Hand Images Research Articles

Bridging the Gap Between Vitiligo Segmentation and Clinical Scores.

Quantitative evaluation of vitiligo is crucial for assessing treatment response. Dermatologists evaluate vitiligo regularly to adjust their treatment plans, which requires extra work. Furthermore, the evaluations may not be objective due to inter- and intra-assessor variability. Though automatic vitiligo segmentation methods provide an objective evaluation, previous methods mainly focus on patch-wise images, and their results cannot be translated into clinical scores for treatment adjustment. Thus, full-body vitiligo segmentation needs to be developed for recording vitiligo changes in different body parts of a patient and for calculating the clinical scores. To bridge this gap, the first full-body vitiligo dataset with 1740 images, following the international vitiligo photo standard, was established. Compared with patch-wise images, full-body images have more complicated ambient light conditions and larger variances in lesion size and distribution. Additionally, in some hand and foot images, skin can be fully covered by either vitiligo or healthy skin. Previous patch-wise segmentation studies completely ignore these cases, as they assume that the contrast between vitiligo and healthy skin is available in each image for segmentation. To address the aforementioned challenges, the proposed algorithm in this study exploits a tailor-made contrast enhancement scheme and long-range comparison. Furthermore, a novel confidence score refinement module is proposed to manage images fully covered by vitiligo or healthy skin. Our results can be converted to clinical scores and used by clinicians. Compared to the state-of-the-art method, the proposed algorithm reduces the average per-image vitiligo involvement percentage error from 3.69% to 1.81%, and the top 10% per-image errors from 23.17% to 8.29%. Our algorithm achieves 1.17% and 3.11% for the mean and max error for the per-patient vitiligo involvement percentage, which is better than an experienced dermatologist's naked-eye evaluation.

The preferences of people in Australia to respond and engage with advertisements to promote reproductive health: Results of a discrete choice experiment

ObjectivesThe health of people prior to pregnancy impacts pregnancy outcomes and childhood health, making the preconception period an important time to optimise health behaviours. Low awareness of the importance of this issue is a recognised barrier to achieving good preconception health. Public health messaging can help to address this barrier. MethodsA discrete choice experiment to assess the preferences of people of reproductive age for a health promotion advertisement for preconception health was conducted. Attributes of the advertisement image, title, additional text content and positioning, and the location of advertisement were assessed by fitting a mixed logit model to the choices made. ResultsThree hundred and thirty-four responses were obtained, from people of reproductive age, both planning and not planning a pregnancy, in Australia. Participants placed most importance on the image, and the location in which they saw the advertisement. An image of adult and baby hands was preferred to adult hands only, and healthcare settings were preferred to more general media locations such as advertising online or on public transport. Preference was also given to the advertisement title of “Healthy you, Healthy baby”, closely followed by “Are you ready for pregnancy?”. The location and content of additional text did not significantly impact engagement with the advertisement. ConclusionThe image and title on the advertisement, and the locations in which they are placed were the most significant features to impact engagement with a health promotion advertisement for preconception health. This can inform health promotion efforts for preconception health.

A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack

Palmprint recognition (PPR) has recently garnered attention due to its robustness and accuracy. Many PPR methods rely on preprocessing the region of interest (ROI). However, the emergence of ROI attacks capable of generating synthetic ROI images poses a significant threat to PPR systems. Despite this, ROI attacks are less practical since PPR systems typically take hand images as input rather than just the ROI. Therefore, there is a pressing need for a method that specifically targets the system by composing hand images. The intuitive approach involves embedding an ROI into a hand image, a comparatively simpler process requiring less data than generating entirely synthetic images. However, embedding faces challenges, as the composited hand image must maintain a consistent color and texture. To overcome these challenges, we propose a training-free, end-to-end hand image composition method incorporating ROI harmonization and palm blending. The ROI harmonization process iteratively adjusts the ROI to seamlessly integrate with the hand using a modified style transfer method. Simultaneously, palm blending employs a pretrained inpainting model to composite a hand image with a continuous transition. Our results demonstrate that the proposed method achieves a high attack performance on the IITD and Tongji datasets, with the composited hand images exhibiting realistic visual quality.

Unraveling the Left-Right Judgment Task in Chronic Low Back Pain: Insights Through Behavioral, Electrophysiological, Motor Imagery, and Bodily Disruption Perspectives

Bodily disruptions have been consistently demonstrated in individuals with chronic low back pain. The performance on the left-right judgment task has been purposed as an indirect measure of the cortical proprioceptive representation of the body. It has been suggested to be dependent on implicit motor imagery, although the available evidence is conflicting. Hence, the aim of this case-control observational study was to examine the performance (accuracy and reaction times) and event-related potentials while performing the left-right judgment task for back and hand images in individuals with chronic low back pain versus healthy controls, along with its relationship with self-reported measurements and quantitative sensory testing. While self-reported data suggested bodily disruptions in the chronic low back pain sample, this was not supported by quantitative sensory testing. Although both groups displayed the same performance, our results suggested an increased attentional load on participants with chronic low back pain to achieve equal performance, measured by a higher N1 peak amplitude in occipital electrodes, especially when the effect of contextual images arises. The absence of differences in the reaction times for the left-right judgment task between both groups, along with inconsistencies in self-reported and quantitative sensory testing data, could question the involvement of implicit motor imagery in solving the task. In conclusion, our results suggest disrupted attentional processing in participants with chronic low back pain to solve the left-right judgment task. PerspectiveAlthough there are no differences in the performance of the left-right judgment task (hits, reaction times) between chronic low back pain patients and controls, the analysis of event-related potentials revealed that patients require a higher cognitive load, measured by N1 peak amplitude.

Clinical application of instant 3D printed cast versus polymer orthosis in the treatment of colles fracture: a randomized controlled trial

BackgroundThe shortcomings of plaster in water resistance, air permeability, skin comfort, fixed stability and weight of wearing are still to be solved. 3D printed cast can overcome the above shortcomings. At present, there is a relative lack of data on the clinical application of 3D printed cast, probably due to its complexity, relatively long operating time, and high price. We aimed to compare and evaluate the short-term effectiveness, safety and advantages of 3D printed wrist cast versus polymer orthosis in the treatment of Colles fracture.MethodsForty patients with Colles fracture in our hospital from June to December 2022 were selected and divided into an observation group (20 patients, treated with instant 3D printed cast) and a control group (20 cases, treated with polymer orthosis). Both groups treated with manual reduction and external fixation. The visual analogue scale (VAS), immobilization effectiveness and satisfaction scores, Disability of the Arm, Shoulder and Hand (DASH) score, complications and imaging data were collected and compared before immobilization and at 2, 6 and 12 weeks after the fracture.ResultsVAS at 2 weeks after the fracture was significantly lower in the observation group than in the control group ( P < 0.05). The immobilization effectiveness and satisfaction scores at 6 weeks after the fracture were significantly higher in the observation group than in the control group (all P < 0.05). The DASH scores at 2 and 6 weeks after the fracture were significantly lower in the observation group than in the control group (all P < 0.05). There wasn’t rupture of the printed cast or orthosis in both groups. There were 2 cases of skin irritation in the control group, and no skin irritation occurred in the observation group. The palmar tilt angle and ulnar inclination angle at 2 weeks and 12 weeks after the fracture were significantly higher in the observation group than in the control group (all P < 0.05).ConclusionsBoth instant 3D printed cast and polymer orthosis are effective in the treatment of Colles fracture. But instant 3D printed cast is better than polymer orthosis in areas of good clinical and imaging performance, and high patient satisfaction and comfort.

Analisis Semiotika Dalam Film Animasi The Anthem Of The Heart

This research is entitled "Semiotic Analysis in the Animated Film The Anthem of the Hear". The aim of this research is to determine the meaning of denotation, connotation and myth in the animated film The Anthem Of The Heart. Researchers use descriptive qualitative methods by recording, analyzing, describing the meanings contained in animated films using Roland Barthes' theory of semiotic analysis, namely by analyzing the meaning of denotation, connotation and myth. The data sources used in this research are primary and secondary data. Data collection techniques were carried out by documenting and watching the animated film The Anthem Of The Heart. The research results show that Roland Barthes's semiotics is interesting to use in analyzing a film and has a clear meaning that can be seen from every scene that the researcher analyzes regarding the meaning of denotation, connotation and myth in the animated film The Anthem Of The Heart. The meaning of denotation is a sign that is visible to the five senses, such as images of palaces and outstretched hands, then the meaning of connotation requires a deeper understanding of the meaning of denotation where the meaning of connotation can give rise to various meanings depending on the point of view of the person giving the meaning. The meaning of myth is formed from the meaning of denotation and connotation. According to Barthes, myth is not always related to culture or customs but is also related to ideology that spreads in wider society. It can be seen in the nine scenes that researchers analyzed, such as myths about crying, helping hands, and divorce.

Virtual Mouse System using Hand Gesture recognition with Open CV

The use of hand gesture recognition for controlling virtual devices has gained popularity with the advancement of artificial intelligence technology. In this paper, we propose a hand gesture-controlled virtual mouse system that employs AI algorithms to identify and interpret hand gestures, translating them into mouse movements. The primary objective of this system is to provide an alternative interface, particularly beneficial for individuals facing challenges with traditional mouse or keyboard usage.Our proposed system utilizes a camera to capture images of the user's hand. To enable gesture recognition, the system undergoes training using a dataset comprising various hand gestures. Once a gesture is identified, it is translated into a corresponding mouse movement, subsequently executed on the virtual screen. An essential feature of our system is its scalability and adaptability, allowing seamless integration into diverse environments and devices. To enhance user interaction, our system incorporates dynamic/static hand gestures along with a voice assistant for virtually controlling all input operations. Notably, our approach employs Machine Learning (ML) and Computer Vision algorithms to recognize hand gestures and voice commands, eliminating the need for additional hardware requirements. The model is implemented using the MediaPipe framework, demonstrating the feasibility of our proposed system. In conclusion, our hand gesture-controlled virtual mouse system presents a promising avenue for improving user experience and accessibility through human-computer interaction. By leveraging AI, ML, and Computer Vision, we aim to create an intuitive and versatile interface that can be easily adapted to different environments, ultimately enhancing accessibility for a wider range of users. Keywords: Computer vision, hand gesture recognition, Media-pipe, virtual mouse.

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.

Temporal differences and commonalities between hand and tool neural processing

Object recognition is a complex cognitive process that relies on how the brain organizes object-related information. While spatial principles have been extensively studied, less studied temporal dynamics may also offer valuable insights into this process, particularly when neural processing overlaps for different categories, as it is the case of the categories of hands and tools. Here we focus on the differences and/or similarities between the time-courses of hand and tool processing under electroencephalography (EEG). Using multivariate pattern analysis, we compared, for different time points, classification accuracy for images of hands or tools when compared to images of animals. We show that for particular time intervals (~ 136–156 ms and ~ 252–328 ms), classification accuracy for hands and for tools differs. Furthermore, we show that classifiers trained to differentiate between tools and animals generalize their learning to classification of hand stimuli between ~ 260–320 ms and ~ 376–500 ms after stimulus onset. Classifiers trained to distinguish between hands and animals, on the other hand, were able to extend their learning to the classification of tools at ~ 150 ms. These findings suggest variations in semantic features and domain-specific differences between the two categories, with later-stage similarities potentially related to shared action processing for hands and tools.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

Skeletal age evaluation using hand X-rays to determine growth problems

A common clinical method for identifying anomalies in bone growth in infants and newborns is skeletal age estimation with X-ray images. Children’s bone abnormalities can result from several conditions including wounds, infections, or tumors. One of the most frequent reasons for bone issues is that most youngsters are affected by the slow displacement of bones caused by pressure applied to the growth plates as youngsters develop. The growth plate can be harmed by a lack of blood supply, separation from other parts of the bone, or slight misalignment. Problems with the growth plate prevent bones from developing, cause joint distortion, and may cause permanent joint injury. A significant discrepancy between the chronological and assessed ages may indicate a growth problem because determining bone age represents the real level of growth. Therefore, skeletal age estimation is performed to look for endocrine disorders, genetic problems, and growth anomalies. To address the bone age assessment challenge, this study uses the Radiological Society of North America’s Pediatric Bone Age Challenge dataset which contains 12,600 radiological images of the left hand of a patient that includes the gender and bone age information. A bone age evaluation system based on the hand skeleton guidelines is proposed in this study for the detection of hand bone maturation. The proposed approach is based on a customized convolutional neural network. For the calculation of the skeletal age, different data augmentation techniques are used; these techniques not only increase the dataset size but also impact the training of the model. The performance of the model is assessed against the Visual Geometry Group (VGG) model. Results demonstrate that the customized convolutional neural network (CNN) model outperforms the VGG model with 97% accuracy.

The lateral approach water bath: A novel method of ultrasound imaging of the hand

BackgroundTraditional water baths for ultrasound exams place a hand into a pan of water and submerge an ultrasound probe into the water. While this improves ultrasound transmission and moves structures into the focal zone to make higher resolution images, this method does have limitations. Patients must be manipulated directly under the probe, which can be limited by pain or normal movement restrictions. The probe must also be held very still in water to minimize motion artifact. The lateral approach water bath method addresses such limitations by imaging through the side of a thin-walled plastic container without submerging the probe. This reduces much need for patient manipulation by imaging through the side of a column-shaped bath, which has 360 degrees of imaging freedom. It also stabilizes the probe directly against the flat, firm container to reduce image degrading motion artifact. We hypothesized that because of these improvements the lateral approach water bath might create higher quality images than traditional water baths. MethodsWe compared twenty images from each method, which were obtained with the same model and ultrasound operator at the same time. Two ultrasound fellowship trained blinded reviewers rated the images for quality and adequacy for clinical decision making on a scale from 1 to 5. ResultsImage quality was better for the lateral water bath, with an average rating of 4.2 compared to the traditional bath's 2.6 (p < 0.001). Adequacy to aid clinical decision making was better for the lateral approach bath with an average rating of 4.0 compared to the traditional bath's 2.6 (p < 0.001). The lateral bath also had a smaller range for image quality and thus greater consistency. ConclusionsThe lateral approach water bath is a method of hand imaging that produces higher quality, more consistent, and more clinically useful images than traditional water bath imaging.

SDFPoseGraphNet: Spatial Deep Feature Pose Graph Network for 2D Hand Pose Estimation.

In the field of computer vision, hand pose estimation (HPE) has attracted significant attention from researchers, especially in the fields of human-computer interaction (HCI) and virtual reality (VR). Despite advancements in 2D HPE, challenges persist due to hand dynamics and occlusions. Accurate extraction of hand features, such as edges, textures, and unique patterns, is crucial for enhancing HPE. To address these challenges, we propose SDFPoseGraphNet, a novel framework that combines the strengths of the VGG-19 architecture with spatial attention (SA), enabling a more refined extraction of deep feature maps from hand images. By incorporating the Pose Graph Model (PGM), the network adaptively processes these feature maps to provide tailored pose estimations. First Inference Module (FIM) potentials, alongside adaptively learned parameters, contribute to the PGM's final pose estimation. The SDFPoseGraphNet, with its end-to-end trainable design, optimizes across all components, ensuring enhanced precision in hand pose estimation. Our proposed model outperforms existing state-of-the-art methods, achieving an average precision of 7.49% against the Convolution Pose Machine (CPM) and 3.84% in comparison to the Adaptive Graphical Model Network (AGMN).

Effect of Service Quality, Store Atmosphere, and Brand Image on Customer Loyalty

This study investigates the relationship between service quality, store atmosphere and brand image towards customer loyalty by considering customer satisfaction as a mediator. Data collection was carried out on 180 Jakarta residents who regularly visit local coffee shops in Jakarta. The results reveal that service quality, store atmosphere and brand image has a significant effect on customer satisfaction, then store atmosphere and customer satisfaction has a significant effect on customer loyalty. On the other hand, service quality and brand image have no effect on customer loyalty. By having service quality as expected, a unique store atmosphere and a good brand image, it will trigger customers to at least try to choose and come to the coffee shop. So in the end, this study is expected to be able to develop customer satisfaction and create loyal customers to local coffee shops in Jakarta.

A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

The accuracy and security of a biometric system are the two sides of a coin. A biometric system must be simple, flexible, efficient, and secure enough from unauthorized access. Concerning these requirements, this paper presents an enhanced palmprint recognition system with superior performance and improved biometric template protection schemes. This method comprises five components: image preprocessing, feature extraction, cancelable biometrics, classification, and Bio-cryptosystem. The palm texture region is extracted from the input hand image during preprocessing. A feature representation technique is then used to extract discriminant features from the palmprint texture patterns. The extracted features are then analyzed using an information coding scheme to compute a user-specific token. Here, the user-specific tokens along with the original palmprint features are used to calculate the feature vectors for the cancelable biometric system. A novel Bio-Cryptosystem has been proposed to protect templates with high security where there is no need to remember the users&#x2019; tokens. Cancelable feature vectors are kept online in encrypted form using the proposed Bio-Cryptosystem, which is used to identify or verify the subjects after decryption. The original feature vectors are kept offline to prevent these from external attacks and misuse. Finally, the multi-class linear support vector machine has been used for user classification. For experimental purposes, two benchmark hand image databases: BOSPHORUS and CASIA-Palmprint, are employed, and the proposed system has achieved nearly 100&#x0025; correct recognition rate in cancelable domain for both the employed databases. Finally, the performance comparison in terms of both verification and identification for the palmprint and the cancelable palmprint recognition systems with the existing state-of-the-art methods shows the superiority of the proposed system. <i>Impact Statement</i> &#x2014;We have improved the biometric template protection scheme by modifying the cancelable biometric technique with two security levels capable of protecting the biometric information from the dictionary, brute-force, database, channel, replay, and spoofing attacks. To avoid the difficulty of remembering long user passwords/tokens, an automatic key generation technique of user passwords has been proposed. Two different keys are generated: the first one used for cancelable biometrics and the second for encryption-decryption processes. Both these keys are not needed to store or remember and are helpful for a multi-factor authentication system. A novel Bio-cryptographic algorithm instead of traditional Bio-Cryptographic algorithms have been introduced within the proposed system. Several attacks and challenging issues of biometric recognition systems have been considered with maintaining outstanding performance in terms of accuracy and security.

Augmented Visual Feedback for Complex Motor Skill Acquisition: A Demonstration with Healthy Young Adults.

Mirror therapy (MT) is a treatment for improving motor function after stroke. Video therapy (VT) training combines observation and imitation of video clips, and it has been used to conduct efficient occupational therapy. We sought to determine the effects of MT and VT on tool-use with healthy young adults. We assigned participants to three different training groups in which they used their non-dominant left hands to move a ball with chopsticks: (a) a self-paced MT group (N = 14), (b) an MT group who moved the ball while looking at the mirror image of the right hand with a sound cue (4-second intervals) (N = 12), and (c) a group who imitated a video demonstration (4-second intervals) (N = 13). The ball-moving time was significantly reduced after training in all three groups: MT group (p < .001), MT-R group (p = .010), and VT group (p = .014), but the video group showed significantly greater relative improvements in motion variability (p = .030) and object prediction (p = .008).We concluded that MT was as effective as VT in improving movement speed, but, with these healthy young adults, MT was less effective than VT in learning speed control or hand pre-shaping to refine task movements for tool use.

Shallow Convolutional Neural Network for Gender Classification Based on Hand

Gender recognition based on the hand image is used in computer vision for human-computer communication, hand-based authentication, and identification systems. Beside this, gender recognition may be applied for criminal investigations, visual surveillance, and other legal purposes. The traditional manual methods require a lot of time and are susceptible to variable fluctuations. However, for low amounts of data, the deep-learning models are going to be overfitted. In this regard, this work proposes a shallow convolutional neural network (CNN) with a regularization method. Here, different gender recognition models are built to detect the gender individually from dorsal and palmar hand images. For that, the 11K hand dataset is divided into four labels, i.e., men dorsal side, women dorsal side, men palm side, and women palm side. These data have been pre-processed by resizing and scaling. Furthermore, a model is developed for recognizing gender from the real time data. According to the experimental results, the model developed for the dorsal hand images outperforms the other proposed models and the current state-of-the-art.

Blood Vessel Injuries of the Fingers: A Clinical Comparison of One- and Two-Arterial Blood Supply.

Traumatic finger injuries are very common in emergency medicine. When patients present with finger injuries, there is often damage to the vascular nerve bundles, which requires subsequent reconstruction. It is unknown if repairing a unilaterally injured artery affects patients' recovery in a well-perfused finger. This retrospective cohort study compares the clinical outcomes of 11 patients with one-vessel supply (mean age 48.3 years; 7 males, 4 females) to 14 patients with two-vessel supply (mean age 44.5 years; 8 males, 6 females). The patient outcomes were assessed using patient questionnaires (Disabilities of Arm, Shoulder, and Hand (DASH), European Quality of Life 5 Dimensions 5 Level Version (EQ-5D-5L), and EuroQol visual analog scale (EQ-VAS)) and a clinical examination of hand function and imaging of circulatory efficiency. No significant changes were observed in the DASH, EQ-5D-5L, and EQ-VAS questionnaires. Clinical evaluation of hand function, measured by cold sensitivity, two-point discrimination, pain numerical analog scale, and grip strength also revealed no significant differences between cohorts. Blood flow measurements using thermal imaging revealed no effects on circulation in the affected digit. Collectively, the study finds reconstruction is not absolutely necessary when there is one intact digital artery as it is sufficient for healing and functional outcomes. We recommend finger artery reconstruction when both digital arteries are injured or if an immediate tension-free suture is possible.

Multifingered Robot Hand Compliant Manipulation Based on Vision-Based Demonstration and Adaptive Force Control.

Multifingered hand dexterous manipulation is quite challenging in the domain of robotics. One remaining issue is how to achieve compliant behaviors. In this work, we propose a human-in-the-loop learning-control approach for acquiring compliant grasping and manipulation skills of a multifinger robot hand. This approach takes the depth image of the human hand as input and generates the desired force commands for the robot. The markerless vision-based teleoperation system is used for the task demonstration, and an end-to-end neural network model (i.e., TeachNet) is trained to map the pose of the human hand to the joint angles of the robot hand in real-time. To endow the robot hand with compliant human-like behaviors, an adaptive force control strategy is designed to predict the desired force control commands based on the pose difference between the robot hand and the human hand during the demonstration. The force controller is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angles. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment compliantly. Our approach has been verified in both simulation and real-world task scenarios based on a multifingered robot hand, that is, the Shadow Hand, and has shown more reliable performances than the current widely used position control mode for obtaining compliant grasping and manipulation behaviors.

Factors associated with hand washing effectiveness: an institution-based observational study

BackgroundFew studies have investigated how the effectiveness of hand washing in removing hand contaminants is influenced by the performance and duration of each step involved. We conducted an observational study by recruiting participants from a university campus, with the aim to comprehensively evaluate how performance, duration and demographic factors influence hand washing effectiveness.MethodsA total of 744 videos were collected from 664 participants in July-October 2022 and independently evaluated by two infection control experts through labelling videos for correct and incorrect performance of each step. The individual hand washing effectiveness was determined by quantifying the percentage of residual fluorescent gel on the dorsum and palm areas of each participant’s hands. A logistic regression analysis was conducted to identify factors that were significantly associated with better hand washing effectiveness. An exposure-response relationship was constructed to identify optimal durations for each step. Approximately 2300 hand images were processed using advanced normalization algorithms and overlaid to visualize the areas with more fluorescence residuals after hand washing.ResultsStep 3 (rub between fingers) was the most frequently omitted step and step 4 (rub the dorsum of fingers) was the most frequently incorrectly performed step. After adjustment for covariates, sex, performance of step 4 and step 7 (rub wrists), rubbing hands during rinsing, and rinsing time were significantly associated with hand washing effectiveness. The optimal overall hand washing time was 31 s from step 1 to step 7, and 28 s from step 1 to step 6, with each step ideally lasting 4–5 s, except step 3. The palms of both hands had less fluorescence residuals than the dorsums. The areas where residuals most likely appeared were wrists, followed by finger tips, finger webs and thumbs.ConclusionsPerformance and duration of some hand washing steps, sex and rinsing time were associated with hand washing effectiveness. The optimal duration might be applied to all seven steps to achieve the best decontamination results. Further studies are needed to refine hand hygiene standards and enhance compliance.