis (MS) patients due to their poor APAs and CPAs and induces their falls. The way of minimizing the risk of falls in neurological patients is by utilizing perturbation-based rehabilitation, as it is efficient in the recovery of the balance disorder. In light of the findings, we present the design, implementation, and experimental evaluation of a novel 3 DoF parallel manipulator to treat the balance disorder of MS. The robotic platform allows angular motion of the ankle based on its anthropomorphic freedom. Moreover, the end-effector endowed with upper and lower platforms is designed to evaluate both the pressure distribution of each foot and the CoM of the body, respectively. Data gathered from the platforms are utilized to both evaluate the performance of the patients and used in high-level control of the robotic platform to regulate the difficulty level of tasks. In this study, kinematic and dynamic analyses of the robot are derived and validated in the simulation environment. Low-level control of the first prototype is also successfully implemented through the PID controller. The capacity of each platform is evaluated with a set of experiments considering the assessment of pressure distribution and CoM of the foot-like objects on the end-effector. The experimental results indicate that such a system well-address the need for balance skill training and assessment through the APAs and CPAs

sEMG-based natural control interface for a variable stiffness transradial hand prosthesis

We propose, implement, and evaluate a natural human-machine control interface for a variable stiffness transradial hand prosthesis that achieves tele-impedance control through surface electromyography (sEMG) signals. This interface, together with variable stiffness actuation (VSA), enables an amputee to modulate the impedance of the prosthetic limb to properly match the requirements of a task while performing activities of daily living (ADL). Both the desired position and stiffness references are estimated through sEMG signals and used to control the VSA hand prosthesis. In particular, regulation of hand impedance is managed through the impedance measurements of the intact upper arm; this control takes place naturally and automatically as the amputee interacts with the environment, while the position of the hand prosthesis is regulated intentionally by the amputee through the estimated position of the shoulder. The proposed approach is advantageous since the impedance regulation takes place naturally without requiring amputees' attention and diminishing their functional capability. Consequently, the proposed interface is easy to use, does not require long training periods or interferes with the control of intact body segments. This control approach is evaluated through human subject experiments conducted over able volunteers where adequate estimation of references and independent control of position and stiffness are demonstrated.Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) ; 219M58

Design and tele-impedance control of a variable stiffness transradial hand prosthesis

According to theWorld Health Organization, only about the half of upper extremity amputees receive prosthetic limbs and only the half of this group consistently use their prosthetic limbs. The prominent reasons that hinder widespread adaptation of prosthetic devices are their high cost, non-intuitive control interface and insufficient dexterity for performing activities of daily living. This dissertation aims to address these challenges and presents the design, implementation, experimental characterization and human subject studies of a low cost, customizable, variable stiffness transradial hand prosthesis controlled through a natural human-machine interface. The transradial hand prosthesis features a low cost, robust, adaptive and lightweight design, thanks to its tendon-driven, under-actuated, compliant fingers and variable stiffness actuation. In particular, the underactuated compliant ngers feature high dexterity by naturally adapting to different object geometries and provide impact resistance. Antagonistically arranged Bowden-cable based variable stiffness actuation enables independent modulation of the impedance and position of the main tendon of prosthesis. Moreover, Bowden-cable based transmission allows for the actuator/ reduction/power module to be opportunistically placed remotely, away from the transradial hand prosthesis, helping significantly decrease the weight of the device. Furthermore, the transradial hand prosthesis, including the compliant fingers, can be implemented through simple and low-cost manufacturing processes, such as 3D printing, and each prosthesis can be customized to ensure an ideal fit to match the needs of the transradial amputee

Data acquisition and feature extraction for classification of prehensile semg signals for control of a multifunctional prosthetic hand

This study focuses on the SEMG (surface electromyography) signals that carry the valuable information of the neuromuscular activity of a muscle and are utilized in the man-machine interfaces such as multi-functional prostheses. The SEMG signals measured from four different muscle groups of the forearm are weak, sophisticated and very sensitive to ambient noise. The first stage of this study is hardware design and implementation for the SEMG measurement. The fundamentals of the design are mainly based on the specifications of the SEMG signal and the factors that affect the signal quality. The second purpose of the thesis is applying various methodologies to the recorded SEMG signal to give meaning to its nature to be used in the further processes. The raw EMG signals have nonlinear characteristics and present useful information if they are quantified. For this purpose, various signal processing methods are applied to the SEMG signal to acquire useful information, features. Features of the signal are extracted to be used for classification of prehensile motions of multi-functional prosthetics. In this part, many algorithms that have been employe as feature extraction methods are compared with respect to their classification performance

Detection of intention level in response to task difficulty from EEG signals

We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI). In particular, we use linear discriminant analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights. We observe that it is possible to classify tasks of varying difficulty based on EEG signals. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. Our experimental results suggest that it is possible to extract the intention level information from EEG signals in response to task difficulty and indicate some level of correlation between EEG and EMG. With a view towards detecting patients' intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies

Detection of motor task difficulty level from EEG data (EEG verisinden motor hareketi zorluk seviyesinin tespiti)

Rehabilitation protocols are used to increase daily life activities of locked-in patients. There are ongoing efforts to use brain-computer interfaces (BCI) in various ways to increase the benefits of such rehabilitation protocols to patients. An interesting claim is that if a system can detect the intention level of a patient and update the daily program according to this patient's motivation, the gain from these rehabilitation protocol can be increased. In this study, a system that records the electroencephalography (EEG) signals of healthy users performing arm movements against two levels of force has been designed based on the assumption that intention level is proportional to the level of motor task difficulty. EEG signals from 7 healthy subjects and 3 channels were recorded while subjects were performing work against two different levels of force. We calculated frequency bands of these channels and applied linear discriminant analysis (LDA) for classification of two environments corresponding to two motor task difficulty levels and resting state

Classification of motor task execution speed from EEG data

It is believed that the obtention of instantaneous intention level from electroencephalogram (EEG) signals and its use as a control signal may increase the benefits gained from the robotic rehabilitation process of stroke patients. This paper investigates a method for classifying the speed of arm movements from EEG recordings of healthy subjects under the assumption that the intention level of a patient may be reflected in motor task execution velocity. Experimental data were collected from eight (four male, four female) healthy volunteers while they were performing right arm movements at two different speeds. We designed an experiment in which the subjects were asked to carry a glass cup in two different environments: nail or cotton. The task speeds for both environments were decided individually by the volunteers; however the nail environment had a maximum speed limit. Participants were warned by a crashing glass audio stimulus if they exceeded the speed limit of the nail environment. As a result, a simple daily life activity was performed at two different speeds as an experimental task. Based on experimental data from eight healthy subjects, we successfully classified two different speed levels and resting state from event related synchronization (ERS) and event related desynchronization (ERD) patterns of EEG signals by linear discriminant analysis (LDA) classifier. Results reveal that LDA can discriminate different velocity levels when six frequency bands of three EEG recording channels were used as the feature vector