Human Finger Joints Research Articles

Multimodal, Ultrasensitive, and Biomimetic Electronic Skin Based on Gradient Micro‐Frustum Ionogel for Imaginary Keyboard and Haptic Cognition

AbstractElectronic skins (E‐skins) are poised to revolutionize human interaction not only with one another but also with machines, electronics, and surrounding environment. However, the wearable E‐skin that simultaneously offers multiple sensing capabilities, high sensitivity, and broad sensing ranges remains a great challenge. Here, drawing inspiration from human haptic perception, a multimodal, ultrasensitive, and biomimetic E‐skin (MES) founded on micro‐frustum ionogel is developed based on iontronic capacitive and triboelectric effects for imaginary keyboard and multifunctional haptic cognition. Leveraging simultaneously the ionogel as capacitive layer and triboelectric layer, the MES enables human‐dermis perception performances of high sensitivity (357.56 kPa−1), low limit of detection (0.47 Pa), and broad linear detection range (0–500 kPa). Moreover, human finger joint movements can be precisely monitored by the attached MES and be transferred into accurate typed letter information on an imaginary keyboard. More importantly, by harnessing signal acquisition/processing circuits and machine learning, the real‐time haptic cognition of different materials, surface roughness, and contact pressure can be achieved by the MES, which endows the advancement of interaction between next‐generation intelligent robot and physical environment. Consequently, the proposed MES demonstrates impressive potentials in the fields of wearable electronics, human–machine interaction (HMI), and Artificial Intelligence (AI).

Detection of human finger joints in ultrasound images: structure and optimization

Synovitis is the inflammation of a synovial membrane surrounding a joint. Its assessment is an important step in the diagnosis and treatment of rheumatoid arthritis. Joint detection is the first stage of an automated method of assessment of a degree of synovitis, from an Ultrasound (USG) image of a finger joint and its surrounding area. A joint detector consists of three parts: image preprocessing, feature extraction, and classification. Each part contains adjustable parameters that must be set experimentally to ensure the proper operation of the detector. Both the structure of a joint detector and a procedure for finding a near-optimal configuration of the adjustable parameters are described. The optimization process is based on two evaluation measures: Area Under the Receiver Operating Characteristic Curve (AUC) and False Positive Count (FPC). The optimization process decreases the number of pictures with multiple detections, which was the main point of works presented in this paper. This was achieved by increasing the number of components of the homogeneous mixed-SURF descriptor which has the greatest influence on the final result. Non-SURF descriptors achieve poorer classification results. Our research led to the creation of a better joint detector which could positively influence the final results of inflammation level classification.

Research on the grasping characteristics of composite-driven double-finger flexible manipulator.

Aiming at the flexible manipulator grasping complex target parts of different shapes, a double-finger flexible manipulator model is proposed. The manipulator is composed of a flexible mechanical finger, a driving component and a position compensation mechanism. It imitates the motion characteristics of human finger joints and follows the principle of double-fingered grasping. According to the structural characteristics of the target contour, the grasping features of the target contour are extracted. Through the motion principle of the flexible manipulator, the kinematic model of the envelope clamping process of the manipulator target is carried out. Finally, the flexible manipulator experimental platform is built to verify the grasping characteristics of the flexible manipulator target. The results show that the manipulator can realize the adaptive grasping of cylinder and cuboid parts, which have high grasping reliability and stability.

Bimodal buckling governs human fingers' luxation.

Equilibrium bifurcation in natural systems can sometimes be explained as a route to stress shielding for preventing failure. Although compressive buckling has been known for a long time, its less-intuitive tensile counterpart was only recently discovered and yet never identified in living structures or organisms. Through the analysis of an unprecedented all-in-one paradigm of elastic instability, it is theoretically and experimentally shown that coexistence of two curvatures in human finger joints is the result of an optimal design by nature that exploits both compressive and tensile buckling for inducing luxation in case of traumas, so realizing a unique mechanism for protecting tissues and preventing more severe damage under extreme loads. Our findings might pave the way to conceive complex architectured and bio-inspired materials, as well as next generation artificial joint prostheses and robotic arms for bio-engineering and healthcare applications.

Wax-shaped wettability assisted patterning of silver nanowires on various substrates as transparent, flexible, or stretchable electrodes

With the rapid development of flexible and wearable devices, flexible transparent conductive electrodes are highly demanded. However, there is no available technology that can pattern emerging electric materials (e.g., silver nanowires (AgNWs)) on versatile substrates in large scale to achieve high performance in different aspects on demands, such as transparency, flexibility, stretchability, and biocompatibility. Here, a wax-shaped wetting/dewetting (WSWD) method is demonstrated to pattern AgNWs networks into transparent conductive circuits ranging from tens of micrometers to centimeters on various substrates. For instance, the AgNWs coatings patterned on polyethylene terephthalate (PET) films achieve high electrical conductivity and optical transparency up to 73%, and a stable resistance (CV < 0.1%) after cyclic bending (>200 times); the AgNWs coatings patterned into polydimethylsiloxane (PDMS) microchannels exhibit good durability after cyclic bending (>200 times) and stretching (30% strain). Finally, the flexible AgNWs coatings are used as wearable transparent heaters applied to the human finger joint and wrist, which are thermally stable and safe (42 °C < T < 50 °C for 3 min) under cyclic bending. Thus, this method provides a simple, low-cost, green, and versatile means for fabricating transparent conductive electrodes, which can be applied to optoelectronic, wearable, and healthcare devices.

Free scan real time 3D ultrasound imaging with shading artefacts removal

Ultrasound imaging (USI) is a widely adopted imaging method in clinical diagnosis owing to its low cost, convenience, and safety. However, due to the complex acoustic attenuation, two-dimensional (2D) USI lacks the capability to achieve a clear imaging result when the target is shaded by high echo tissues. This paper proposes a three-dimensional (3D) free-scan real-time ultrasound imaging (FRUSI) method. By integrating 2D ultrasound image sequences around the region of interest (ROI) with a real-time and spatially accurate probe tracking method, the proposed FRUSI system provides clear and accurate ultrasound images for medical study. The experiment results on reconstruction precision and accuracy show the potential ability of our proposed system to provide high-quality 3D ultrasound imaging. Moreover, previously shaded targets can be discerned clearly in the same scan plane in both phantom studies and in vivo studies on the human finger joint. The performance of the proposed FRUSI system has demonstrated its potential value for clinical diagnosis to provide high ultrasound imaging quality and rich details in spatial information. Due to the convenient setup, the FRUSI system might potentially be expanded to other ultrasound imaging modalities.

Soft Wearable Piezoresistive Sensors Based on Natural Rubber Fabricated with a Customized Vat-Based Additive Manufacturing Process.

Piezoresistive sensors for monitoring human motions are essential for the prevention and treatment of injury. Natural rubber is a material of renewable origin that can be used for the development of soft wearable sensors. In this study, natural rubber was combined with acetylene black to develop a soft piezoresistive sensing composite for monitoring the motion of human joints. An additive manufacturing technique based on stereolithography was used, and it was seen that the sensors produced with the method could detect even small strains (<10%) successfully. With the same sensor composite fabricated by mold casting, it was not possible to detect low strains reliably. TEM microscopy revealed that the distribution of the filler was not homogeneous for the cast samples, suggesting a directionality of the conductive filler network. For the sensors fabricated through the stereolithography-based method, a homogeneous distribution could be achieved. Based on mechano-electrical characterization, it was seen that the samples produced with AM combined the ability to endure large elongations with a monotonic sensor response. Under dynamic conditions, the sensor response of the samples produced by 3D printing showed lower drift and lower signal relaxation. The piezoresistive sensors were examined for monitoring the motion of the human finger joints. By increasing the bending angle of the sensor, it was possible to increase the sensitivity of the response. With the renewable origin of natural rubber and manufacturing method, the featured sensors can expand the applicability of soft flexible electronics in biomedical applications and devices.

A bioinspired 3D-printable flexure joint with cellular mechanical metamaterial architecture for soft robotic hands.

Compliant flexure joints have been widely used for cable-driven soft robotic hands and grippers due to their safe interaction with humans and objects. This paper presents a soft and compliant revolute flexure joint based on the auxetic cellular mechanical metamaterials with a heterogeneous structure. The heterogeneous architecture of the proposed metamaterial flexure joint (MFJ), which is inspired by the human finger joints, provides mechanically tunable multi-stiffness bending motion and large range of bending angle in comparison to conventional flexure joints. The multi-level variation of the joint stiffness over the range of bending motion can be tuned through the geometrical parameters of the cellular mechanical metamaterial unit cells. The proposed flexure joints are 3D printed with single flexible material in monolithic fashion using a standard benchtop 3D printer. The application of the MFJ is demonstrated in robotic in-hand manipulation and grasping thin and deformable objects such as wires and cables. The results show the capability and advantages of the proposed MFJ in soft robotic grippers and highly functional bionic hands.

ARC Joint: Anthropomorphic Rolling Contact Joint With Kinematically Variable Torsional Stiffness

As compliant joints not only compensate for the lack of actuated degrees of freedom of an under-actuated system and improve grasp stability but also prevent system failure from unexpected contacts, various types of compliant joints have been applied to end-effectors. Although joint compliance increases the success rate of power grasping, when the finger wraps around large objects, it can reduce the grasping success rate in pinch gripping when dealing with small objects using the fingertips. To overcome this drawback, we propose a novel rolling contact joint, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anthropomorphic rolling contact joint</i> , that mimicked the structures of the human finger joint, tongue-and-groove, and collateral ligaments. The anthropomorphic rolling contact joint can passively adjust the torsional stiffness according to the joint angle without additional weight and space. With the anthropomorphic rolling contact joint, flexing the fingers for pinch grasping increases torsional stiffness of the joints and improves grasping stability. In this study, the characteristics and performance of anthropomorphic rolling contact joints were experimentally analyzed by varying the tongue-and-groove shape. Furthermore, it was demonstrated that the three-finger gripper with high torsional stiffness stably grasps small objects in the pinch grasping posture. In addition, because it is easy to manufacture, an anthropomorphic rolling contact joint has advantages in terms of maintenance.

Stretchable Polyaniline@Epoxidized Natural Rubber Composites with Strong 3D Conductive Networks for High Performance Strain Sensors

AbstractFlexible strain sensors are one of the core devices in wearable electronics. At present, it is still difficult to develop flexible strain sensors with high stretchability, sensitivity, and long‐term stability. In this study, polyaniline (PANI) is successfully coated onto the surface of epoxidized natural rubber (ENR) nanospheres to form the PANI@ENR composites with core–shell structure by in situ polymerization. At PANI content of 5 wt% (PANI/ENR), the optimally conductive networks of PANI endow the composites with excellent comprehensive properties. The strain‐sensing tests show that the PANI@ENR composites possess two successive linear sensing ranges (gauge factor of 7.3 in 0–400% strain, gauge factor of 1.8 in 400–1000% strain) and excellent cycling stability (over 2500 cycles at 0–50% strain). In addition, PANI@ENR‐based strain sensors can monitor the movements of human finger joints, elbow joints, and knee joints in real time, indicating a wide range of potential applications in wearable flexible electronics.

Biomechanical analysis of the effect of finger joint configuration on hand grasping performance: rigid vs flexible.

Human finger joints are conventionally simplified as rigid joints in robotic hand design and biomechanical hand modelling, due to their anatomic and morphologic complexity. However, our understanding of the effect of the finger joint configuration on the resulting hand performance is still primitive. In this study, we systematically investigate the grasping performance of the hands with the conventional rigid joints and the biomechanical flexible joints based on a computational human hand model. The measured muscle electromyography (EMG) and hand kinematic data during grasping are used as inputs for the grasping simulations. The results show that the rigid joint configuration currently used in most robotic hands leads to large reductions in hand contact force, contact pressure and contact area, compared to the flexible joint configuration. The grasping quality could be reduced up to 40% and 36% by the rigid joint configuration in terms of algebraic properties of grasping matrix and finger force limit respectively. Further investigation reveals that these reductions are caused by the weak rotational stiffness of the rigid joint configuration. This study implies that robotic/prosthetic hand performance could be improved by exploiting flexible finger joint design. Hand contact parameters and grasping performance may be underestimated by the rigid joint simplification in human hand modelling.

Single-Step 3D Printing of Bio-Inspired Printable Joints Applied to a Prosthetic Hand.

Single-step 3D printing, which can manufacture complicated designs without assembly, has the potential to completely change our design perspective, and how 3D printing products, rather than printing static components, ready-to-use movable mechanisms become a reality. Existing 3D printing solutions are challenged by precision limitations, and cannot directly produce tightly mated moving surfaces. Therefore, joints must be designed with a sufficient gap between the components, resulting in joints and other mechanisms with imprecise motion. In this study, we propose a bio-inspired printable joint and apply it to a Single sTep 3D-printed Prosthetic hand (ST3P hand). We simulate the anatomical structure of the human finger joint and implement a cam effect that changed the distance between the contact surfaces through the elastic bending of the ligaments as the joint flexed. This bio-inspired design allows the joint to be single-step 3D printed and provides precise motion. The bio-inspired printable joint makes it possible for the ST3P hand to be designed as a lightweight (∼255 g), low-cost (∼$500) monolithic structure with nine finger joints and manufactured via single-step 3D printing. The ST3P hand takes ∼6 min to assemble, which is approximately one-tenth the assembly time of open-source 3D printed prostheses. The hand can perform basic hand tasks of activities of daily living by providing a pulling force of 48 N and grasp strength of 20 N. The simple manufacturing of the ST3P hand could help us take one step closer to realizing fully customized robotic prosthetic hands at low cost and effort.

Preparation and tensile conductivity of carbon nanotube/polyurethane nanofiber conductive films based on the centrifugal spinning method

Flexible conductive thin films have recently become a research area of focus in both academia and industry. In this study, a method of preparing nanofiber conductive films by centrifugal spinning is proposed. Polyurethane (PU) nanofiber films were prepared by centrifugal spinning as the flexible substrate film, and carbon nanotubes (CNTs) were used as the conducting medium, to obtain CNTs/PU nanofiber conductive films with good conductivity and elasticity. The effects of different CNT concentrations on the properties of the nanofiber films were investigated. It was found that the conductivity of the nanofiber conductive films was optimal when an impregnation concentration of 9% CNTs was used in the stretching process. Cyclic tensile resistance tests showed that the nanofiber conductive films have good durability and repeatability. Physical and structural property analysis of the CNT/PU conductive films indicate that the adsorption of the CNTs on the PU surface was successful and the CNTs were evenly dispersed on the surface of the matrix. Moreover, the CNTs improved the thermal stability of the PU membrane. The CNT/PU conductive films were pasted onto a human finger joint, wrist joint, and Adam’s apple to test the detection of movement. The results showed that finger bending, wrist bending, and laryngeal prominence movement all caused a change in resistance of the conductive film, with an approximately linear curve. The results indicate that the CNT/PU nanofiber conductive film developed in this study can be used to test the motion of human joints.

Design of Wearable Hand Rehabilitation Glove With Bionic Fiber-Reinforced Actuator.

Background: The hand motor function is lost and activities in daily life (ADLs) are affected due to some illnesses such as stroke and hemiplegia. As a coping way, we present a wearable rehabilitation glove with the bionic actuator for restoring the hand function and the motor control ability lost by stroke patients. Methods: The soft pneumatic bionic actuator (SPBA) is developed on the basis of the research of human hand bone structure and finger joint characteristics and a series of tests are conducted. Besides, we built the rehabilitation glove system based on the proposed SPBAs to verify the availability due to typical gesture, mirror therapy (MT) and grasping experiment for irregular objects. Result: The bending angle of SPBA can reach 260°. The output force of it can reach 5.1N with 0.25 MPa air pressure input. The maximum variance of the bending angle can be concluded at 5.1° in MT. The grasping experiments of the glove worn on the hand or not shows the proposed glove is flexible, the grip force is large and achieve stable grasping of objects. Conclusion: The designed SPBA is satisfied with the requirements of rehabilitation training and the proposed glove restore the normal hand motion of patients in ADLs.

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion.

Capturing hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person’s finger joint. Due to the modular design, the ECSMS’s sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.

A flexible and wearable joint motion sensor using triboelectric nanogenerators for hand gesture monitoring

The rapid advancement in electronic devices and miniaturisation leads to the development of portable and wearable electronic devices for various day-to-day activities. Wearable technology has been rapidly expanding all over the globe due to its flexibility, sensitivity, durability, and multifunctionality as its desirable features. Most of the devices have a higher-end application such as robotics and gesture recognition integrated with artificial intelligence. In this work we propose a Flexion based Triboelectric nanogenerator (F-TENG), which scavenges the mechanical motion of the human finger joints and converts into an electrical signal that can be further programmed using a microcontroller to actuate robotic devices, giving a deeper insight towards self-powered robotic and gesture monitoring applications. The TENG device consists of silicone elastomer as the sensing layer onto which an aluminium electrode is embedded, which enhances the flow of charges between the skin and elastomer and works on the principle of single electrode mode contact electrification. This mode of designing a finger motion sensor is a cost-effective approach, with simple and easy fabrication. The device generates a maximum voltage of ~75 V and ~9 μA current with high power density. The performance analysis proves that F-TENG can serve as a potential candidate for acquiring the joint signals leading to a world of automation.

Video-Rate Ring-Array Ultrasound and Photoacoustic Tomography.

Ultrasonography and photoacoustic tomography provide complementary contrasts in preclinical studies, disease diagnoses, and imaging-guided interventional procedures. Here, we present a video-rate (20 Hz) dual-modality ultrasound and photoacoustic tomographic platform that has a high resolution, rich contrasts, deep penetration, and wide field of view. A three-quarter ring-array ultrasonic transducer is used for both ultrasound and photoacoustic imaging. Plane-wave transmission/receiving approach is used for ultrasound imaging, which improves the imaging speed by nearly two folds and reduces the RF data size compared with the sequential single-channel scanning approach. GPU-based image reconstruction is developed to advance computational speed. We demonstrate fast dual-modality imaging in phantom, mouse, and human finger joint experiments. The results show respiration motion, heart beating, and detailed features in the mouse internal organs. To our knowledge, this is the first report on fast plane-wave ultrasound imaging and single-shot photoacoustic computed tomography in a ring-array system.

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors.

Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible.

A comparative study on water and dry coupling in photoacoustic tomography of the finger joints

We present a systematical study on comparison between water and dry coupling in photoacoustic tomography of the human finger joints. Compared to the direct water immersion of the finger for water coupling, the dry coupling is realized through a transparent PDMS film-based water bag, which ensures water-free contact with the skin. The results obtained suggest that the dry coupling provides image quality comparable to that by water coupling while eliminating the wrinkling of the finger joint caused by the water immersion. In addition, the dry coupling offers more stable hemodynamic images than the water coupling as the water immersion of the finger joint causes reduction in blood vessel size.

Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques

The inputs to the outputs of nonlinear systems can be modeled using machine and deep learning approaches, among which artificial neural networks (ANNs) are a promising option. However, noisy signals affect ANN modeling negatively; hence, it is important to investigate these signals prior to the modeling. Herein, two customized and simple approaches, visual inspection and absolute correlation, are proposed to examine the relationship between the inputs and outputs of a nonlinear system. The system under consideration uses biosignals from surface electromyography as inputs and human finger joint angles as outputs, acquired from eight intact participants performing movements and grasping tasks in dynamic conditions. Furthermore, the results of these approaches are tested using the standard mutual information measure. Hence, the system dimensionality is reduced, and the ANN learning (convergence) is accelerated, where the most informative inputs are selected for the next phase. Subsequently, four ANN types, i.e., feedforward, cascade-forward, radial basis function, and generalized regression ANNs, are used to perform the modeling. Finally, the performance of the ANNs is compared with findings from the signal analysis. Results indicate a high level of consistency among all the aforementioned signal pre-analysis techniques from one side, and they also indicate that these techniques match the ANN performances from the other side. As an example, for a certain movement set, the ANN models resulted in the rotation estimation accuracy of the joints in the following descending order: carpometacarpal, metacarpophalangeal, proximal interphalangeal, and distal interphalangeal. This information has been indicated in the signal pre-analysis step. Therefore, this step is crucial in input–output variable selections prior to machine-/deep-learning-based modeling approaches.