Human Limb Research Articles

A modified human shoulder model considering the migration of the humeral head from the scapular glenoid fossa for the design of shoulder exoskeletons

The wearable upper limb exoskeletons designed using the traditional shoulder model have a serious issue related to joint axis misalignment with the human shoulder. The misalignment is primarily due to the limited degrees of freedom model assumed for the shoulder. The glenohumeral joint in the conventional upper limb model is considered a single spherical joint, which, when considering the human anatomy, neglects the humeral head translation from the glenoid fossa of the scapula. This shoulder model cannot be used for designing the wearable upper limb exoskeletons, which can damage the shoulder joint due to interactive forces generated from the human-exoskeleton joint misalignment. This paper presents a modified, anatomically similar, human upper limb model where the single spherical joint of the glenohumeral joint is modified into two spherical joints connected by a prismatic joint. The kinematic and dynamic model of the proposed shoulder model is explained in detail. The elbow reachable space for this model has been calculated and compared with that of the existing models, and it is found to be closer to the actual workspace. The proposed model is simulated with a conventional exoskeleton attachment to show the effect of misalignment during humeral head translation and the benefits of using the proposed model in the design of the shoulder exoskeleton. An exoskeleton test bench that uses the modified shoulder model is also presented.

Progress and development trends of lower limb exoskeleton rehabilitation robots

Abstract. Currently, as the aging population intensifies, many elderly people are trapped by lower limb dysfunctions caused by diseases such as stroke sequelae, spinal cord injuries, and bone injuries. Research on lower limb exoskeleton robots has made progress both domestically and internationally. Foreign lower limb exoskeleton robot technology is very mature and widely applied; although starting later, domestic development in this field has been rapid. This paper studies the key technologies of lower limb exoskeleton robots from several aspects: the degrees of freedom in human lower limb movement, the cyclic movement of human gait, structural design, driving methods, and interactive control. With the continuous advancement of technology, lower limb exoskeleton robots will become more intelligent and focus more on personalized services and wearing comfort in future development, aiming to bring more convenience and help to disabled individuals, rehabilitation patients, and groups with special needs.

Design and analysis of a multi-motion self-balancing lower limb exoskeleton based on flat terrain

In this study, a multi-motion self-balancing lower limb exoskeleton is designed to provide crutches-free rehabilitation training for quadriplegic patients on flat terrain without hand support. First, based on the biomechanical characteristics of the human body and the bionic mechanism configuration of the human lower limb, an exoskeleton with a full-drive series-parallel hybrid structure was established. Secondly, the D-H method, geometric method and Newton iteration method were used to establish the forward and reverse kinematic solutions of the exoskeleton. In addition, the static self-balancing principle was used to complete the planning of the multi-motion self-balancing trajectory. Finally, in order to verify the effectiveness of the proposed method, some numerical simulations were performed in this paper. The theoretical trajectories of the waist, ankle joint and foot center were compared with the simulated trajectories. The research results show that the forward and reverse kinematics model of the exoskeleton is feasible. Under the condition of contact force parameters, the exoskeleton has the ability of multi-motion self-balancing walking on flat terrain.

Human Muscle Inspired Anisotropic and Dynamic Metal Ion-Coordinated Mechanically Robust, Stretchable and Swelling-Resistant Hydrogels for Underwater Motion Sensing and Flexible Supercapacitor Application.

Mechanically robust and anisotropic conductive hydrogels have emerged as crucial components in the field of flexible electronic devices, since they possess high mechanical properties and intelligent sensing capabilities. However, the hydrogels often swell on exposure to aqueous medium because of their hydrophilicity, which compromises their mechanical properties. Additionally, the hydrogels' isotropic polymeric networks demonstrate isotropic ion transport, which significantly diminishes the sensing capabilities of electrical devices based on hydrogels. These factors greatly limit their use in flexible and wearable sensors. In this study, we have developed poly(acrylamide-co-maleic acid-co-butyl acrylate) based anisotropic hydrogels by prestretching and drying, followed by ionic cross-linking to fix the alignment. The anisotropic arrangement of the polymer network resulted in significant improvements in mechanical performance and electrical conductivity along the prestretching direction. This anisotropic hydrogel combines hydrophobic and metal ion-ligand interactions, enhancing the maximum tensile strength up to 11 MPa along the prestretching direction, about 3 times higher than in the perpendicular direction. The optimized 200% prestretched hydrogel exhibited high tensile strength (7 MPa), flexibility (fracture strain 370%), high toughness (16 MJ m-3) and antiswelling behavior in water (equilibrium swelling ratio 2% after 15 days). alongside higher conductivity (3 times higher) and strain sensing ability (4 times higher gauge factor) along the prestretching direction. The hydrogel demonstrated efficient and stable underwater sensing for underwater communication and to monitor human limb position and movement. The anisotropic hydrogel electrolyte-based flexible supercapacitor exhibited 117 Fg-1 specific capacitance at 0.5 Ag-1, and maximum energy density 5.85 Whkg-1, significantly higher than the corresponding values for the isotropic hydrogel-based device (88 F g-1 and 4.4 Whkg-1, respectively). This hydrogel mimics the structural design of unidirectionally oriented muscle fibers, showing better direction dependent functional properties than the corresponding isotropic hydrogel. The anti-swelling ability and retention of mechanical and conductive properties of these hydrogels in aqueous environment suggest long-term usage capability of these functional materials.

New insight into the development of synpolydactyly caused by expansion of HOXD13 polyalanine based on weighted gene co-expression network analysis

BackgroundSynpolydactyly (SPD) is mainly caused by mutations of polyalanine expansion (PAE) in the transcription factor gene HOXD13 and the involved cell types and signal pathway are still not clear possible pathways and single-cell expression characteristics of limb bud in HOXD13 PAE mice was analyzed in this study.MethodWe investigated a previous study of a mouse model with SPD and conducted weighted gene co-expression network analysis (WGCNA) using a single-cell RNA sequencing dataset from limb bud cells of SPD mouse model of HOXD13 + 7A heterozygote.ResultsAnalysis of WGCNA revealed that synpolydactyly-associated Hoxd13 PAEs alter the immune response and osteoclast differentiation, and enhance DNA replication. Bmp4, Hand2, Hoxd12, Lnp, Prrx1, Gmnn, and Cdc6 were found to play potentially key roles in synpolydactyly.ConclusionsThese findings evaluated the main genes related to SPD with PAE mutations in HOXD13 and advance our understanding of human limb development.

Instantaneous Tiltmeter Triggered by Dynamic Wetting Behavior.

A novel instantaneous tiltmeter with dynamic and static monitoring functions is reported that is based on liquid metal dynamic wetting behavior in a bio-fabricated anisotropic microchannel. The proposed system achieves instantaneous tiltmeter functionality, offering a broad detection range (-90°-90°) with high precision (0.05°), a rapid reaction time (0.11 s), and enhanced durability. Moreover, a seamless integration has enabled water wave detection, language programming, and human limb monitoring. Especially, the integration of tiltmeter and a t3D motion platform results in a surface structure scanning system capable of effectively performing large area (>200 cm2) and height difference scanning functions. This innovative approach holds great potential for transformative changes in the fields of advanced manufacturing, flexible robotics, and the flexible sensing, further facilitating widespread adoption.

Lower Limb Rehabilitation Exoskeleton Robots, A review

Using a lower limb exoskeleton for rehabilitation (LLE) Lower limb exoskeleton rehabilitation robots (LER) are designed to assist patients with daily duties and help them regain their ability to walk. Even though a substantial portion of them is capable of doing both, they have not yet succeeded in conducting agile and intelligent joint movement between humans and machines, which is their ultimate goal. The typical LLE products, rapid prototyping, and cutting-edge techniques are covered in this review. Restoring a patient's athletic prowess to its pr-accident level is the aim of rehabilitation treatment. The core of research on lower limb exoskeleton rehabilitation robots is the understanding of human gait. The performance of common prototypes might be used to match wearable robot shapes to human limbs. To imitate a normal stride, robot-assisted treatment needs to be able to control the movement of the robot at each joint and move the patient's limb.

Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG.

Human gait motion intention recognition is very important for the lower extremity exoskeleton robot to accurately synchronize and respond to the user's natural motion. And motion intention recognition is generally performed through sEMG. Deep learning neural networks perform well in dealing with high-dimensional data and nonlinear relationships such as sEMG, but different deep learning neural networks have their own advantages in dealing with different types of data. Therefore, a multi-branch deep learning neural network, which enables different neural networks to process different feature items, could achieve more accurate and efficient motion intention recognition. The purpose of this study is to 1) Establish a multi-branch deep learning neural network model to achieve accurate gait recognition and effective estimation of joint angles. 2) Quantify the performance of the multi-branch deep learning neural network model in gait recognition and joint angle prediction using sEMG. This study involved the collection of sEMG and plantar pressure data during walking in human subjects. Firstly, the collected signals are filtered and denoised to ensure the quality and reliability of the data. Calculate the time domain features and the frequency domain features to capture the key information of gait. Then, using the sensitivity difference of different structural neural networks to different feature data, a multi-branch deep learning neural network model is developed, in which the extracted features are used as the input of the model. The output of the model includes gait cycle and joint angle, so as to realize the accurate recognition of human gait and the effective estimation of joint angle. The results show that the proposed method has high accuracy in identifying human gait and estimating joint angles. The multi-branch neural network model successfully integrates time-domain and frequency-domain features and provides reliable prediction of gait cycle and joint angle. The highest accuracy of gait recognition is 95.42%, the lowest is 90.11%, and the average is 92.16%. The average error of joint angle estimation is 3.19. This study designed a human walking gait recognition and joint angle prediction model to achieve accurate human lower limb motion intention recognition.The model can be integrated into the sEMG sensor to design a angular biosensors, which can predict the human joint angle in real time.

Generation of human expandable limb-bud-like progenitors via chemically induced dedifferentiation.

In certain highly regenerative animals, cellular dedifferentiation occurs after injury, allowing specialized cells to become progenitor cells for regeneration. However, this capacity is restricted in human cells due to reduced plasticity. Here, we introduce a chemical-induced dedifferentiation approach that reverts the differentiated cells to a progenitor-like state, conferring the features of human limb bud cells from human adult somatic cells. These chemically induced human limb-bud-like progenitors (hCiLBP cells) show a high degree of transcriptomic similarity to human embryonic limb bud progenitors. Importantly, we established culture conditions that allow hCiLBP cells to undergo extensive expansion while maintaining population homogeneity and long-term self-renewal capacity. Moreover, hCiLBP cells exhibit increased osteochondrogenic differentiation ability, providing an innovative platform for generation of skeletal lineage cell types. These results highlight a potential therapeutic approach for repairing damaged human tissues through reversal of developmental pathways from mature cells to expandable progenitor cells.

Structural Design and Experimental Analysis of the Self-Balancing Lower Limb Exoskeleton Robot

To facilitate walking rehabilitation training for individuals with lower limb paralysis, a self-balancing exoskeleton robot with 12 degrees of freedom was conceived. The principal structural design was conducted in line with the biomechanics of the human lower limbs, and a kinematic model was formulated. The stipulated gait was resolved by reverse kinematics in MATLAB to derive the joint angle actuation curves. These curves served as the motive input in ADAMS kinematic simulation experiments, yielding a gait trajectory with an error margin of less than 2 mm compared to the prearranged gait, which is within a reasonable range of deviation. Experiments involving walking with the exoskeleton were also executed. The analysis of the six-axis force sensor data from the sole demonstrated that the ground reaction force curve consistently remained within the bounds of the foot’s support area, substantiating the exoskeleton’s capability for stable ambulation with a load. The simulations and walking experiments together verified the soundness of the exoskeleton’s structural design.

Self-powered human movement measurement with a unidirectional ratchet driven wearable energy harvester

A large amount of energy is generated during human movement which is mostly not effectively utilized. To collect human movement energy and utilize it rationally, a wearable piezoelectric-electromagnetic energy harvester (WPEEH) driven by a unidirectional ratchet is proposed, which adopts the principle of up-conversion to convert mechanical energy into electrical energy. The electromagnetic power generator unit is used to obtain energy to power the sensing circuit, while piezoelectric generator unit can be used for both energy generation and self-powered sensing. A spring push rod and a ratchet structure are used to convert the swinging of the human limb into the unidirectional rotation of the device to enhance its energy harvesting efficiency. The theoretical model of the WPEEH is established, and the experimental test platform is built. The results show that the WPEEH is able to generate a maximum power of 17.2 mW at an oscillation frequency of 1.5 Hz. In the actual wearable test, the proposed energy harvester is able to supply the harvested energy to the low-power electronic devices through capacitors, and it enables to light up 42 LEDs as well as to drive the digital thermo-hygrometer to work continuously and stably. Apart from having excellent properties of a high output power and low drive frequency, the proposed energy harvester also exhibits a high energy density (7.1 × 10–5 W/cm3). What’s more, the speed of human movement can be predicted and calculated for different individuals by testing the results of the swing frequency with a maximum error of 2.71 %. The investigation proves that the proposed WPEEH can measure human movement and has great potential in the field of power supply for low-power electronic devices.

Multiphase soft metal enabled high-performance fabric-based wearable energy harvesting

Highly efficient and flexible power sources have always been the focus and goal in the field of wearable electronics. However, the existing wearable power sources are limited by their large size, high weight, electrolyte leakage, and poor mechanical compliance, which seriously hinders their practical applications. Herein, we propose a novel strategy to achieve and demonstrate a fabric-based high-performance wearable triboelectric nanogenerator (TENG) structure. The metals of gallium (Ga), bismuth (Bi), indium (In), and tin (Sn) are mixed according to specific mass ratios, and then they are transformed in liquid phase to form room-temperature multiphase soft metals of GaBiInSn. The material exhibits both metallic and fluidic properties, and possesses a characteristic of multiphase structure. The GaBiInSn material is directly deposited between polytetrafluoroethylene (PTFE) layers and nonwoven fabrics to establish multi-level conductive and frictional interfaces, creating charge transfer and solid-liquid hybrid dielectric layers. Therefore, the thin film-type triboelectric nanogenerators with a structure of PTFE/GaBiInSn/nonwoven fabric (LM-P-TENGs) is manufactured. The LM-P-TENGs can be integrated onto fabrics without compromising their breathability, comfortableness, and flexibility. Particularly, LM-P-TENGs efficiently convert the mechanical energy of human limb movements into sustainable electrical energy output. Under the human limb mechanical triggering conditions, LM-P-TENG achieves a champion specific open-circuit voltage up to 900 V, peak short-circuit current density of 43.3 mA/m2, and high power density of 12.56 W/m2. This work demonstrates the application potential of room-temperature multiphase soft materials in flexible wearable power sources, while introducing a novel power supply mode for wearable electronics. Additionally, the LM-P-TENGs also present applications in self-powered wearables, medical biosensing systems, human-machine interaction systems, near-eye display systems, etc. for the flexible electronics.

Surgical Anatomy of the Supraretinacular Fat Pad: Sensory Innervation and Preservation in Open Carpal Tunnel Release

BACKGROUND AND OBJECTIVES: Postoperative pain may occur following open carpal tunnel release (OCTR). Various causes have been postulated. During OCTR, adipose tissue located between the palmar aponeurosis and the flexor retinaculum is exposed. It is unknown whether damage to this pad of supraretinacular fat (SRF) might contribute to postoperative palmar pain or tenderness. We studied the sensory innervation of the SRF exposed in OCTR to assess whether SRF damage could potentially generate pain. METHODS: A microanatomic dissection of the innervation and vascular supply of the SRF was performed in 25 embalmed human cadaveric upper limbs. Eight fat pads were removed en bloc for histological evaluation. Three-dimensional reconstructions were made based on immunohistochemically stained sections using computer-assisted microscopy. RESULTS: The SRF is the radial continuation of the hypothenar fat pad that covers the neurovascular bundle in the Guyon canal. The fat pad is richly innervated and contains Pacinian corpuscles. The sensory innervation originates exclusively from the ulnar nerve (palmar branch) and its vascular supply from the ulnar artery. The integrity of the SRF can be preserved by detaching it from the flexor retinaculum in a radial to ulnar fashion. CONCLUSION: The SRF, which is exposed during OCTR, is richly innervated by sensory fibers from the ulnar nerve. It is the radialmost extension of the hypothenar fat pad. In view of its rich innervation, damage to the SRF during OCTR might generate postoperative pain. Preserving its integrity during OCTR is technically possible and even simplifies the procedure. Clinical trials are needed to corroborate whether preservation of the SRF during OCTR indeed makes a clinical difference in postoperative pain generation.

Humanoid Robot Teleoperation Through 3-D Pose Estimation

The knowledge of how the human motions is performed helps to understand how the human body works. This paper presents a method to estimate the human limbs angles through a kinematic model depicted by Roll-Pitch-Yaw rotationmatrix and the mimic of those angles on a humanoid robot. The advantage of this model is the detailed representation of each joint movement in the coordinate axes (x, y, z). The angles estimation is made with the information provided by algorithms of artificial vision and artificial intelligence. In order to reduce the latency between the human motion capture and robot motions, a fuzzy logic controller is implemented in order to control each robot joint. The final robot limbs angles are compared with the human angles in order to obtain the final error between those measurements. This method shows a similar result on the arms posture regarding previous works.

Inertial Motion Capture-Driven Digital Human for Ergonomic Validation: A Case Study of Core Drilling.

In the evolving realm of ergonomics, there is a growing demand for enhanced comfortability, visibility, and accessibility in the operation of engineering machinery. This study introduces an innovative approach to assess the ergonomics of a driller's cabin by utilizing a digital human. Through the utilization of inertial motion capture sensors, the method enables the operation of a virtual driller animated by real human movements, thereby producing more precise and realistic human-machine interaction data. Additionally, this study develops a simplified model for the human upper limbs, facilitating the calculation of joint forces and torques. An ergonomic analysis platform, encompassing a virtual driller's cabin and a digital human model, is constructed using Unity 3D. This platform enables the quantitative evaluation of comfortability, visibility, and accessibility. Its versatility extends beyond the current scope, offering substantial support for product development and enhancement.

Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals

The human lower limb movements recognition (LLMR) plays a pivotal role in active lower limb exoskeleton robots. Employing surface electromyography (sEMG) signals for LLMR allows for the convenient, rapid and stable capture of signal variations, facilitating efficient identification of lower limb motion patterns. However, current sEMG-based LLMR methods face challenges such as incomplete feature extraction, limited contextual information and restricted feature extraction scales during feature extraction. This paper proposed a LLMR method based on Gramian Angular Fields (GAF) and multiscale fusion of Residual Neural Network (MS-ResNet). The denoised sEMG time series was transformed into Gramian Angular Difference Field (GADF) matrix based on GAF. The MS-ResNet model, incorporating ResNet and multiscale feature fusion concepts, was proposed to comprehensively capture global and local information through different-scale feature extraction and fusion, so as to improve recognition performance. sEMG signals from 11 muscles of the preferred leg of 15 healthy subjects were recorded during six common lower limb movements. Experimental analysis investigated the impact of the convolutional kernel size (k × k) in Stream 2 of MS-ResNet and the number of muscles involved on recognition performance. The study revealed that selecting k as 13, coupled with 11 muscles, yielded optimal model performance with the average cross-individual recognition accuracy reaching 97.62 %, demonstrating the model’s efficiency in LLMR. This method could provide a viable solution for developing more efficient and reliable LLMR systems, applicable to lower limb exoskeleton robots and intelligent prosthetics.

Rethinking the physiological cross-sectional area of skeletal muscle reveals the mechanical advantage of pennation

The shape of skeletal muscle varies remarkably—with important implications for locomotor performance. In many muscles, the fibres are arranged at an angle relative to the tendons’ line of action, termed the pennation angle. These pennate muscles allow more sarcomeres to be packed side by side, enabling the muscle to generate higher maximum forces for a given muscle size. Historically, the physiological cross-sectional area (PCSA) has been used to capture both the size and arrangement of muscle fibres, and is one of the best predictors of a muscles capacity to produce force. However, the anatomical and mechanical implications of PCSA remain ambiguous as misinterpretations have limited our ability to understand the mechanical advantage of pennate muscle designs. We developed geometric models to resolve the mechanistic and functional impacts of pennation angle across a range of muscle shapes and sizes. Comparisons among model predictions and empirical data on human lower limb muscles demonstrated how a pennate arrangement of fibres allows muscles to produce up to six times more isometric force when compared with non-pennate muscles of the same volume. We show that in muscles much longer than thick, an optimal pennation angle exists at which isometric force is maximized. Using empirically informed geometric models we demonstrate the functional significance of a pennate muscle design and provide a new parameter, pennation mechanical advantage, which quantifies this performance improvement.

High-Performance Sensing Platform Based on Morphology/Lattice Collaborative Control of Femtosecond-Laser-Induced MXene-Composited Graphene.

Flexible sensors based on laser-induced graphene (LIG) are widely used in wearable personal devices, with the morphology and lattice arrangement of LIG the key factors affecting their performance in various applications. In this study, femtosecond-laser-induced MXene-composited graphene (LIMG) is used to improve the electrical conductivity of graphene by incorporating MXene, a 2D material with a high concentration of free electrons, into the LIG structure. By combining pump-probe detection, laser-induced breakdown spectroscopy (LIBS), and density functional theory (DFT) calculations, the morphogenesis and lattice structuring principles of LIMG is explored, with the results indicating that MXene materials are successfully embedded in the graphene lattice, altering both their morphology and electrical properties. The structural sparsity and electrical conductivity of LIMG composites (up to 3187 Sm-1) are significantly enhanced compared to those of LIG. Based on these findings, LIMG has been used in wearable electronics. LIMG electrodes are used to detect uric acid, with a minimum detection limit of 2.48µM. Additionally, LIMG-based pressure and bending sensors have been successfully used to monitor human limb movement and pulse. The direct in situ femtosecond laser patterning synthesis of LIMG has significant implications for developing flexible wearable electronic sensors.

The ontogeny of human fetal trabecular bone architecture occurs in a limb-specific manner

Gestational growth and development of bone is an understudied process compared to soft tissues and has implications for lifelong health. This study investigated growth and development of human fetal limb bone trabecular architecture using 3D digital histomorphometry of microcomputed tomography data from the femora and humeri of 35 skeletons (17 female and 18 male) with gestational ages between 4 and 9 months. Ontogenetic data revealed: (i) fetal trabecular architecture is similar between sexes; (ii) the proximal femoral metaphysis is physically larger, with thicker trabeculae and greater bone volume fraction relative to the humerus, but other aspects of trabecular architecture are similar between the bones; (iii) between 4 and 9 months gestation there is no apparent sexual or limb dimorphism in patterns of growth, but the size of the humerus and femur diverges early in development. Additionally, both bones exhibit significant increases in mean trabecular thickness (and for the femur alone, bone volume fraction) but minimal trabecular reorganisation (i.e., no significant changes in degree of anisotropy, connectivity density, or fractal dimension). Overall, these data suggest that in contrast to data from the axial skeleton, prenatal growth of long bones in the limbs is characterised by size increase, without major reorganizational changes in trabecular architecture.

Ultra-Stretchable Composite Organohydrogels Polymerized Based on MXene@Tannic Acid-Ag Autocatalytic System for Highly Sensitive Wearable Sensors.

Conductive hydrogels have attracted widespread attention in the fields of biomedicine and health monitoring. However, their practical application is severely hindered by the lengthy and energy-intensive polymerization process and weak mechanical properties. Here, a rapid polymerization method of polyacrylic acid/gelatin double-network organohydrogel is designed by integrating tannic acid (TA) and Ag nanoparticles on conductive MXene nanosheets as catalyst in a binary solvent of water and glycerol, requiring no external energy input. The synergistic effect of TA and Ag NPs maintains the dynamic redox activity of phenol and quinone within the system, enhancing the efficiency of ammonium persulfate to generate radicals, leading to polymerization within 10min. Also, ternary composite MXene@TA-Ag can act as conductive agents, enhanced fillers, adhesion promoters, and antibacterial agents of organohydrogels, granting them excellent multi-functionality. The organohydrogels exhibit excellent stretchability (1740%) and high tensile strength (184kPa). The strain sensors based on the organohydrogels exhibit ultrahigh sensitivity (GF=3.86), low detection limit (0.1%), and excellent stability (>1000 cycles, >7 days). These sensors can monitor the human limb movements, respiratory and vocal cord vibration, as well as various levels of arteries. Therefore, this organohydrogel holds potential for applications in fields such as human health monitoring and speech recognition.