Artificial Hand Research Articles

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.

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

Human-centered Artificial Intelligence Development

Few researchers provide a wider vision of artificial feet, hands, mouths, eyes, ears, and brains. This limits our vision of them and their significant impacts on the modern Industrial Revolution and Artificial Intelligence (AI) history. This article presents a novel perspective on human-centered social development starting from artificial feet. After briefly reviewing AI, this article explores the age of AI and artificial feet, hands, mouths, eyes, ears, and brains. It also applies AI to artificial feet and artificial brains. The research reveals that artificial feet are one of the origins of the Industrial Revolution and a real foundation of AI. The study demonstrates that artificial feet and brains liberate our body and society, whereas from artificial brains to artificial feet is control of our body and society. This article also looks at AI's trends and challenges. The approach in this article will facilitate the research and development of big data, analytics, and intelligences.

Highly compressive aerogel-elastomer hybrid porous material with redistributed strain for high-sensitive soft pressure sensors

Owing to the good sensitivity and simple fabrication process, flexible piezoresistive pressure sensors based on aerogels shows great applications in soft robotics, bioelectronics and so on. However, the poor compressibility and stability of the aerogel is still challenging the development of aerogel based flexible piezoresistive pressure sensors, simultaneously, it is urgent to pave a way to enhance the sensitivity of these sensors. Here, for the first time, we propose a universal strategy to prepare aerogel skeleton-elastomer shell hybrid porous material with enhanced compressibility for high-sensitive and stable flexible pressure sensors. We found that the elastomer shell disperses the strain in the aerogel skeleton and enhances the compressibility of the aerogel, which improve the stability of the sensor (10000 cycles of compression). More importantly, there are elastomer micro-blocks formed in the hybrid material, these micro-blocks redistribute the strain in the material which contributes to the high sensitivity (10.25 kPa−1) of the sensors. Furthermore, the sensors are successful in monitoring both of the large joint bending and subtle skin vibration, as well as remote controlling toy car and artificial hand. This work provides an effective and universal strategy to enhance the sensitivity and stability of the aerogel based flexible pressure sensors and promotes their development towards practical applications.

The Latest Research Progress on Bionic Artificial Hands: A Systematic Review.

Bionic prosthetic hands hold the potential to replicate the functionality of human hands. The use of bionic limbs can assist amputees in performing everyday activities. This article systematically reviews the research progress on bionic prostheses, with a focus on control mechanisms, sensory feedback integration, and mechanical design innovations. It emphasizes the use of bioelectrical signals, such as electromyography (EMG), for prosthetic control and discusses the application of machine learning algorithms to enhance the accuracy of gesture recognition. Additionally, the paper explores advancements in sensory feedback technologies, including tactile, visual, and auditory modalities, which enhance user interaction by providing essential environmental feedback. The mechanical design of prosthetic hands is also examined, with particular attention to achieving a balance between dexterity, weight, and durability. Our contribution consists of compiling current research trends and identifying key areas for future development, including the enhancement of control system integration and improving the aesthetic and functional resemblance of prostheses to natural limbs. This work aims to inform and inspire ongoing research that seeks to refine the utility and accessibility of prosthetic hands for amputees, emphasizing user-centric innovations.

Artificial Intelligence-Powered Hand Surgery Consultation: GPT-4 as an Assistant in a Hand Surgery Outpatient Clinic

Exploring the integration of artificial intelligence in clinical settings, this study examined the feasibility of using Generative Pretrained Transformer 4 (GPT-4), a large language model, as a consultation assistant in a hand surgery outpatient clinic. The study involved 10 simulated patient scenarios with common hand conditions, where GPT-4, enhanced through specific prompt engineering techniques, conducted medical history interviews, and assisted in diagnostic processes. A panel of expert hand surgeons, each board-certified in hand surgery, evaluated GPT-4's responses using a Likert Scale across five criteria with scores ranging from 1 (lowest) to 5 (highest). Generative Pretrained Transformer 4 achieved an average score of 4.6, reflecting good performance in documenting a medical history, as evaluated by the hand surgeons. These findings suggest that GPT-4 can effectively document medical histories to meet the standards of hand surgeons in a simulated environment. The findings indicate potential for future application in patient care, but the actual performance of GPT-4 in real clinical settings remains to be investigated. This study provides a preliminary indication that GPT-4 could be a useful consultation assistant in a hand surgery outpatient clinic, but further research is required to explore its reliability and practicality in actual practice.

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

People use their hands for intricate tasks like playing musical instruments, employing myriad touch sensations to inform motor control. In contrast, current prosthetic hands lack comprehensive haptic feedback and exhibit rudimentary multitasking functionality. Limited research has explored the potential of upper limb amputees to feel, perceive, and respond to multiple channels of simultaneously activated haptic feedback to concurrently control the individual fingers of dexterous prosthetic hands. This study introduces a novel control architecture for three amputees and nine additional subjects to concurrently control individual fingers of an artificial hand using two channels of context-specific haptic feedback. Artificial neural networks (ANNs) recognize subjects’ electromyogram (EMG) patterns governing the artificial hand controller. ANNs also classify the directions objects slip across tactile sensors on the robotic fingertips, which are encoded via the vibration frequency of wearable vibrotactile actuators. Subjects implement control strategies with each finger simultaneously to prevent or permit slip as desired, achieving a 94.49% ± 8.79% overall success rate. Although no statistically significant difference exists between amputees’ and non-amputees’ success rates, amputees require more time to respond to simultaneous haptic feedback signals, suggesting a higher cognitive load. Nevertheless, amputees can accurately interpret multiple channels of nuanced haptic feedback to concurrently control individual robotic fingers, addressing the challenge of multitasking with dexterous prosthetic hands.

Simulation and experimental study on rope driven artificial hand and driven motor.

Prosthetic hands have the potential to replace human hands. Using prosthetic hands can help patients with hand loss to complete the necessary daily living actions. This paper studies the design of a bionic, compact, low-cost, and lightweight 3D printing humanoid hand. The five fingers are underactuated, with a total of 9 degrees of freedom. In the design of an underactuated hand, it is a basic element composed of an actuator, spring, rope, and guide system. A single actuator is providing power for five fingers. And the dynamic simulation is carried out to calculate the motion trajectory effect. In this paper, the driving structure of the ultrasonic motor was designed, and the structural size of the ultrasonic motor vibrator was determined by modal and transient simulation analysis, which replace the traditional brake, realize the lightweight design of prosthetic hand, improve the motion accuracy and optimize the driving performance of prosthetic hand. By replacing traditional actuators with new types of actuators, lightweight design of prosthetic hands can be achieved, improving motion accuracy and optimizing the driving performance of prosthetic hands.

SEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control.

The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.

Highly sensitive flexible pressure sensor based on a 1D/2D hybrid aerogel

Wearable pressure sensors with highly sensitive and wide detection ranges are essential for human motion monitoring. In this work, a self-assembled hybrid aerogel was obtained by polyacrylonitrile nanofibers, polyaniline, and reduced graphene oxide. Based on the complementation of 1D and 2D nanomaterials, the aerogel acted like a spring under lower pressure and a membrane under higher pressure, showing great potential in human motion monitoring. According to the wide detection range of the aerogel, an artificial hand was designed to recognize different spatial pressures such as grasping a pen, holding a bottle, and shaking hands. These signal outputs could be precisely reflected by voltage change and further converted to RGB values. Meanwhile, a shoe pad was prepared to monitor foot pressure and the resistance change could be converted to color scale signals. This work provides a new strategy for 1D/2D hybrid nanomaterials-based aerogel with wide pressure ranges in flexible electronics.

Eye infection risks from Pseudomonas aeruginosa via hand soap and eye drops.

Eye infections from bacterial contamination of bulk-refillable liquid soap dispensers and artificial tear eye drops continue to occur, resulting in adverse health outcomes that include impaired vision or eye enucleation. Pseudomonas aeruginosa (P. aeruginosa), a common cause of eye infections, can grow in eye drop containers and refillable soap dispensers to high numbers. To assess the risk of eye infection, a quantitative microbial risk assessment for P. aeruginosa was conducted to predict the probability of an eye infection for two potential exposure scenarios: (i) individuals using bacteria-contaminated eye drops and (ii) contact lens wearers washing their hands with bacteria-contaminated liquid soap prior to placing the lens. The median risk of an eye infection using contaminated eye drops and hand soap for both single and multiple exposure events (per day) ranged from 10-1 to 10-4, with contaminated eye drops having the greater risk. The concentration of P. aeruginosa was identified as the parameter contributing the greatest variance on eye infection risk; therefore, the prevalence and level of bacterial contamination of the product would have the greatest influence on health risk. Using eye drops in a single-use container or with preservatives can mitigate bacterial growth, and using non-refillable soap dispensers is recommended to reduce contamination of hand soap. Given the opportunistic nature of P. aeruginosa and its ability to thrive in unique environments, additional safeguards to mitigate bacterial growth and exposure are warranted.IMPORTANCEPseudomonas aeruginosa (P. aeruginosa) is a pathogen that can persist in a variety of unusual environments and continues to pose a significant risk for public health. This quantitative microbial risk assessment (QMRA) estimates the potential human health risks, specifically for eye infections, associated with exposure to P. aeruginosa in bacteria-contaminated artificial tear eye drops and hand soap. This study applies the risk assessment framework of QMRA to evaluate eye infection risks through both consumer products. The study examines the prevalence of this pathogen in eye drops and soap, as well as the critical need to implement measures that will mitigate bacterial exposure (e.g., single-use soap dispensers and eye drops with preservatives). Additionally, limitations and challenges are discussed, including the need to incorporate data regarding consumer practices, which may improve exposure assessments and health risk estimates.

Transcutaneous Magnet Localizer for a Self-Contained Myokinetic Prosthetic Hand.

The search for a physiologically appropriate interface for the control of dexterous hand prostheses is an ongoing challenge in bioengineering. In this context, we proposed an interface, named myokinetic control interface, based on the localization of magnets implanted in the residual limb muscles, to monitor their contractions and send appropriate commands to the artificial hand. As part of such concept, this interface requires a transcutaneous magnet localizer that can be integrated in a self-contained limb prosthesis, a feature yet to be realized within the current state of the art. In an attempt to cover this gap, here we present a modular embedded system consisting of a computation unit able to acquire synchronized samples captured by up to eight acquisition units, so to localize multiple magnets. The system exhibits short computation times (<60ms) and power consumption (0.6-1.2W) which are suitable for use in a clinically viable prosthetic arm. The system proved able to localize magnets while moving at speeds in the range of physiological movements (<0.24m/s), with high accuracy (<1mm) and precision (<0.5mm). We demonstrated a system suitable for the implementation of a self-contained myokinetic prosthetic hand. These results pave the way towards the clinical implementation of the myokinetic interface, with amputees controlling an artificial arm by means of implanted magnets.

Composite Laminar Jamming: Toward Designing a Tunable Stiffness Hybrid Soft Robotic Actuator

Tunable stiffness in soft robotic actuators is crucial for developing sensor augmented artificial hands capable of mimicking human gripping complexity at reduced costs. This work proposes a synergistic actuator integrated with a composite laminar jamming structure developed by bonding together layers of printer paper and abrasive paper of 400 grit size. The proposed structure demonstrates superior stiffness and a broader tunable stiffness range compared to traditional uniform paper jammers. The results of load sensing revealed that the composite jammer requires less precise vacuum control mechanisms. The experimental findings confirm the effective response of the composite laminar jamming technique in terms of stiffness creation, tunability, and vacuum control efficiency. The proposed design holds significant potential for integration into sensor augmented soft robotic systems, specifically in precision robotics and biomedical applications.

Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration.

For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk.

Perceiving and Mentally Rotating Real and Artificial Hands

ABSTRACT Introduction In a hand laterality judgment task, participants determine whether rotated images depict a left or a right hand. It is believed that people use motor imagery (imagining their own body moving) to complete this task. This is evidenced by a biomechanical constraints effect (BCE), whereby people take longer to respond to hands shown at biologically awkward angles. It is unknown whether this extends to artificial hands. This study investigated whether prosthetic hands are processed differently when mentally manipulated, potentially due to their perceived eeriness. Materials and Methods In two online studies, participants completed a hand laterality judgment task containing real and artificial hands (realistic and mechanical prosthetics). Results All three hand types produced a BCE, evidencing motor imagery, although this was weaker in mechanical hands. Reaction time and slope (effect of angular rotation on reaction time) also varied between the hand types. Conclusions Our findings indicate that people use motor imagery to process artificial hands, although possible differences in processing between the hands should be followed up. Clinical Relevance Hand laterality judgment tasks could be used to further explore differences in how people process real and artificial hands. Future research might incorporate these tasks with users of prosthetic hands.

Bodily self-awareness and the onset of ownership illusion

The moving Rubber Hand Illusion (mRHI) is a paradigm which investigates the sense of ownership (SoO) and the sense of agency (SoA) towards an artificial hand. This study aimed to highlight the role of the SoA on the development of the SoO according to the information provided by an actionrelated auditory cue. We expected that a reinforcement of the SoO illusion by the SoA could not occur if the sound of the action was removed. In order to assess the SoO and SoA, 25 participants performed the task in the the active congruent, passive congruent, and active incongruent conditions. Also, the onset time of the SoO in the congruent conditions was reported. The results confirmed our hypothesis, showing that agency did not play a pivotal role in promoting the onset of the ownership illusion if ecological auditory feedback was removed. Further research on the cross-modal assessment of individual bodily selfconsciousness is recommended.&amp;nbsp;

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging problem for both neuroscience and engineering. The aim of this study is to review the current state of the art in hand and grasp control from a neuroscientific perspective, focusing on the brain mechanisms that underlie sensory integration for hand control and the engineering implications for developing artificial hands that can mimic and interface with the human brain. The brain controls the hand by processing and integrating sensory information from vision, proprioception, and touch, using different neural pathways. The user’s intention can be obtained to control the artificial hand by using different interfaces, such as electromyography, electroneurography, and electroencephalography. This and other sensory information can be exploited by different learning mechanisms that can help the user adapt to changes in sensory inputs or outputs, such as reinforcement learning, motor adaptation, and internal models. This work summarizes the main findings and challenges of each aspect of hand and grasp control research and highlights the gaps and limitations of the current approaches. In the last part, some open questions and future directions for hand and grasp control research are suggested by emphasizing the need for a neuroscientific approach that can bridge the gap between the brain and the hand.

Decomposition and Reconstruction of Human Palm Movements.

The human hand is known to have excellent manipulation ability compared to other primate hands. Without the palm movements, the human hand would lose more than 40% of its functions. However, uncovering the constitution of palm movements is still a challenging problem involving kinesiology, physiology, and engineering science. By recording the palm joint angles during common grasping, gesturing, and manipulation tasks, we built a palm kinematic dataset. Then, a method for extracting the eigen-movements to characterize the common motion correlation relationships of palm joints was proposed to explore the palm movement constitution. This study revealed a palm kinematic characteristic that we named the joint motion grouping coupling characteristic. During natural palm movements, there are several joint groups with a high degree of motor independence, while the movements of joints within each joint group are interdependent. Based on these characteristics, the palm movements can be decomposed into seven eigen-movements. The linear combinations of these eigen-movements can reconstruct more than 90% of palm movement ability. Moreover, combined with the palm musculoskeletal structures, we found that the revealed eigen-movements are associated with joint groups that are defined by muscular functions, which provided a meaningful context for palm movement decomposition. This paper suggests that some invariable characteristics underlie the variable palm motor behaviors and can be used to simplify palm movement generation. This paper provides important insights into palm kinematics, and helps facilitate motor function assessment and the development of better artificial hands.

Deep Learning based Brain Stroke Detection using Improved VGGNet

Brain stroke is one of the critical health issues as the after effects provides physical inability and sometimes death. The inability of focus in the brain due to bleeding or clogged blood vessels leads to stroke. Early treatment and diagnosis are crucial in and following manual procedures takes more time which further increases the criticalness. Artificial intelligence and machine learning techniques hands together in medical domain and numerous applications are developed to reduce the diagnosis time and to improve the accuracy. Incorporating machine learning techniques in brain stroke detection is a familiar research arena and numerous research works are evolved with better solutions. However, the drive towards developing better system for brain stroke detection is still in progress. Thus, in this research work, deep learning-based brain stroke detection system is presented using improved VGGNet. Simulation analysis using a set of brain stroke data and the performance of learning algorithms are measured in terms of accuracy, sensitivity, specificity, precision, f-measure, and Jaccard index. The better performance of proposed model is comparatively analyzed with traditional machine learning algorithms like support vector machine, Naïve Bayes, Decision tree, K-Nearest neighbor, and recent deep learning models like ResNet, Squeeze Net, Alex Net, and Google Net algorithms. Experimental results validates that the Improved VGG model attained better performance for all the parameters. Specifically with 96.86% of detection accuracy improved VGG model detects the brain strokes effectively compared to other learning algorithms.

Tough and Photo-Plastic Liquid Crystal Elastomer with a 2-Fold Dynamic Linker for Artificial Muscles.

Liquid crystal elastomers (LCEs) have been optimized by combining cross-linkers and dynamic bonds to achieve a reversible actuation behavior comparable to living skeletal muscles. In this study, one unique type of segment with 2-fold dynamic properties was introduced into LCEs, which offered not only dynamic diselenide covalent bonds for thermo-/photoplasticity but also H-bond arrays for dynamic cross-linking and mechanical robustness. Besides self-healing, self-welding, and recyclability, the LCEs were reprogrammable with elevated temperature or intensive visible light irradiation. The resultant LCEs gave an actuation blocking stress of 1.96 MPa and an elastic modulus of 14.4 MPa at 80 °C. The actuation work capacity reached 135.2 kJ m-3. When incorporating the Joule electrode or photothermal materials, the LCEs could be programmed as the electricity-driven and photothermal artificial muscles and thereby promised the application both as a biomimetic artificial hand and as an energy collector from sunlight. Thus, the 2-fold dynamic LCEs offered the pathway of enabling the reversible actuation behavior comparable to living skeletal muscles and promising applications in sustainable actuators, artificial muscles, and soft robots.