Finger Joints Research Articles

Associations of inflammatory and metabolic biomarkers with incident erosive hand osteoarthritis in the osteoarthritis initiative cohort

Erosive hand osteoarthritis (eHOA) is a subtype of hand osteoarthritis (OA) that develops in finger joints with pre-existing OA and is differentiated by clinical characteristics (hand pain/disability, inflammation, and erosions) that suggest inflammatory or metabolic processes. This was a longitudinal nested case-cohort design among Osteoarthritis Initiative participants who had hand radiographs at baseline and 48-months, and biospecimens collected at baseline. We classified incident radiographic eHOA in individuals with ≥1 joint with Kellgren-Lawrence ≥2 and a central erosion present at 48-months but not at baseline. We used a random representative sample (n=1282) for comparison. We measured serum biomarkers of inflammation, insulin resistance and dysglycemia, and adipokines using immunoassays and enzymatic colorimetric procedures, blinded to case status. Eighty-six participants developed incident radiographic eHOA. In the multivariate analyses adjusted for age, gender, race, smoking, and body mass index, and after adjustment for multiple analyses, incident radiographic eHOA was associated with elevated levels of interleukin-7 (risk ratio (RR) per SD=1.30 [95% confidence interval (CI) 1.09, 1.55] p trend 0.01). This exploratory study suggests an association of elevated interleukin-7, an inflammatory cytokine, with incident eHOA, while other cytokines or biomarkers of metabolic inflammation were not associated. Interleukin-7 may mediate inflammation and tissue damage in susceptible osteoarthritic finger joints and participate in erosive progression.

A marker-based approach to determine the centers of rotation of finger joints

Background and ObjectiveThe methods proposed in literature to estimate the position of hand joints Centers of Rotation (CoRs) typically require computationally non-trivial optimization routines and exploit a high number of markers to calculate CoRs positions from surface marker trajectories. Moreover, most of the existing works evaluated the accuracy only in simulation. This work proposes a new procedure, based on the Pratt circle fit, to estimate joints CoRs position in 2D through marker-based acquisitions. MethodsThe advantage of the Pratt circle fit lies in its simplicity and computational speed, and in the possibility of exploiting a reduced markerset for calculating CoRs. By applying simplifying assumptions regarding the movement of the fingers (i.e., planar and decoupled flexion-extension movements of each joint occurring in the same flexion plane for all the joints of the finger), it is possible to determine the position of the CoR of each joint in 2D. For this reason, the estimation of the Carpo-MetaCarpal joint of the thumb was not included in this work, as it exhibits a more complex movement associated to the combination of a flexion-extension and adduction-abduction degree of freedom. The errors in estimating CoRs were evaluated by conducting experimental acquisitions on an anthropomorphic robotic hand and comparing the position of the estimated CoR with the real position of the CoR. The repeatability of the method and its capability to estimate anatomically plausible CoRs were evaluated through experimental acquisitions conducted on five healthy volunteers. ResultsErrors in estimating finger joints CoRs were in the order of 0.70 mm and 0.18 mm respectively along the finger longitudinal direction (i.e., x coordinate) and thickness (i.e., y coordinate). Standard Deviations of CoRs positions were comparable to the ones obtained in literature (i.e., below 2 mm and 1 mm respectively for the x and y coordinates), thus demonstrating the repeatability of the method. The Anatomical Plausibility Rate of the proposed approach was between 80% and 100%. ConclusionsThe performance of the Pratt-based CoRs estimation procedure proposed in this work was comparable to other existing methods, with the advantage of exploiting a simple fitting algorithm and a reduced markerset with respect to the state-of-the-art techniques.

Intra- and inter-rater reliability of goniometric finger range of motion using a written protocol.

Goniometric finger range of motion (ROM) is the most common outcome measure used for functional evaluation of finger joints, but its reliability is not well-evaluated. This study aimed to investigate intra- and inter-rater reliability of goniometric finger ROM using a written protocol for active, passive, and composite movements in healthy adults. The design was a single-center, cross-sectional, reliability study. Participants were 20 healthy adults (mean ± standard deviation, 36.4 ± 10.9 years). ROM for active, passive, and composite movements of the fingers was assessed by three occupational therapists with at least 5 years clinical experience in the field of physical disabilities. To standardize the measurement method used, we developed a written protocol, stabilized the wrist position, and trained the evaluators. Intraclass correlation coefficient (ICC) values were used for the reliability analysis. ICC (1,1) was used for intra-rater reliability. ICC (2,1) was used for inter-rater reliability. Hand-shaped heatmaps were used to summarize the reliability data. Most of the results (88.7%) showed moderate to good intra-rater reliability (ICC ≥ 0.50), while inter-rater reliability showed less (69.0%). Both intra- and inter-rater reliability showed no trends between dominant and non-dominant hands, type of movement, finger, or joint. Intra-rater reliability was relatively high and using a written protocol was beneficial. Inter-rater reliability tended to be lower, and differences in the physical structure of both raters and participants may have affected inter-rater reliability values.

RadarHand: A Wrist-Worn Radar for On-Skin Touch-Based Proprioceptive Gestures

We introduce RadarHand, a wrist-worn wearable with millimetre wave radar that detects on-skin touch-based proprioceptive hand gestures. Radars are robust, private, small, penetrate materials, and require low computation costs. We first evaluated the proprioceptive and tactile perception nature of the back of the hand and found that tapping on the thumb is the least proprioceptive error of all the finger joints, followed by the index finger, middle finger, ring finger, and pinky finger in the eyes-free and high cognitive load situation. Next, we trained deep-learning models for gesture classification. We introduce two types of gestures based on the locations of the back of the hand: generic gestures and discrete gestures. Discrete gestures are gestures that start at specific locations and end at specific locations at the back of the hand, in contrast to generic gestures, which can start anywhere and end anywhere on the back of the hand. Out of 27 gesture group possibilities, we achieved 92% accuracy for a set of seven gestures and 93% accuracy for the set of eight discrete gestures. Finally, we evaluated RadarHand’s performance in real-time under two interaction modes: Active interaction and Reactive interaction. Active interaction is where the user initiates input to achieve the desired output, and reactive interaction is where the device initiates interaction and requires the user to react. We obtained an accuracy of 87% and 74% for active generic and discrete gestures, respectively, as well as 91% and 81.7% for reactive generic and discrete gestures, respectively. We discuss the implications of RadarHand for gesture recognition and directions for future works.

Joint angle prediction for a cable-driven gripper with variable joint stiffness through numerical modeling and machine learning

Soft grippers in automation, particularly those with variable joint stiffness, offer promising possibilities for precise manipulation tasks. However, accurately predicting finger joint bending angles in this field poses significant challenges due to the soft and complex nature of the grippers, making modeling and angle prediction difficult. This paper presents the development of a predictive model for precisely controlling bending angles in multi-material printed soft grippers with variable stiffness, which are pivotal for delicate manipulation tasks in automation. In particular, we explore a cable-driven gripper design made of thermoplastic polyurethane and conductive polylactic acid materials, featuring integrated resistive joints for stiffness modulation through controlled Joule heating. A data-driven modeling approach, combining numerical modeling of the gripper and machine learning techniques, was employed for the development of the predictive model. We performed static structural simulations using ANSYS Workbench to measure bending angles under various conditions for developing datasets for model training. In this work, we evaluated several machine learning models such as linear regression, decision tree, and K-nearest neighbor regression models to predict the correlation between temperature, pull distance, and bending angle. The K-nearest neighbor regression model demonstrated the highest accuracy, with a mean absolute error of approximately 11%. These findings underline the importance of precise angle prediction models in enhancing the functionality and reliability of soft grippers, paving the way for their broader application in automation and robotics.

Development of a Novel Compact Robotic Exoskeleton Glove With Reinforcement Learning Control

Abstract This paper presents the design, optimization, control, and experimental evaluation of a novel compact exoskeleton glove aiming to assist patients with brachial plexus injuries in grasping daily used objects. The finger mechanism is based on a rigid coupling hybrid mechanism concept, which utilizes a serially connected rack-and-pinion mechanism and an offset slider-crank mechanism to couple the motions of different finger joints. The glove dimensions are synthesized based on the natural grasping motion of human hands. To better control the glove and enhance the grasping capabilities, a simulation environment was developed and reinforcement learning techniques were applied. This learning-based control trained an agent to perform different grasp types with appropriate force. The trained agent was then applied in real-world experiments with the developed exoskeleton glove. The results validated the effectiveness of the mechanical design and the real-time self-adjustable control policy, which demonstrated the glove’s functionality and capability to grasp various objects relevant to activities of daily living.

Wide strain range and high sensitivity sandwich structure CNTs/AgNWs/CNTs/TPU strain sensors for human motion detection

Nowadays, designing a flexible strain sensor that offers both high sensitivity and a wide operating range has been a significant challenge. However, this paper demonstrates the successful preparation of a sandwich-structured, flexible strain sensor with high sensitivity and wide operating range through vacuum-assisted filtration. The substrate of sensor is composed of electrospinning TPU. The three conductive layers of the sensor consist of CNTs, AgNWs, and CNTs. The AgNWs provide extremely low initial resistance and serve to bridge the individual CNTs islands under large strains. The CNTs/AgNWs/CNTs/TPU flexible strain sensor boasts a high sensitivity (GF=1005.8), a wide strain range (up to 350%), and fast response time (39 ms). It also maintains a long working range and long-term usage stability at 2500 cycles. Additionally, the electrothermal and photothermal performance of the sensor were investigated, which revealed a rapid temperature rise to 93.1 °C at 5 V and 80.8 °C at 500 mW/cm2 irradiation intensity. This sensor can accurately monitor the full range of human motion, from tiny pulse and breathing to large-scale finger joint flexion. These findings suggest that our sensor could have great potential for application in wearable devices as well as body health monitoring.

RFWBS Model: Multilevel Hybrid Mapping Solution Framework for Designing Neurorehabilitation Physiotherapy Devices

Healthcare services and rehabilitation equipment have entered a phase of rapid development driven by user requirements. However, the development of corresponding design models for rehabilitation equipment is lacking. A general framework and development process are urgently needed for neurorehabilitation physiotherapy equipment. To address problems such as inadequate knowledge representation in the design process and difficulties in modeling the functional structure of the product, we combined a decomposition topology model of neurorehabilitation physiotherapy equipment with the modular design method. We proposed a general model for the design of neurorehabilitation physiotherapy equipment comprising the following modules: requirements (R), function (F), principle workspace (W), behavior (B), and structure (S), i.e., the RFWBS model. Using the neurorehabilitation robotic glove as an example for design practice, in terms of kinematics, the mechanism is analyzed by establishing a Lagrangian coordinate system for resolution. The mechanism has three degrees of freedom and can achieve the natural flexion and extension angles of each finger joint. In terms of dynamics, during the entire finger extension, the angular acceleration is almost zero, and the average angular velocity is approximately 30~50°/s. This indicates that the mechanism is suitable for wearable use, validating the scientific and effective nature of the RFWBS expanded model.

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients.

Wearing robotic gloves has become increasingly crucial for hand rehabilitation in stroke patients. However, traditional robotic gloves can exert additional pressure on the hand, such as prolonged use leading to poor blood circulation and muscle stiffness. To address these concerns, this work analyzes the finger kinematic model based on computerized tomography (CT) images of human hands, and designs a low-pressure robotic glove that conforms to finger kinematic characteristics. Firstly, physiological data on finger joint flexion and extension were collected through CT scans. The equivalent rotation centers of finger joints were obtained using the SURF and RANSAC algorithms. Furthermore, the trajectory of finger joint end and the correlation equation of finger joint motion were fitted, and a comprehensive finger kinematic model was established. Based on this finger kinematic model, a novel under-actuated exoskeleton mechanism was designed using a human-machine integration approach. The novel robotic glove fully aligns with the equivalent rotation centers and natural motion trajectories of the fingers, exerting minimal and evenly distributed dynamic pressure on the fingers, with a theoretical static pressure value of zero. Experiments involving gripping everyday objects demonstrated that the novel robotic glove significantly reduces the overall pressure on the fingers during grasping compared to the pneumatic glove and the traditional exoskeleton robotic glove. It is suitable for long-term use by stroke patients for rehabilitation training.

Soft Wearable Body-Powered Hydraulic Actuation System for a Prosthetic Finger Design.

Finger and fingertip loss is the most common form of upper-limb amputation. With a focus on amputations involving the loss of distal and/or partial middle finger segments, this paper outlines the design and development of a novel soft body-powered hydraulically-driven actuation system for a prosthetic finger, while offering an in-depth examination of its subsystems. The proposed device utilises a soft wearable hydraulic mechanism to transfer pressure from the proximal interphalangeal (PIP) joint of the human finger to the distal interphalangeal (DIP) joint of the prosthetic finger, enabling movement of the soft prosthetic DIP joint. The design parameters of the soft actuator, such as its configuration, constituent material, and volume were analysed through experiments with able-bodied participants. Each participant tried 42 different actuators while flexing their index finger, repeating the task four times, yielding 168 trials per participant. The human and prosthetic finger flexion angles and resultant pressures were measured using an Aurora electromagnetic sensor and a fluid pressure transducer. All data was segmented and analysed. Soft actuator designs were selected through statistical analysis of the material (Agilus 30 and Dragon Skin 30), configuration (chambers located underside or around the PIP joint), and volume. The study demonstrated that the selected soft wearable hydraulic mechanism transferred generated pressure from the participant's PIP joint effectively, enabling movement of the prosthetic digit. Our research contributes to current developments in versatile body-powered prosthetic devices, laying the foundations for broad applications in affordable healthcare devices.

A Lightweight Dynamic Hand Orthosis With Sequential Joint Flexion Movement for Postoperative Rehabilitation of Flexor Tendon Repair Surgery.

During the postoperative hand rehabilitation period, it is recommended that the repaired flexor tendons be continuously glided with sufficient tendon excursion and carefully managed protection to prevent adhesion with adjacent tissues. Thus, finger joints should be passively mobilized through a wide range of motion (ROM) with physiotherapy. During passive mobilization, sequential flexion of the metacarpophalangeal (MCP) joint followed by the proximal interphalangeal (PIP) joint is recommended for maximizing tendon excursion. This paper presents a lightweight device for postoperative flexor tendon rehabilitation that uses a single motor to achieve sequential joint flexion movement. The device consists of an orthosis, a cable, and a single motor. The degree of spatial stiffness and cable path of the orthosis were designed to apply a flexion moment to the MCP joint prior to the PIP joint. The device was tested on both healthy individuals and a patient who had undergone flexor tendon repair surgery, and both flexion and extension movement could be achieved with a wide ROM and sequential joint flexion movement using a single motor.

Pulmonary squamous cell carcinoma metastatic to the proximal interphalangeal joint of the right finger: report of a rare case and review of the literature

Pulmonary squamous cell carcinoma metastatic to the proximal interphalangeal joint of the right finger: report of a rare case and review of the literature

Fabrication of a high performance flexible capacitive porous GO/PDMS pressure sensor based on droplet microfluidic technology.

Porous structures are an effective way to improve the performance of flexible capacitive sensors, but the pore size uniformity of porous structures is not easily controlled by current methods, which may affect the inconsistent performance of different batches of sensors. In this paper, a high performance capacitive flexible porous GO/PDMS pressure sensor was prepared based on droplet microfluidic technology. By testing the performance of the sensor, we found that the sensor with a flow rate ratio of 1 : 3 has relatively good performance, with a degree of hysteresis (DH) of 8.64% and a coefficient of variation (CV) of 5.2%. Therefore, we studied the sensor performance based on this process. The result shows that the sensitivity of the flexible capacitive porous GO/PDMS pressure sensor reached 0.627 kPa-1 at low pressure (0-3 kPa), which is significantly higher than that of the pure PDMS thin film sensor (about 0.031 kPa-1) and the porous PDMS pressure sensor (0.263 kPa-1). At the same time, the sensor has a large range with a fast response time of 240 ms and a relaxation time of 300 ms at 30 kPa and an ultra-low detection limit (70 Pa). It can maintain stable operation under continuous force loading/unloading cycles and can respond well to different pressure step changes, so the sensor can be used to detect the movement process of each finger, knee, foot and other joints of the human body. In conclusion, the droplet microfluidic technology can effectively prepare high-performance capacitive flexible porous GO/PDMS pressure sensors.

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.

Bidirectional Support for Individual Finger Joints in Soft Rehabilitation Gloves: Integration of Foldable Pouch Actuators With Modular Elastomeric Actuators

Bidirectional Support for Individual Finger Joints in Soft Rehabilitation Gloves: Integration of Foldable Pouch Actuators With Modular Elastomeric Actuators

Addressing data imbalance in Sim2Real: ImbalSim2Real scheme and its application in finger joint stiffness self-sensing for soft robot-assisted rehabilitation.

The simulation-to-reality (sim2real) problem is a common issue when deploying simulation-trained models to real-world scenarios, especially given the extremely high imbalance between simulation and real-world data (scarce real-world data). Although the cycle-consistent generative adversarial network (CycleGAN) has demonstrated promise in addressing some sim2real issues, it encounters limitations in situations of data imbalance due to the lower capacity of the discriminator and the indeterminacy of learned sim2real mapping. To overcome such problems, we proposed the imbalanced Sim2Real scheme (ImbalSim2Real). Differing from CycleGAN, the ImbalSim2Real scheme segments the dataset into paired and unpaired data for two-fold training. The unpaired data incorporated discriminator-enhanced samples to further squash the solution space of the discriminator, for enhancing the discriminator's ability. For paired data, a term targeted regression loss was integrated to ensure specific and quantitative mapping and further minimize the solution space of the generator. The ImbalSim2Real scheme was validated through numerical experiments, demonstrating its superiority over conventional sim2real methods. In addition, as an application of the proposed ImbalSim2Real scheme, we designed a finger joint stiffness self-sensing framework, where the validation loss for estimating real-world finger joint stiffness was reduced by roughly 41% compared to the supervised learning method that was trained with scarce real-world data and by 56% relative to the CycleGAN trained with the imbalanced dataset. Our proposed scheme and framework have potential applicability to bio-signal estimation when facing an imbalanced sim2real problem.

Research on Tremor Suppression Strategies Under a Constant Current Peripheral Electrical Stimulation Device for Parkinson's Disease.

Tremor, a prevalent symptom in Parkinson's patients, is conventionally treated with medications and craniotomy. However, the associated side effects and high surgical costs pose challenges for some individuals. In this study, a lightweight constant current electrical stimulator was developed, which is driven by the FPGA to control the underlying logic and has multiple programmable stimulation parameters. Clinical experiments involving patients with Parkinson's-related resting tremor symptoms were conducted to assess the efficacy of peripheral electrical stimulation. Two Co-contraction Avoidance Stimulation (CAS) strategies targeting nerves and muscles were proposed to reduce tremors. Four Parkinson's disease (PD) patients were recruited to verify the effectiveness of these strategies. Kinematic data recorded by inertial sensors showed that the radial nerve and muscle intervention strategies reduced the average angular velocity amplitude of finger joints during resting tremor by 75.92% and 82.41%, respectively. Notably, under low-frequency pulse stimulation (100 Hz) focused on muscle interference, a low-intensity current of no more than 8 mA maintained a tremor suppression rate of 59.91% even 5 minutes post-stimulation. Based on the experimental results, it is concluded that the constant current electrical stimulator developed in this study can effectively suppress tremor under specific stimulation strategies. These findings have significant implications for the development of lightweight, wearable tremor suppression devices. The stimulator's adaptability, coupled with its precise control parameters, demonstrates promise for advancing non-invasive and cost-effective tremor management in Parkinson's patients.

Novel Assessment Technique: Ring Gauge Measurement of Finger Joint Circumference Changes in Patients with Rheumatoid Arthritis

Novel Assessment Technique: Ring Gauge Measurement of Finger Joint Circumference Changes in Patients with Rheumatoid Arthritis

Design and Analysis of Modular Gripper Finger Actuated by Antagonistic Wire and Shape Memory Alloy Springs

The traditional underactuated grippers can only passively adapt to the shape contour of the object to complete the envelope or grasping action. In this paper, a wire and shape memory alloy (SMA) spring‐based differential drive gripper finger are proposed, which can actively control the grasping morphology according to the objects to be grasped, and are more in line with the grasping characteristics of the human hand. The wire simulates the flexor muscle, and the SMA and reset springs simulate the extensor muscle of the finger, which together control the finger morphology. According to the principle of moment equilibrium, the static mechanical model of the gripper is established, and the influence of the wire driving force and the equivalent stiffness of the finger joints on the grasping morphology are analysed, and the theoretical grasping force is verified by ADAMS simulation. A comparative analysis of the grasping force between the non‐active and active morphology control is presented. Finally, the experimental system of the gripper is constructed, and the verification of the deformation morphology of the single finger and the gripper’s grasping experiments is completed. The results show that according to the contour size of the object, by actively controlling the wire force of the gripper and the equivalent stiffness of the joints, the enveloping or grasping action for different objects can be completed. In addition, the grasping force of the finger can be improved by actively controlling the grasping morphology.

Dynamics of movement amplitude indicators in joints of patients with rheumatoid arthritis

The purpose of the paper is to evaluate the effectiveness of the technology of rehabilitation measures in patients with rheumatoid arthritis according to the dynamics of the amplitude of movements indicators. Theoretical (analysis and generalization of literary and Internet data), survey, collection of anamnesis, objective examination, anthropometric (goniometry) methods of mathematical statistics. 188 patients with rheumatoid arthritis were examined, of which 156 (83%) were women, 32 (17%) were men, and their average age was 44.9±7.6 years old. The analysis of the goniometric indicators of the joints of hands affected by rheumatoid arthritis after six months of conducted rehabilitation measures showed a significant improvement in the mobility of the radiocarpal joint and hand fingers in the examined patients. In particular, active flexion in the radiocarpal joint significantly increased in patients of the experimental group and was 72.5±6.0º, while in the patients of the control group this indicator was 5.3º less (67.2±5.0º) (р<0.05). A significant result was achieved in restoring flexion of the hand fingers in patients of the experi­mental group: mobility of the fingers in the metacarpal phalangeal joints was 83.2±3.6º, and in the control group it was 73.4±5.0º, in the proximal interphalangeal joints in patients of the experimental group it was 84.1±3.3º, and in the control group – 67.8±4.8º (р<0.05). Positive dynamics of active movements in the foot and finger joints was also observed in pa­tients of both groups at the end of the study. In the patients of the experimental group, the active dorsiflexion in the ankle joint was 27.7±1.4º, the plantar flexion was 41.3±1.8º, which was significantly better than in patients of the control group in which the observed dorsiflexion was 23.4±1.7º, and plantar flexion – 41.3±1.8º (р<0.05). Accordingly, there was a significant increase in the motor function indicators in the metatarsal phalangeal joints of patients in the expe­rimental group: flexion was up to 37.1±1.4º, extension – up to 39.2±1.0º, compared with the indicators in the control group: flexion – up to 33.5±1.2º, extension – up to 36.2±1.1º (р<0.05). The effectiveness of the developed technology is con­firmed by improvement of the results of goniometric indicators in patients of the experimental group compared with the control group.