Bionic Hand Research Articles

A study of three-dimensional (3-D) printed prosthetic upper limb models in local context

Abstract Sri Lanka is recovering from a war that left behind many amputees. 3-D printed Flexy, Shira, LimbForge and a bionic hand were produced and analyzed based on gripping force and grasping patterns. LimbForge is preferred by underprivileged for its low cost and realistic look, but non-functionality makes it incompetent. A bionic hand opens to performance enhancement but the cost is high compared to other models. The Flexy and Shira are more promising as they offer functionality at low cost. Experiments were carried out to compare performance. Based on these experiments Flexy showed high grip strength and increased dexterity compared to the Shira hand.

A stretchable dual-mode sensor array for multifunctional robotic electronic skin

A stretchable dual-mode sensor array is developed for applications as multifunctional robotic electronic skin (E-skin). The sensor array, fabricated with cross-grid liquid metal layers as electrodes separated by micro-structured dielectric layers, can act as E-skin sensors under either capacitance mode or triboelectric nanogenerator (TENG) mode. Under capacitance mode, the device can function as proximity sensors or pressure sensors, the design of the top and bottom electrodes geometry leads to optimized proximity sensing performances and the deliberate introduction of hollow micro-pillar microstructures in the dielectric layer enables the E-skin to differentiate applied pressure from tensile strain. Under TENG mode, the contact location and pressure can be determined by scanning the outputs from the top and bottom electrode arrays. The two modes are complimentary to each other for pressure measurements as the capacitive mode is effective for high pressure range (10–120 kPa), while the TENG mode demonstrates high sensitivity (highest 1.04 V/kPa) at low pressure range (<10 kPa). The sensor array is also implemented as E-skin in a bionic hand for tactile sensing.

Research on upper limb biomechanical system

Biomechanical systems for human upper limb have been studied and developed for more than 60 years, due to requirements of improving life quality for people who cannot partially, or totally, use their hand. This paper presents aspects related to preliminary research (kinematic model of thumb, 3D mechanic model, hard-software platform) of a bionic hand that will be done and is intended to be of high sensitivity, good accuracy, low weight, friendly user interface, with efficient command and control system.

Restoring Tactile sensations via neural interfaces for real-time force-and-slippage closed-loop control of bionic hands.

Despite previous studies on the restoration of tactile sensation on the fingers and the hand, there are no examples of use of the routed sensory information to finely control the prosthesis hand in complex grasp and manipulation tasks. Here it is shown that force and slippage sensations can be elicited in an amputee subject by means of biologically-inspired slippage detection and encoding algorithms, supported by a stick-slip model of the performed grasp. A combination of cuff and intraneural electrodes was implanted for eleven weeks in a young woman with hand amputation, and was shown to provide close-to-natural force and slippage sensations, paramount for significantly improving the subject's manipulative skills with the prosthesis. Evidence is provided about the improvement of the subject's grasping and manipulation capabilities over time, thanks to neural feedback. The elicited tactile sensations enabled the successful fulfillment of fine grasp and manipulation tasks with increasing complexity. Grasp performance was quantitatively assessed by means of instrumented objects and a purposely developed metrics. Closed-loop control capabilities enabled by the neural feedback were compared to those achieved without feedback. Further, the work investigates whether the described amelioration of motor performance in dexterous tasks had as central neurophysiological correlates changes in motor cortex plasticity and whether such changes were of purely motor origin, or else the effect of a strong and persistent drive of the sensory feedback.

Bionic hand as artificial organ: Current status and future perspectives

Even though the hand comprises only 1% of our body weight, about 30% of our central nervous systems (CNS) capacity is related to its control. The loss of a hand thus presents not only the loss of the most important tool allowing us to interact with our environment, but also leaves a dramatic sensory-motor deficit that challenges our CNS. Reconstruction of hand function is therefore not only an essential part of restoring body integrity and functional wholeness but also closes the loop of our neural circuits diminishing phantom sensation and neural pain. If biology fails to restore meaningful function, today we can resort to complex mechatronic replacement that have functional capabilities that in some respects even outperform biological alternatives, such as hand transplantation. As with replantation and transplantations, the challenge of bionic replacement is connecting the target with the CNS to achieve natural and intuitive control. In recent years, we have developed a number of strategies to improve neural interfacing, signal extraction, interpretation and stable mechanical attachment that are important parts of our current research. This work gives an overview of recent advances in bionic reconstruction, surgical refinements over technological interfacing, skeletal fixation, and modern rehabilitation tools that allow quick integration of prosthetic replacement.

The quest for a bionic hand: recent achievements and future perspectives

Replacing a missing upper limb with a functional one is an ancient need and desire. Historically, humans have replaced a missing limb with a prosthesis for many reasons, be it cosmetic, vocational, or for personal autonomy. The hand is a powerful tool and its loss causes severe physical and often mental debilitation. The need for a versatile prosthetic limb with intuitive motor control and realistic sensory feedback is huge and its development is absolutely necessary for the near future.

Designing, Manufacturing and Controlling of the Elastic Materials Based Bionic Hand

Designing, Manufacturing and Controlling of the Elastic Materials Based Bionic Hand

EEG Control of a Bionic Hand With Imagination Based on Chaotic Approximation of Largest Lyapunov Exponent: A Single Trial BCI Application Study

This paper investigates a method for imaginary hand fisting pattern recognition based on the electroencephalography (EEG). The proposed method estimate the largest Lyapunov exponent (LLE) chaotic feature that is based on approximation of mutual information (MI) and false nearest neighbor (FNN) methods for reconstructing a phase space. The selected method for MI and FNN approximation approaches is a new version of Tug of War optimization algorithm. The new algorithm utilizes chaotic maps to update candidate solutions. The chaotic approximation of the LLE (CALLE) is the utilized method for extracting the chaotic features and then categorizing features by means of soft margin support vector machine with a generalized radial basis function kernel classifier. Accuracy and paired t-test values are obtained and compared with the traditional LLE method; 18 candidates were participated to record the EEG for imaginary right-hand fisting task. The results show improvements for the CALLE algorithm in comparison with the traditional LLE by achieving a higher accuracy of 68.25%. Feature changes between two imaginary statuses were significant for 17 subjects, and the paired t-test values were (p <; 0.05). From the results, it is concluded that the Tug of War optimization method finds different values to reach a higher accuracy than the traditional LLE method, and the traditional methods for the LLE are not optimum.

Biomimetic encoding model for restoring touch in bionic hands through a nerve interface

Objective. Hand function can be restored in upper-limb amputees by equipping them with anthropomorphic prostheses controlled with signals from residual muscles. The dexterity of these bionic hands is severely limited in large part by the absence of tactile feedback about interactions with objects. We propose that, to the extent that artificial touch mimics its natural counterpart, these sensory signals will be more easily integrated into the motor plan for object manipulation. Approach. We describe an approach to convey tactile feedback through electrical stimulation of the residual somatosensory nerves that mimics the aggregate activity of tactile fibers that would be produced in the nerve of a native hand during object interactions. Specifically, we build a parsimonious model that maps the stimulus—described as time-varying indentation depth, indentation rate, and acceleration—into continuous estimates of the time-varying population firing rate and of the size of the recruited afferent population. Main results. The simple model can reconstruct aggregate afferent responses to a wide range of stimuli, including those experienced during activities of daily living. Significance. We discuss how the proposed model can be implemented with a peripheral nerve interface and anticipate it will lead to improved dexterity for prosthetic hands.

Sensory adaptation to electrical stimulation of the somatosensory nerves

Objective. Sensory systems adapt their sensitivity to ambient stimulation levels to improve their responsiveness to changes in stimulation. The sense of touch is also subject to adaptation, as evidenced by the desensitization produced by prolonged vibratory stimulation of the skin. Electrical stimulation of nerves elicits tactile sensations that can convey feedback for bionic limbs. In this study, we investigate whether artificial touch is also subject to adaptation, despite the fact that the peripheral mechanotransducers are bypassed. Approach. Using well-established psychophysical paradigms, we characterize the time course and magnitude of sensory adaptation caused by extended electrical stimulation of the residual somatosensory nerves in three human amputees implanted with cuff electrodes. Main results. We find that electrical stimulation of the nerve also induces perceptual adaptation that recovers after cessation of the stimulus. The time course and magnitude of electrically-induced adaptation are equivalent to their mechanically-induced counterparts. Significance. We conclude that, in natural touch, the process of mechanotransduction is not required for adaptation, and artificial touch naturally experiences adaptation-induced adjustments of the dynamic range of sensations. Further, as it does for native hands, adaptation confers to bionic hands enhanced sensitivity to changes in stimulation and thus a more natural sensory experience.

Illusory movement perception improves motor control for prosthetic hands.

To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement's progress. This largely nonconscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement. Without the kinesthetic sense, however, these devices do not become intuitively controllable. We report a method for endowing human amputees with a kinesthetic perception of dexterous robotic hands. Vibrating the muscles used for prosthetic control via a neural-machine interface produced the illusory perception of complex grip movements. Within minutes, three amputees integrated this kinesthetic feedback and improved movement control. Combining intent, kinesthesia, and vision instilled participants with a sense of agency over the robotic movements. This feedback approach for closed-loop control opens a pathway to seamless integration of minds and machines.

A Low-Cost Hand Trainer Device Based On Microcontroller Platform

Conventionally, the rehabilitation equipment used in the hospital or recovery center to treat and train the muscle of the stroke patient is implementing the pneumatic or compressed air machine. The main problem caused by this equipment is that the arrangement of the machine is quite complex and the position of it has been locked and fixed, which can cause uncomfortable feeling to the patients throughout the recovery session. Furthermore, the harsh movement from the machine could harm the patient as it does not allow flexibility movement and the use of pneumatic actuator has increased the gripping force towards the patient which could hurt them. Therefore, the main aim of this paper is to propose the development of the Bionic Hand Trainer based on Arduino platform, for a low-cost solution for rehabilitation machine as well as allows flexibility and smooth hand movement for the patients during the healing process. The scope of this work is to replicate the structure of the hand only at the fingers structure that is the phalanges part, which inclusive the proximal, intermediate and distal of the fingers. In order to do this, a hand glove is designed by equipping with flex sensors at every finger and connected them to the Arduino platform. The movement of the hand will motorize the movement of the dummy hand that has been controlled by the servo motors, which have been equipped along the phalanges part. As a result, the bending flex sensors due to the movement of the fingers has doubled up the rotation of the servo motors to mimic this movement at the dummy hand. The voltage output from the bending sensors are ranging from 0 volt to 5 volts, which are suitable for low-cost hand trainer device implementation. Through this system, the patient will have the power to control their gripping operation slowly without having a painful force from the external actuators throughout the rehabilitation process.

Expectation maximization transfer learning and its application for bionic hand prostheses

Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data. Such changes can severely affect the model performance, leading for example to misclassifications of data. This is particularly apparent in the domain of bionic hand prostheses, where machine learning models promise faster and more intuitive user interfaces, but are hindered by their lack of robustness to everyday disturbances, such as electrode shifts. One way to address changes in the data distribution is transfer learning, that is, to transfer the disturbed data to a space where the original model is applicable again. In this contribution, we propose a novel expectation maximization algorithm to learn linear transformations that maximize the likelihood of disturbed data after the transformation. We also show that this approach generalizes to discriminative models, in particular learning vector quantization models. In our evaluation on data from the bionic prostheses domain we demonstrate that our approach can learn a transformation which improves classification accuracy significantly and outperforms all tested baselines, if few data or few classes are available in the target domain.

Case-study of a user-driven prosthetic arm design: bionic hand versus customized body-powered technology in a highly demanding work environment

BackgroundProsthetic arm research predominantly focuses on “bionic” but not body-powered arms. However, any research orientation along user needs requires sufficiently precise workplace specifications and sufficiently hard testing. Forensic medicine is a demanding environment, also physically, also for non-disabled people, on several dimensions (e.g., distances, weights, size, temperature, time).MethodsAs unilateral below elbow amputee user, the first author is in a unique position to provide direct comparison of a “bionic” myoelectric iLimb Revolution (Touch Bionics) and a customized body-powered arm which contains a number of new developments initiated or developed by the user: (1) quick lock steel wrist unit; (2) cable mount modification; (3) cast shape modeled shoulder anchor; (4) suspension with a soft double layer liner (Ohio Willowwood) and tube gauze (Molnlycke) combination. The iLimb is mounted on an epoxy socket; a lanyard fixed liner (Ohio Willowwood) contains magnetic electrodes (Liberating Technologies). An on the job usage of five years was supplemented with dedicated and focused intensive two-week use tests at work for both systems.ResultsThe side-by-side comparison showed that the customized body-powered arm provides reliable, comfortable, effective, powerful as well as subtle service with minimal maintenance; most notably, grip reliability, grip force regulation, grip performance, center of balance, component wear down, sweat/temperature independence and skin state are good whereas the iLimb system exhibited a number of relevant serious constraints.ConclusionsResearch and development of functional prostheses may want to focus on body-powered technology as it already performs on manually demanding and heavy jobs whereas eliminating myoelectric technology’s constraints seems out of reach. Relevant testing could be developed to help expediting this. This is relevant as Swiss disability insurance specifically supports prostheses that enable actual work integration. Myoelectric and cosmetic arm improvement may benefit from a less forgiving focus on perfecting anthropomorphic appearance.

A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study

A bionic hand with fine motor ability could be a favorable option for replacing the human hand when performing various operations. Myoelectric control has been widely used to recognize hand movements in recent years. However, most of the previous studies have focused on whole-hand movements, with only a few investigating subtler motions. The aim of this study was to construct a prototype system for recognizing hand postures with the aim of controlling a bionic hand by analyzing sEMG signals measured at the flexor digitorum superficialis and extensor digitorum muscles. We adopted multiple features commonly used in previous studies—mean absolute value, zero crossing, slope sign change, and waveform length—in the algorithm for extracting hand-posture features, and the k-nearest-neighbors (KNN) algorithm as the classifier to perform hand-posture recognition. The bionic hand was controlled by an Arduino microprocessor, which converted the signals received from the classification process that were fed to the servo motors controlling the bionic fingers. We constructed a two-channel sEMG pattern-recognition system that can identify human hand postures and control a homemade bionic hand to perform corresponding hand postures. The KNN approach was able to recognize four different hand postures with a classification accuracy of 94% in the online experiment by using the channel combination. Moreover, the experimental tests show that the bionic hand could faithfully imitate the hand postures of the human hand. This study has bridged the gap between the features of sEMG signals of fingers and the postures of a bionic hand.

A Case Study of a Force-myography Controlled Bionic Hand Mitigating Limb Position Effect

AbstractForce Myography (FMG), which monitors pressure or radial deformation of a limb, has recently been proposed as a potential alternative for naturally controlling bionic robotic prostheses. This paper presents an exploratory case study aimed at evaluating how FMG behaves when a person with amputation uses a hand prosthetic prototype. One volunteer (transradial amputation) participated in this study, which investigated two experimental cases: static and dynamic. The static case considered forearm muscle contractions in a fixed elbow and shoulder positions whereas the dynamic case included movements of the elbow and shoulder. When considering eleven different hand grips, static data showed an accuracy over 99%, and dynamic data over 86% (within-trial analysis). The across-trial analysis, that takes into account multiple trials in the same data collection set, showed a meaningful accuracy respectively of 81% and 75% only for the reduced six grips setup. While further research is needed to increase these accuracies, the obtained results provided initial evidence that this technology could represent an interesting alternative that is worth exploring for controlling prosthesis.

Autonomous Selection of Closing Posture of a Robotic Hand Through Embodied Soft Matter Capacitive Sensors

Soft matter capacitive sensors are developed and integrated into the silicone skin of finger tips and palm of a robotic hand. Distributed sensing nodes are used for obtaining preliminary pre-touch information about the object that should be grasped. This includes conductive objects taking advantage of proximity sensing from changes in electric field and non-conductive objects taking advantage of pressure sensing from the deforming capacitive electrodes. We argue that this system can eventually address one important challenge in control of multi-DOF bionic hands, i.e., autonomously choosing the right closing pattern. Sensor fabrication, characterization, and integration into the hand are described. An algorithm for detection and activation of closing patterns is suggested, and results are discussed.

A genuine bionic hand

A genuine bionic hand

Simulating tactile signals from the whole hand with millisecond precision

When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.

Kinematic Structures Description of Bionic Hand Based on VF Set

This paper presents a method for describing kinematic structure of bionic hand based on VF (virtual finger) set. At first, a 20 DOFs (degrees of freedom) human hand kinematic model is built, which is expressed by five fingers’ kinematic chains consisting of kinematic pairs and symbols that represent geometric relationships of kinematic pairs’ axes. Based on the concept of VF, the hand fingers are divided into two types: VFAA having adduction/abduction motion and VFFE having flexion/extension motion. The concept of VF set comprising VFAAs and VFFEs is defined, human hand and six basic grasp postures are described by VF set. Then, the structures corresponding VFAA and VFFE are given according to active and passive forms of finger joints, and VFFE Structure-Base comprising 20 conventional structures is built. Based on VF set and the structures of VFAA and VFFE, VF sets and kinematic structures of several classic bionic hands are given.