Hand Research Articles

The effect of full-body weight-bearing on palmar pressure distribution in collegiate-level gymnasts

ABSTRACT Wrist and hand biomechanics under full-body load are not fully understood. To identify potential anatomy-related differences in hand loading, 15 former collegiate athletes completed a 45-second handstand on a novel emed® pressure platform system. Center of pressure (CoP) and force distribution across the palmar surface were analysed during the stabilised phase. Maximum force, mean pressure, and contact area were calculated in four palmar anatomic subregions: hypothenar, thenar, metacarpals, and fingers. These values were related to ulnar variance measurements obtained from a participant handstand hold in a weight-bearing computed tomography machine. About 93% of participants shifted their CoP towards their dominant hand (p < 0.001), and among all participants, the dominant hand applied an average of 8.91% (p = 0.002) higher maximum force than the nondominant hand. The proportion of total mean force was highest in the hypothenar (47.1%) and thenar regions (36.5%). Every 1.00 mm increase in ulnar variance corresponded to a 2.8% increase in maximum force in the hypothenar region (p = 0.037). This investigation emphasises the role of gymnastics hand dominance on left/right hand weight distribution and the importance of the hypothenar zone in distributing pressure during handstands. It also indicates that force transmission through the wrist to the palm is contingent on radioulnar positioning.

An overview about hand exoskeleton actuation system and thumb motions analysis

Abstract. Stroke patients experience impaired hand function which significantly impacts their daily activities. Robot-assisted training is essential for restoring motor function, with hand exoskeletons serving as assistive devices to support, amplify, replace, or counteract body movements. Designing an exoskeleton that accurately mimics the natural movement of the thumb and provides adequate support is a key challenge in rehabilitation. This paper designs a retrieval model for hand rehabilitation exoskeletons from 2009 to 2023, analyzing the hand anatomy and thumb motion range. It discusses various driving mechanisms for hand exoskeletons, including cable, linkage, pneumatic, gear motor, and hybrid systems. While progress has been made in experimental stages, more research is needed for clinical applications. A universal hand exoskeleton actuation system that meets all patient preferences does not exist. Ongoing research on hybrid drive systems aims to enhance hand exoskeletons for better rehabilitation and daily use for patients with hand dysfunction.

Anthropomorphic modular gripper finger actuated by antagonistic wire and shape-memory alloy (SMA) springs

Abstract. The traditional underactuated grippers can only passively adapt to the contour of the object, and the passive contact process may lead to the object slipping, affecting the stability of the grasping process. In this paper, an anthropomorphic modular gripper finger actuated by antagonistic wire and shape-memory alloy (SMA) springs, which can actively control the grasping morphology according to the characteristics of the objects to be grasped, is proposed. The wire drive simulates the flexor muscle, and the SMA and reset springs simulate the extensor muscles of the finger, which antagonistically control the grasping morphology of the finger. It is more in line with the grasping characteristics of the human hand. According to the moment equilibrium principle of the finger joints, the deformation model of the gripper is established, the influence of the wire tension and the equivalent stiffness of the finger joints on the grasping morphology is analyzed, and the theoretical joint angle results are verified by the Adams simulation; finally, the experimental system of the gripper is constructed, and the verification of the deformation morphology of the single finger and the gripper's enveloping–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 interphalangeal joints, the enveloping–grasping action of different objects can be completed and the stable grasping of objects of different shapes and sizes can be realized.

Algorithmic Risk Assessment in the Hands of Humans

We evaluate the impacts of adopting algorithmic risk assessments in sentencing. We find that judges changed sentencing practices in response to the risk assessment, but that discretion played a large role in mediating its impact. Judges deviated from the recommendations associated with the algorithm in systematic ways, suggestive of alternative objectives. As a result, risk assessment did not lead to detectable gains in terms of public safety or reduced incarceration rates. Using simulations, we show that strict adherence to the sentencing recommendations associated with the algorithm would have had benefits (less incarceration) but also some costs (increased sentences for youth). (JEL D81, D91, H76, K41, K42)

Humanlike manual activities in Australopithecus

The evolution of the human hand is a topic of great interest in paleoanthropology. As the hand can be involved in a vast array of activities, knowledge regarding how it was used by early hominins can yield crucial information on the factors driving biocultural evolution. Previous research on early hominin hands focused on the overall bone shape. However, while such approaches can inform on mechanical abilities and the evolved efficiency of manipulation, they cannot be used as a definite proxy for individual habitual activity. Accordingly, it is crucial to examine bone structures more responsive to lifetime biomechanical loading, such as muscle attachment sites or internal bone architecture. In this study, we investigate the manual entheseal patterns of Australopithecus afarensis, Australopithecus africanus, and Australopithecus sediba through the application of the validated entheses-based reconstruction of activity method. Using a comparative sample of later Homo and three great ape genera, we analyze the muscle attachment site proportions on the thumb, fifth ray, and third intermediate phalanx to gain insight into the habitual hand use of Australopithecus. We use a novel statistical procedure to account for the effects of interspecies variation in overall size and ray proportions. Our results highlight the importance of certain muscles of the first and fifth digits for humanlike hand use. In humans, these muscles are required for variable in-hand manipulation and are activated during stone-tool production. The entheses of A. sediba suggest muscle activation patterns consistent with a similar suite of habitual manual activities as in later Homo. In contrast, A. africanus and A. afarensis display a mosaic entheseal pattern that combines indications of both humanlike and apelike manipulation. Overall, these findings provide new evidence that some australopith species were already habitually engaging in humanlike manipulation, even if their manual dexterity was likely not as high as in later Homo.

A low-cost prosthetic arm with real-time haptic feedback and gesture control for enhanced user autonomy

In daily life, manipulating and feeling objects is essential for autonomy and engagement in various activities. However, individuals relying on prosthetic limbs often encounter barriers such as high costs, ineffective feedback, and limited design adaptability. Unlike conventional prosthetic limbs, which frequently struggle to integrate seamlessly and provide intuitive control, the robotic arm outlined in this study represents a leap in functionality and user experience. At the core of its design lies a specialized glove outfitted with a calibrated array of flex-sensing resistors capable of precisely detecting and interpreting the wearer's movements. These sensors input to the robotic arm, translating the user's gestures into motions that follow human hands. The fingertips are equipped with force-sensing resistors to discern the magnitude of force applied during gripping actions. This real-time sensory feedback is conveyed to the user through a haptic system integrated into the glove. By modulating the intensity and pattern of vibrations, users can gauge their grip strength and manipulate their grasp of the object accordingly. Comprehensive experiments were conducted across various scenarios to assess its performance, agility, strength, and sensory accuracy. Through refinement, the author aims to optimize functionality and reliability, enhancing practicality for users. This project has broader implications for the prosthetics community. By utilizing the scalability and cost-effectiveness of this model, the author envisions tailored solutions that empower individuals with limb differences to live more independently.

Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee’s intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.

“He Worked with Human Hands”. Work as Human Action and Christ’s Action

The complexity of work in our time presents new challenges for a theology that understands this fundamental dimension of existence and gives it an integrating, truly human meaning and a path to holiness. In order to embrace all modern forms of work, a definition of work is proposed as an action that involves the whole person and leads to fulfilment through work. Not only is work not a punishment, but the punishment would be not to be able to work. In work there is a convergence between nature and spirit. In order for the relationship between them to be harmoniously realised, a mediation is necessary that can only be exercised by man in which both meet. This avoids the risk of materialism and spiritualism in the understanding of work. But this mediation is limited to "this" concrete work. In order for there to be a mediation that includes all work, a Mediator is necessary, and that Mediator is Christ. The work that Jesus of Nazareth carried out was an exercise of his pro-existence and was therefore redemptive and salvific. It is in union with Christ that human work finds its ultimate meaning and transcendent efficacy.

Field tests reveal acoustic variation of call types in a subterranean rodent, the Northern Mole Vole Ellobius talpinus

Abstract This study investigates acoustic variation of human-audible sonic (below 20 kHz) and human-inaudible ultrasonic (above 20 kHz) calls in a wild subterranean rodent, the Northern Mole Vole (Ellobius talpinus), under 3 call-eliciting tests conducted during captures for 1 day. The Contact-in-Tunnel Test modeled contacts of 2 individuals during digging earth in a burrow tunnel. The Restraint Test modeled restraint of an animal by a surrogate predator (human hand). The Release-to-Burrow Test modeled acoustic communication of many family members returned to their home burrow after their isolation for about 8 h, from morning to evening. We described 8 call types: 3 sonic, 3 ultrasonic, and 2 expanding from sonic to ultrasonic range of frequencies; 6 call types were described for the first time for this species. No relationship was found between acoustic parameters and proxies of body size (body mass and the width of 2 upper incisors). No sex differences were found in body size or the acoustic parameters. Different call types prevailed in different tests: wheeks and upsweeps were made during peaceful interactions; squeaks and squeals were related to animal discomfort during the Restraint Test; rasps were only made in Release-to-Burrow Tests when animals were plugging the burrow entrance; and variative calls did not show any relationship with type of test. Based on presence or absence of certain call types in the tests, we evaluate their potential communicative role in comparison with published data on vocal repertoires of other subterranean rodents.

Grasp with push policy for multi-finger dexterity hand based on deep reinforcement learning

When interacting with the external environment, dexterous hands face various challenges, including interference in cluttered environments and difficulty in accurately locating target objects. We observe that the human hand is often at ease in the face of complex unstructured environments: pushing objects that can rearrange clutter, locating target objects, and creating space for fingers. In addition, the gripping action complements the push by achieving the precise movement of unrelated objects. Taking inspiration from this, to enable manipulators to perform accurate grasping tasks in complex environments, we propose a new data-driven grasping approach for robotic dexterous hands: the push-fit grasping approach. This method combines human grasping with model-free deep reinforcement learning to realize the robot's collaborative grasping ability. An end-to-end conditional converter grasping network is first trained, which takes the visual point cloud input to the action output. We use DQN algorithms for strategy training, further enhanced by fine-tuning methods for real-world learning. The experimental results show that the push strategy we learned significantly improves the grasping performance, and the success rate of cooperative grasping is increased by 8 %. It is worth noting that this fine-tuning method significantly reduces the actual training time, with the real world training taking up only one-fifth of the time in the simulated world. Even in demanding scenarios characterized by confusion and complexity, it facilitates rapid learning. This study provides new insights for effectively addressing complex challenges involving multi-fingered dexterous hands and human-computer interaction. Our code is available in the github repository.

Designing Hand Orthoses: Advances and Challenges in Material Extrusion

The intricate structure of human hands requires personalized orthotic treatments, especially with the growing aging population’s demand for accessible care. While traditional orthoses are effective, they face challenges of cost, customization time, and accessibility. Additive manufacturing, particularly material extrusion (MEX) techniques, can effectively address challenges in orthotic device production by enabling automated, complex, and cost-effective solutions. This work aims to provide engineers with a comprehensive set of design considerations for developing hand orthoses using MEX technology, focusing on applying design for additive manufacturing principles, to enhance rehabilitation outcomes. This objective is achieved by establishing design requirements for hand orthoses, reviewing design choices and methodologies across conventional and state-of-the-art MEX-based devices, and proposing an innovative approach to orthotic design. Hand orthosis design requirements were gathered through workshops with occupational therapists and categorized into engineer-, medical-, and patient-specific needs. A review of 3D-printed hand orthoses using MEX analyzes various design approaches, providing insights into existing solutions. The study introduces a modular design concept aimed at improving rehabilitation by enhancing customizability and functionality. It highlights the potential of MEX for creating personalized, cost-effective orthoses and offers recommendations for future research, to optimize designs and improve patient outcomes.

Pupil dilation responds to the intrinsic social characteristics of affective touch

Affective Touch is characterized by both emotional and arousing dimensions that rely on specific features of a gentle human caress. In this study, we investigated whether and how both the nature of the touching effector (Human hand vs. Artificial hand) and touch type (Dynamic vs. Static) influenced the participants’ pupil dilation and their subjective experience during tactile stimulation. We observed that when participants received a dynamic touch, their pupil dilation increased more when the touch was produced by a human compared to an artificial hand. This discrimination was not present for static touch. Also, dynamic touch given by a human hand invoked a supralinear enhancement of pupil dilation indicating that the combination of these two features induced a stronger autonomic activation than the summed effects of each separately. Moreover, this specific type of touch was perceived as the most pleasant compared to all other tactile stimulations. Overall, our results suggest that pupil dilation could reflect the pleasant experience of human-to-human tactile interactions, supporting the notion that the autonomic nervous system is responsive to the emotional and hedonic aspects associated with Affective Touch as a part of a complex and holistic social experience, rather than solely reacting to its low-level sensory properties.

Modeling the Dynamics of Prosthetic Fingers for the Development of Predictive Control Algorithms

In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis’ dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss.

Nanotechnological Innovations in Healthcare

Nanotechnology is a concept much older and more prevalent than you may think. This article will delve into the applications of nanotechnology in various fields of medicine. Using ideas and research, old and new, this publication uses various studies to explore how nanotechnology saves, improves, and, in some cases, enables life. Frankly, the fields discussed further in this paper have nothing in common other than significant and interesting applications of nanotechnology. However, even with this diverse array of fields, only a fraction of nanotechnology’s massive impact across medicinal practice altogether is covered. Nanotechnology has broken into almost every major sector of medicine, finding use from routine practices, such as drug delivery, all the way to extraordinary procedures, such as bone regeneration. This article opens up on the applications of nanotechnology in the cardiovascular, reproductive, antiviral, skeletal, and surgical fields. A substantial amount of research has been conducted to show that nanotechnology is no longer limited to science fiction, and has a major impact that will only grow with time and technology. Doctors and scientists are making full use of nanotechnology’s capabilities by using it in any and all cases that require precision and effectiveness that is either impossible or extremely difficult and dangerous when performed by human hands. This makes many treatments less hazardous and more effective, saving and improving an exponential number of lives as time goes on.

Self‐Powered Iontronic Capacitive Sensing Unit with High Sensitivity in Charge‐Output Mode

AbstractThe operation of iontronic capacitive sensors typically requires an external alternating current (AC) power source, resulting in additional energy consumption and AC‐frequency‐related sensing performance. Here, a class of self‐powered iontronic capacitive sensing units (SICSUs) is proposed based on a dynamic electric double layer (EDL), with a significant charge sensitivity of up to 24270 pC N−1, surpassing most piezoelectric materials by nearly 10 times. The effects of various design parameters and loading conditions on the sensing performance of the SICSUs are systematically investigated. The EDL at the hydrogel‐electrode interface is characterized in situ, revealing the underlying mechanism for high sensitivity and linearity. The capability of SICSUs in detecting diverse human‐related mechanical loads is demonstrated. Furthermore, a robotic hand equipped with a SICSU‐based artificial algesia sensor is fabricated to mimic the withdrawal reflex behavior of a human hand when its skin detects noxious stimuli caused by sharp objects.

Modular soft pneumatic actuator mimics elephant trunk locomotion

Robots support and facilitate tasks in all life fields. Soft robots specifically have the advantages of inherent compliance, safe interaction and flexible deformability. Soft pneumatic network (Pneu-Net) is a soft pneumatic actuator (SPA) composed of network of chambers that is actuated by pneumatic power. Soft Pneu-Net fits the human interface applications perfectly. In this paper, a bio-inspired modular based design for Pneu-Net actuator is developed. The actuator mimics the elephant trunk curling to be employed for rehabilitation of human hand fingers. The actuator is an integrated four Pneu-Net modules actuator which is attached to hand’s finger. The main introduced advantages in the new developed actuator are: providing four degrees of freedom (DoF) essential for finger’s motion by single compound actuator and developing a methodology for a modular soft Pneu-Net actuator that is efficiently reproducible. The actuator’s design is developed using computer aided design (CAD) software SOLIDWORKS. The design is simulated using finite element modeling (FEM) software ABAQUS. Fabrication process uses 3D printed molds. Soft material is molded in the 3D printed molds, forming actuator’s modules. Actuator’s modules are integrated by adhesion using the soft material. A proposed non-standard hyper-elastic material biaxial tension test is introduced as a quick material properties identification method that can produce a test table used for material identification in the FEM. Enhanced version for the actuator uses reinforcement fibers. Results show advances for the reinforced actuator, as it limits the unwanted actuator’s strain and deformation. The reinforced actuator shows improved energy efficiency reaches to 46%.

Advances in vision-based deep learning methods for interacting hands reconstruction: A survey

Vision-based hand reconstructions have become noteworthy tools in enhancing interactive experiences in various applications such as virtual reality, augmented reality, and autonomous driving, which enable sophisticated interactions by reconstructing complex motions of human hands. Despite significant progress driven by deep-learning methodologies, the quest for high-fidelity interacting hands reconstruction faces challenges such as limited dataset diversity, lack of detailed hand representation, occlusions, and differentiation between similar hand structures. This survey thoroughly reviews deep learning-based methods, diverse datasets, loss functions, and evaluation metrics addressing the complexities of interacting hands reconstruction. Mainstream algorithms of the past five years are systematically classified into two main categories: algorithms that employ explicit representations, such as parametric meshes and 3D Gaussian splatting, and those that utilize implicit representations, including signed distance fields and neural radiance fields. Novel deep-learning models like graph convolutional networks and transformers are applied to solve the aforementioned challenges in hand reconstruction effectively. Beyond summarizing these interaction-aware algorithms, this survey also briefly discusses hand tracking in virtual reality and augmented reality. To the best of our knowledge, this is the first survey specifically focusing on the reconstruction of both hands and their interactions with objects. The survey contains the various facets of hand modeling, deep learning approaches, and datasets, broadening the horizon of hand reconstruction research and future innovation in natural user interactions.

PEDOT:PSS-Based Wearable Flexible Temperature Sensor and Integrated Sensing Matrix for Human Body Monitoring.

Flexible temperature sensors have been widely used in electronic skins and health monitoring. Body temperature as one of the key physiological signals is crucial for detecting human body's abnormalities, which necessitates high sensitivity, quick responsiveness, and stable monitoring. In this paper, we reported a resistive temperature sensor designed as an ultrathin laminated structure with a serpentine pattern and a bioinspired adhesive layer, which was fabricated with a composite of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)/single-wall carbon nanotubes/reduced graphene oxide (PEDOT:PSS/SWCNTs/rGO) and polydimethylsiloxane (PDMS). The temperature sensor exhibited a high temperature sensitivity of 0.63% °C-1, coupled with outstanding linearity of 0.98 within 25-45 °C. Furthermore, it showed fast response and recovery speeds of 4.8 and 5.8 s, respectively, between 25 and 36 °C. It also demonstrated exceptional stability when subjected to stress and bending disturbances with the maximum bending interference deviation of 0.03%. Additionally, it displayed good cyclic stability over a broad temperature range from 25 to 85 °C, and the standard deviation at 25 °C is 0.14%. A series of experiments including blowing detection, respiratory monitoring with or without a mask, and during rest or sleep were conducted to show the potential of the flexible temperature sensors in human body monitoring. Furthermore, a 4 × 4 flexible temperature sensor matrix was integrated to detect and map objects such as wrenches and blood vessels through human hand skin. The results were consistent with those of infrared measurements. The flexible temperature sensor is capable of real-time temperature monitoring and has the potential in tracking human respiration, assessing sleep quality, and mapping the temperature of various objects.

Contemporary Applications and Future Perspectives of Robots in Endodontics: A Scoping Review.

In the era of modern advanced endodontics, the reduction of reliance on human hands and shifting towards robotics could benefit the precision and accuracy of endodontic treatment. This scoping review aims to describe current and emerging trends in the applications of robots in endodontics and highlight their limitations and future perspectives. This review followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) standards. A comprehensive search of internet databases was conducted until February 2024. Studies that specifically examined robots in the field of endodontics were included. The studies focused on root canal cleaning, shaping, surgical procedures, and other applications. Robotic systems demonstrated improved accuracy, precision, reduced errors, and time savings compared with manual techniques. Robotics in endodontics show promise, especially in surgical procedures, with AI integration enhancing accuracy and efficiency. Challenges such as cost, physical limitations, and absence of tactile feedback require further research and investment.

Bio-inspired e-skin with integrated antifouling and comfortable wearing for self-powered motion monitoring and ultra-long-range human-machine interaction

Electronic skin (e-skin) inspired by the sensory function of the skin demonstrates broad application prospects in health, medicine, and human–machine interaction. Herein, we developed a self-powered all-fiber bio-inspired e-skin (AFBI E-skin) that integrated functions of antifouling, antibacterial properties, biocompatibility and breathability. AFBI E-skin was composed of three layers of electrospun nanofibrous films. The superhydrophobic outer layer Poly(vinylidene fluoride)-silica nanofibrous films (PVDF-SiO2 NFs) possessed antifouling properties against common liquids in daily life and resisted bacterial adhesion. The polyaniline nanofibrous films (PANI NFs) were used as the electrode layer, and it had strong “static” antibacterial capability. Meanwhile, the inner layer Polylactic acid nanofibrous films (PLA NFs) served as a biocompatible substrate. Based on the triboelectric nanogenerator principle, AFBI E-skin not only enabled self-powered sensing but also utilized the generated electrical stimulation for “dynamic” antibacterial. The “dynamic-static” synergistic antibacterial strategy greatly enhanced the antibacterial effect. AFBI E-skin could be used for self-powered motion monitoring to obtain a stable signal output even when water was splashed on its surface. Finally, based on AFBI E-skin, we constructed an ultra-long-range human-machine interaction control system, enabling synchronized hand gestures between human hand and robotic hand in any internet-covered area worldwide theoretically. AFBI E-skin exhibited vast application potential in fields like smart wearable electronics and intelligent robotics.