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Background: Effective information transfer relies on the proper use of educational tools. Evaluating the quality of presentations permits us to improve educational materials in plastic surgery. The authors' aims were to assess the quality of presentations at a national hand surgery meeting, using a checklist of presentation standards from the literature, and to identify areas of improvement., Methods: The study sample included presentations from the clinical papers sessions at the 2020 Annual Meeting of the American Society for Surgery of the Hand. A modified checklist based on the literature was used to assess the presentations. Two members of the research team extracted data from the included presentations, and disagreements were reviewed collaboratively., Results: A total of 96 presentations were included in this sample. The mean number of deficiencies per slide set was approximately 9. Misused graphics, ambiguous content (eg, undefined abbreviations, undefined symbols), and overdetermined slides were the most common deficiencies identified in the sample. One-way analysis of variance of presenter role found a significant difference in the mean number of deficiencies ( F2,93 = 7.36; P = 0.001) among different types of presenters, with surgeon presenters exhibiting more deficiencies than students and other health care professionals., Conclusions: The use of a checklist to evaluate a presentation helps cultivate more effective presentations in national meetings. A collaborative peer-review process, incorporating feedback from multiple trainees, audience members, and colleagues, facilitates effective information transfer through presentations., (Copyright © 2023 by the American Society of Plastic Surgeons.)

Objectives: This study aims to conduct a thorough characterization of hand morphology. Employing a 2D geometric morphometric approach, we scrutinize individual fingers and the palm, delineating the ontogenetic trajectories for each biological sex and investigating the alterations that take place at various stages of human development., Materials and Methods: A set of thirty-two 2D anatomical landmarks were assessed in a sex-balanced sample of human hands (F = 275, M = 250 males), spanning all stages of human development. Following Procrustes registration, the data on size and shape for individual fingers and the palm were examined for each biological sex and age group. Regression analysis was utilized to quantify ontogenetic trajectories for each biological sex., Results: The findings suggest a gradual escalation in sexual dimorphism throughout human development, with statistically noteworthy distinctions becoming apparent in size starting at the age of 3, and in shape from the age of 7 onwards. Additionally, our analyses uncover a distinctive sigmoid pattern between sexes, indicating that biological male hands exhibit a sturdier build compared to biological female hands from early childhood onward., Conclusions: In conclusion, this study enriches our insights into sexual dimorphism in human hands, stressing the importance of considering both size and shape across different ontogenetic stages. These findings not only expand our understanding of human biological variation but also lay the foundation for future interdisciplinary research in diverse scientific domains., (© 2024 The Author(s). American Journal of Biological Anthropology published by Wiley Periodicals LLC.)

Background: Patient informed consent is a crucial subject in preoperative care of patients before elective hand surgery, ensuring that patients have the necessary information and a comprehensive understanding to make autonomous decisions. The use of video-based informed consent systems is an innovative concept to enhance the consent process with multimedia tools. In addition to the conventional process, mostly relying on verbal communication and written documents, the video-based approach aims to present information in a standardized and visually appealing format., Methods: In this study, 33 patients were asked to watch a video on a tablet about the planned elective hand surgery after a conventional pre-treatment consultation including informed consent throughout verbal explanations and paper forms by an attending physician or resident. All patients were asked to complete a questionnaire after watching the video., Results: An overwhelming majority of participants, specifically 97.0%, stated that the video improved their understanding of the upcoming surgery. 90.9% of the participant would refer the video to other patients undergoing elective hand surgery, while 72.7% of participants indicated that they would have appreciated the opportunity to view an informational video before undergoing different types of surgeries in the past., Conclusion: The use of a video-based patient information system in elective hand surgery had a positive impact on patient education and satisfaction with the informed consent process. Therefore, it is a powerful tool in preoperative management to guarantee a standardized and educative informed consent., (© 2024. The Author(s).)

Background: In recent years, hand drying has been highlighted as a key step in appropriate hand hygiene, as moisture on hands can increase the transfer of micro-organisms from hands to surfaces and vice versa., Aim: To understand bacterial and viral aerosolization following hand drying, and study the transfer of micro-organisms from hands to surfaces after drying using different methods., Methods: Groups of five volunteers had their hands pre-washed with soap, rinsed and dried, then inoculated with a concentrated mixture of Pseudomonas fluorescens and MS2 bacteriophage. Volunteers entered an empty washroom, one at a time, and rinsed their hands with water or washed their hands with soap prior to drying with a jet dryer or paper towels. Each volunteer applied one hand successively to various surfaces, while their other hand was sampled using the glove juice method. Both residual bacteria and viruses were quantified from the washroom air, surface swabs and hand samples., Findings: P. fluorescens and MS2 bacteriophages were rarely aerosolized while drying hands for any of the drying methods studied. Results also showed limited, and similar, transfer of both micro-organisms studied on to surfaces for all drying methods., Conclusion: The use of jet dryers or paper towels produces low levels of aerosolization when drying hands in a washroom. Similarly, all drying methods result in low transfer to surfaces. While the coronavirus disease 2019 pandemic raised concerns regarding public washrooms, this study shows that all methods tested are hygienic solutions for dry washed hands., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5°, displacement up to 6 pixels, and scale change up to 94%., (© 2021 Wiley Periodicals LLC.)

Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest form is provided with the online version of the article ( http://links.lww.com/JBJS/H396 ).

Background: Hand drying is an essential step of hand hygiene, helping remove microbes remaining on hands following handwashing. However, it is unclear whether particles dispersed or aerosolized during hand drying can also have an impact on microbe dissemination and so pose an infection risk., Methods: We used a PR772 bacteriophage to investigate whether microorganisms remaining on hands can disperse in the washroom environment and contaminate facemasks of others sharing the same space, as a surrogate for virus inhalation risk. Hand drying using either a jet air dryer or paper towels were performed, and mask contamination by splattering and droplet deposition was investigated, up to 15 min following each procedure., Results: Facemask contamination by splattering was 10-fold higher when a jet air dryer was used, compared with hand drying by paper towels, for both the person performing the hand drying and for standby users stationed at 1 and 2 m distance. Facemask contamination by droplet/aerosols deposition was higher in the first 5 min following hand drying, for both methods; however, virus load was significantly higher when a jet air dryer was used. In the jet air dryer assays, facemask contamination increased at 15 min post-hand drying, suggesting aerosolization of small particles that remain airborne for longer., Conclusion: When using a jet air dryer, virus contamination dispersed further and for a longer period of time (up to 15 min post hand-drying). The method chosen for hand drying can potentially impact the airborne dissemination of microbial pathogens, including respiratory virus, and so potentially increase the risk of exposure and infection for other washroom users., Competing Interests: Author MW has received honoraria for consultancy work, financial support to attend meetings and research funding from Astellas, AstraZeneca, Abbott, Actelion, Alere, Bayer, bioMérieux, Cerexa, Cubist, Da Volterra, Durata, ETS, Merck, Nabriva Therapeutics plc, Pfizer, Qiagen, Roche, Seres Therapeutics Inc., Synthetic Biologics, Summit and The Medicines Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Moura, Bentley and Wilcox.)

Bidet toilets (electric toilet seats with water spray) are increasing in popularity worldwide. However, the extent of reduction of microbial contamination of the hands with the use of bidet toilets after defecation is unclear. Microbe contamination of the hands with and without the use of bidet toilets after defecation was examined in 32 nursing students. Double gloves were worn on the dominant hand and four layers of toilet paper were used to wipe the buttocks after defecation, and microbe contamination of the second glove (outer glove) of the double gloves was examined. The volunteers were free to select the flow volume, wash time of the bidet, and the type of bidet. Without the use of a bidet toilet, the average value+standard deviation of the number of microbes attached to the gloves was 39,499.3+77,768.3 colony forming units (cfu)/glove; however, it was 4,146.9+11,427.7 cfu/glove when the bidet toilet was used. The number of microbes adhering to gloves was significantly reduced when a bidet toilet was used (p,0.00001, Wilcoxon signed-rank test). [ABSTRACT FROM AUTHOR]

The evolution and variations of human anatomy are of great interest to physicians and anatomists. Variations can be categorized as vestigial, accessory or atavistic structures. Vestigial muscles are frequently encountered structures that are normally present but have become rudimentary through evolution. Muscles that disappeared during evolution sometimes arise again, although rarely; such muscles are referred to as atavistic. They arise from failure of suppression of genetic loci. Some common atavistic muscles seen clinically are the extensor digitorum brevis manus, chondroepitrochlearis and plantaris. Atavistic muscles appear more frequently in the upper limb than in any other region of the human body. One explanation for the appearance of these muscles, mainly within the upper limbs, is based on the evolution of the complex upper extremities formed in humans today. Often, the presence of atavistic muscles is asymptomatic, but they can compromise the function of normal anatomical structures and complicate clinical situations if their presence is unknown. They can cause complications if they are confused with soft tissue pathology, if they compress or displace surrounding structures, or if they require an additional blood supply during times of exercise and stress. The purpose of this paper was to describe the common atavistic muscles, their hypothesized evolutionary origins, their potential complications and possible treatments for the diagnosing clinician., (© 2022 Wiley-VCH GmbH.)

Surface Electromyographic (sEMG) signal is a prime source of information to activate prosthetic hand such that it is able to restore a few basic hand actions of amputee, making it suitable for rehabilitation. In this work, a non-invasive single channel sEMG amplifier is developed that captures sEMG signal for three typical hand actions from the lower elbow muscles of able bodied subjects and amputees. The recorded sEMG signal detrends and has frequencies other than active frequencies. The Empirical Mode Decomposition Detrending Fluctuation Analysis (EMD-DFA) is attempted to de-noise the sEMG signal. A feature vector is formed by extracting eight features in time domain, seven features each in spectral and wavelet domain. Prominent features are selected by Fuzzy Entropy Measure (FEM) to ease the computational complexity and reduce the recognition time of classification. Classification of different hand actions is attempted based on multi-class approach namely Partial Least Squares Discriminant Analysis (PLS-DA) to control the prosthetic hand. It is inferred that an accuracy of 89.72% & 84% is observed for the pointing action whereas the accuracy for closed fist is 81.2% & 79.54% while for spherical grasp it is 80.6% & 76% respectively for normal subjects and amputees. The performance of the classifier is compared with Linear Discriminant Analysis (LDA) and an improvement of 5% in mean accuracy is observed for both normal subjects and amputees. The mean accuracy for all the three different hand actions is significantly high (83.84% & 80.18%) when compared with LDA. The proposed work frame provides a fair mean accuracy in classifying the hand actions of amputees. This methodology thus appears to be useful in actuating the prosthetic hand., (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)

Objectives: To evaluate the capability of hyperspectral imaging (HSI), a contact-less and noninvasive technology, to monitor perfusion changes of the hand during a modified Allen test (MAT) and cuff occlusion test. Furthermore, the study aimed at obtaining objective perfusion parameters of the hand., Methods: HSI of the hand was performed on 20 healthy volunteers with a commercially available HSI system during a MAT and a cuff occlusion test. Besides gathering red-green-blue (RGB) images, the perfusion parameters tissue hemoglobin index (THI), (superficial tissue) hemoglobin oxygenation (StO2), near-infrared perfusion (NIR), and tissue water index (TWI) were calculated for four different regions of interest on the hand. For the MAT, occlusion (OI; the ratio between the condition during occlusion and before occlusion) and reperfusion (RI; the ratio between the non-occlusion state and the prior occlusion state) indices were calculated for each perfusion parameter. All data were correlated to the clinical findings., Results: False-color images showed visible differences between the various perfusion conditions during the MAT and cuff occlusion test. THI, StO2, and NIR behaved as expected from physiology, while TWI did not in the context of this study. During rest, mean THI, StO2, and NIR of the hand were 34 ± 2, 72 ± 9, and 61 ± 6, respectively. The RI for THI showed a roundabout threefold increase after reperfusion of both radial and ulnar artery and was thus, distinctly pronounced when compared with StO2 and NIR (~1.25). The OI was lowest for THI when compared with StO2 and NIR., Conclusions: HSI with its parameters THI, StO2, and NIR proved to be suitable to evaluate perfusion of the hand. By this, it could complement visual inspection during the MAT for evaluating the functionality of the superficial palmary arch before radial or ulnar artery harvest. The presented RI might deliver useful comparative values to detect pathological perfusion disorders at an early stage. As microcirculation monitoring is crucial for many medical issues, HSI shows potential to be used, besides further applications, in the monitoring of (free) flaps and transplants and microcirculation monitoring of critically ill patients., (© 2021 The Authors. Lasers in Surgery and Medicine published by Wiley Periodicals LLC.)

Large intestine 4 (LI4) is a major acupoint used in various treatments in acupuncture and Traditional Chinese Medicine. There are structures associated within the region of LI4 that have three-dimensional anatomical relationship that needs further characterization. The aims of this study were: (a) to observe the anatomical variation of structures around LI4; (b) to observe specific overlap of structures around LI4. A 1256 mm 2 area was dissected in 25 cadaveric hands around LI4. Nondissected areas were marked with pins as reference points. Dissections were photographed with a fixed camera. Subsequently, images were imported to Adobe Photoshop 2020 and analyzed. Descriptive statistics and graphs were compiled using Graphpad Prism 2020. The tributaries of the dorsal venous plexus (22.3%), branches of superficial radial nerve (18.9%), first dorsal interosseous muscle (52.4%), arterial branches in the first interosseous space (10.2%), and deep ulnar nerve (4.0%) were observed in the area of LI4. One branch of the superficial radial nerve passed through LI4. The deep ulnar nerve was found in the bulk of the first dorsal interosseous muscle. Several structures observed intersected at LI4. The superficial radial nerve interweaved with the dorsal venous plexus superficially. The deep ulnar nerve passed anterior to the second palmar metacarpal artery before entering into the first dorsal interosseous muscle. These results provide anatomical evidence and variation into the vascular contributions at LI4., (© 2021 American Association for Anatomy.)

We rely on accurate body representations to successfully interact with the environment. As adults, we rely on many years of experience with a body that has stayed relatively the same size. Children, however, go through periods of rapid growth and whether or not their body representation matches this physical growth is unknown. To address this question, we examined the developmental trajectory of the body model of the hand. The body model is the representation of our bodies that underlies position sense. We recruited a group of children (8-16 years) and a control group of young adults (18-26 years) and asked them to complete the body model task. In this task, participants estimated the location of ten different landmarks (the tips and metacarpophalangeal joints of each of their five fingers). The position (XY location) of each estimate was tracked using an Optotrak camera. From the XY locations we derived hand width and finger length. Not surprisingly, children's physical hand width and finger length were smaller than adults but remarkably, the body model, was similar for both groups. This result indicates that children overestimate hand size and suggests that the body model is ahead of physical growth. This result contradicts the notion that body representation lags physical growth during puberty, accounting for the clumsy motor behaviour characteristic of teens. We discuss the results in relation to the different taxonomies of body representation and how an enlarged representation of the hand during childhood may influence action., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

To communicate with one another hand, gesture is very important. The task of using the hand gesture in technology is influenced by a very common way humans communicate with the natural environment. The recognizing and finding pose estimation of hand comes under the area of hand gesture analysis. To find out the gesturing hand is very difficult than finding the another part of the human body because the hand is smaller in size. The hand has greater complexity and more challenges due to differences between the cultural or individual factors of users and gestures invented from ad hoc. The complication and divergences of finding hand gestures will deeply affect the recognition rate and accuracy. This paper emphasizes on summary of hand gestures technique, recognition methods, merits and demerits, various applications, available data sets, and achieved accuracy rate, classifiers, algorithm, and gesture types. This paper also scrutinizes the performance of traditional and deep learning methods on dynamic hand gesture recognition. [ABSTRACT FROM AUTHOR]

Patients experiencing severe hemiplegia following a stroke struggle to rehabilitate their affected limbs. Cross-education (CE) training emerges as a promising rehabilitation method due to its safety, simplicity, low risk, and ability to effectively improve muscle strength in the affected limb. However, controversy surrounds the neural mechanisms and clinical applications of CE. To address this, we employed functional near-infrared spectroscopy to monitor the response of regions of interest (ROI) and functional connectivity in patients with stroke experiencing severe hemiplegia during one session of 50% maximal voluntary contraction (MVC) strength training with less-affected hand in both subacute and convalescent phases. Our objective was to compare the two stroke groups to gain insight into the potential utility for unilateral training of the less-affected limb as an effective rehabilitation approach during different phases post of stroke. The findings revealed varying degrees of activation in the ROIs within the affected hemisphere across both groups during the task. Additionally, we found that the subacute stroke patients with severe hemiplegia (SPS) had higher blood oxygen levels in the ipsilesional primary motor (iM1), ipsilesional pre-motor and supplementary motor area (iP-SMA) and contralesional P-SMA (cP-SMA). Functional connectivity strength between the iM1 and contralesional brain regions, as well as between the iP-SMA and ipsilesional ROIs, showed statistically significant differences in SPS compared to convalescent stroke patients with severe hemiplegia (CPS) during a 50% MVC strength training session using the less-affected hand. SIGNIFICANCE STATEMENT: Exploring the neural mechanisms underlying one session of 50% MVC strength training with less-affected hand sheds light on a safe therapy. The study enhances our understanding of less-affected hand training and investigates the feasibility as a future rehabilitation approach. Analyzing how one session of 50% MVC strength training with less-affected hand affects brain activation and connectivity could lead to more tailored and effective rehabilitation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

We investigated proprioceptive acuity for location and motion of a never seen hand-held tool (30 cm long rod) and the accuracy of movements to place tool parts in the location of remembered visual targets. Ten blindfolded right-handed subjects (5 females) reached with the tool held in the right hand to touch the tip and midpoint to the stationary and moving left index-tip, to the right and left ear lobes and to remembered visual target locations. We also tested accuracy of left hand rod reaches to the ear lobes to determine if rod dimensions and control of tool movements experienced during right hand tool use could be used to accurately localize the rod parts when held in the left hand. Errors for right hand-held rod-tip movements to touch the stationary and moving left index-tip averaged only about 1 cm larger than observed previously for right hand movements to touch its index-tip to the left index-tip. The tool-tip was localized with lower mean distance errors (about 1 cm) than the tool-midpoint (5.5-6.5 cm) when reaching to touch the ear lobes with the rod in right and left hands. Right hand reaches to place the tool- tip and midpoint in remembered visual target locations were inaccurate with large overshoots of close targets and undershoots of far targets, similar to previous reports for reaching with the right hand to remembered visual targets. These results support the distalization hypothesis, that the tool endpoint becomes the effective upper limb endpoint when using the tool., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Brain Research Organization (IBRO). Published by Elsevier Inc. All rights reserved.)

Background: Simultaneous and proportional control (SPC) based on surface electromyographic (sEMG) signals has emerged as a research hotspot in the field of human-machine interaction (HMI). However, the existing continuous motion estimation methods mostly have an average Pearson coefficient (CC) of less than 0.85, while high-precision methods suffer from the problem of long inference time (> 200 ms) and can only estimate SPC of less than 15 hand movements, which limits their applications in HMI. To overcome these problems, we propose a smooth Multi-scale Attention Patching Encoder Network (sMAPEN)., Methods: The sMAPEN consists of three modules, the Multi-scale Attention Fusion (MAF) module, the Patching Encoder (PE) module, and a smoothing layer. The MAF module adaptively captures the local spatiotemporal features at multiple scales, the PE module acquires the global spatiotemporal features of sEMG, and the smoothing layer further improves prediction stability., Results: To evaluate the performance of the model, we conducted continuous estimation of 40 subjects performing over 40 different hand movements on the Ninapro DB2. The results show that the average Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), coefficient of determination (R 2 ), and smoothness (SMOOTH) of the sMAPEN model are 0.9082, 0.0646°, 0.8163, and - 0.0017, respectively, which significantly outperforms that of the state-of-the-art methods in all metrics (p < 0.01). Furthermore, we tested the deployment performance of sMAPEN on the portable device, with a delay of only 97.93 ms., Conclusions: Our model can predict up to 40 hand movements while achieving the highest predicting accuracy compared with other methods. Besides, the lightweight design strategy brings an improvement in inference speed, which enables the model to be deployed on wearable devices. All these promotions imply that sMAPEN holds great potential in HMI., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: All authors of this paper have read and approved the final version submitted. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enhancing the overall quality of life, particularly in the context of advanced healthcare. This paper presents a novel approach for real-time hand gesture recognition using bio-impedance techniques. The developed device, powered by a Raspberry Pi and connected to electrodes for impedance data acquisition, employs an impedance chip for data collection. To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. The promising results lay a foundation for future developments in nonverbal communication between humans and machines as it contributes to the advancement of technology for the benefit of individuals with hearing impairments, addressing a critical social need., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Consent for publication: All subjects provided informed consent for the publication of identifying information and/or images in this open-access publication, in accordance with ethical standards., (© 2024. The Author(s).)

Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition. Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model's architecture. To address the non-stationarity of sEMG signals, we employ a windowing strategy for signal augmentation within deep learning architectures. The MobileNetV2 encoder and U-Net architecture extract relevant features from sEMG spectrogram images. Edge computing integration is leveraged to further enhance innovation by enabling real-time processing and decision-making closer to the data source. Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3-4% average accuracy improvement and a 10% variance reduction. Notably, Mendeley Data, BioPatRec DB3, and BioPatRec DB1 surpassed advanced models in their respective domains with classification accuracies of 88.71%, 90.2%, and 88.6%, respectively. Experimental results underscore the significant enhancement in generalizability and gesture recognition robustness. This approach offers a fresh perspective on prosthetic management and human-machine interaction, emphasizing its efficacy in improving accuracy and reducing variance for enhanced prosthetic control and interaction with machines through edge computing integration., Competing Interests: Declarations. Consent for publication: All authors have reviewed and consented to the submission and publication of this paper. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Previous research has shown that visual impairment results in reduced audio, tactile and proprioceptive ability. One hypothesis is that these issues arise from inaccurate body representations. Few studies have investigated metric body representations in a visually impaired population. We designed an ecologically valid behavioural task in which visually impaired adults haptically explored various sized gloves or shoes. They were asked to indicate if they perceived each clothing item as bigger than the size of their hand or foot. In the post-hoc analyses we fit psychometric curves to the data to extract the point of subjective equality. We then compared the results to age/sex matched controls. We hypothesized the blind participants body representations should be more distorted. Because previous research has shown that females are more likely to overestimate body size, we predicted sex differences in the sighted participants. However, because blind adults have no exposure to visual ideals of body size, we predicted that there would be no sex differences. Our results showed thatblind participants overestimated their hands and feetto a similar degree. Sighted controls overestimated their hands significantly more than their feet. Taken together, our results partially support our hypothesis and suggest that visual deprivation, even for short periods result in hand size overestimation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of internal cancers. Traditionally, human arsenic exposure is estimated through arsenic levels in biological tissues; however, these methods are invasive and time-consuming. This study aims to develop a noninvasive approach to predict arsenic exposure using artificial intelligence (AI) to analyze photographs of hands and feet. By incorporating well water consumption data and arsenic concentration levels, we developed an AI algorithm trained on 9988 hand and foot photographs from 2497 subjects. This algorithm correlates visual features of palmoplantar hyperkeratosis with arsenic exposure levels. Four pictures per patient, capturing both ventral and dorsal aspects of hands and feet, were analyzed. The AI model utilized existing arsenic exposure data, including arsenic concentration (AC) and cumulative arsenic exposure (CAE), to make binary predictions of high and low arsenic exposure. The AI model achieved an optimal area under the curve (AUC) values of 0.813 for AC and 0.779 for CAE. Recall and precision metrics were 0.729 and 0.705 for CAE, and 0.750 and 0.763 for AC, respectively. While biomarkers have traditionally been used to assess arsenic exposure, efficient noninvasive methods are lacking. To our knowledge, this is the first study to leverage deep learning for noninvasive arsenic exposure assessment. Despite challenges with binary classification due to imbalanced and sparse data, this approach demonstrates the potential for noninvasive estimation of arsenic concentration. Future studies should focus on increasing data volume and categorizing arsenic concentration statistics to enhance model accuracy. This rapid estimation method could significantly contribute to epidemiological studies and aid physicians in diagnosis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Problem: Accommodations for injured and disabled surgical providers have to balance an individual's needs with measures that ensure sterility requirements, patient and provider safety. The highly specialized nature of the surgical environment poses challenges when implementing changes in the operating room and literature is limited on adaptive surgical hand preparation techniques necessary to maximize a disabled medical student's active participation in their surgical clerkship., Intervention: This paper presents a detailed account of the development and implementation of an adaptive surgical hand preparation designed to address mobility needs, enabling a student's active participation and education in the surgical curriculum. This offers a framework for adapting traditional surgical hand preparation techniques for crutches consisting of essential requirements in terms of equipment and personnel, step-by-step guide for implementation, discussion of potential risks related to contamination and safety, and a discussion on future directions for further innovation., Context: An adaptive surgical hand preparation technique was necessary to sterilize forearm crutches for a third-year medical student with a physical disability to ensure accessibility in the operating room and equity in surgical clerkship and medical education. Successful use of this protocol, in over 40 surgical cases throughout an 8-week surgical clerkship, created opportunity for a disabled medical student to access the sterile operating table through collaboration and innovation in the operating room., Impact: The adaptive hand preparation and sterile crutch cover solution was necessary for the student to assist in open, laparoscopic, and surgical procedures resulting in high clinical performance marks in the surgical clerkship. Beyond the individual benefit, this protocol promotes the importance of equity in medication education and encourages diversity through adaptive measures in the surgical field., Lessons Learned: Designing an adaptive sterilization protocol for use of crutches in the operating room serves as an example of educational engineering and adaptable accessibility. The entire collaborative effort involving the medical student, university, surgical providers and operating room staff demonstrates the importance of teamwork in creating access in healthcare settings. Through learned experience, the authors provide insights for future directions for innovation, aiming to enhance access and inclusivity in medical education and surgical practice. This paper reflects on the broader implications of educational engineering and inclusive practices in healthcare., (Copyright © 2024 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

Purpose: Diffusion weighted imaging (DWI) has revealed microstructural changes in lower limb nerves in people with diabetic neuropathy. Microstructural changes in upper limb nerves using DWI in people with diabetes have not yet been explored., Methods: This cross-sectional study aimed to quantify and compare the microstructure of the median and ulnar nerve in people without diabetes (n = 10), people with diabetes without distal symmetrical polyneuropathy (DSPN; n = 10), people with DSPN in the lower limbs only (DSPN FEET ONLY ; n = 12), and people with DSPN in the upper and lower limbs (DSPN HANDS & FEET ; n = 9). DSPN diagnosis included electrodiagnosis and corneal confocal microscopy. Tensor metrics, such as fractional anisotropy, radial diffusivity and axial diffusivity, and constrained spherical deconvolution metrics, such as dispersion and complexity, were calculated. Linear mixed-models were used to quantify DWI metrics from multiple models in median and ulnar nerves across the groups, and to evaluate potential differences in metrics at the wrist and elbow based on the principle of a distal-to-proximal disease progression., Results: Tensor metrics revealed microstructural abnormalities in the median and ulnar nerve in people with DSPN HANDS & FEET , and also already in DSPN FEET ONLY . There were significant negative correlations between electrodiagnostic parameters and tensor metrics. A distal-to-proximal pattern was more pronounced in the median nerve. Non-tensor metrics showed early microstructural changes in people with diabetes without DSPN., Conclusion: Compared to people without diabetes, microstructural changes in upper limb nerves can be identified in people with diabetes with and without DSPN, even before symptoms occur., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Robert E Smith reports a relationship with Australian National Imaging Facility that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Palm-vein has been widely used for biometric recognition due to its resistance to theft and forgery. However, with the emergence of adversarial attacks, most existing palm-vein recognition methods are vulnerable to adversarial image attacks, and to the best of our knowledge, there is still no study specifically focusing on palm-vein image attacks. In this paper, we propose an adversarial palm-vein image attack network that generates highly similar adversarial palm-vein images to the original samples, but with altered palm-identities. Unlike most existing generator-oriented methods that directly learn image features via concatenated convolutional layers, our proposed network first maps palm-vein images into multi-scale high-dimensional shallow representation, and then develops attention-based dual-path feature learning modules to extensively exploit diverse palm-vein-specific features. After that, we design visual-consistency and identity-aware loss functions to specially decouple the visual and identity features to reconstruct the adversarial palm-vein images. By doing this, the visual characteristics of palm-vein images can be largely preserved while the identity information is removed in the adversarial palm-vein images, such that high-aggressive adversarial palm-vein samples can be obtained. Extensive white-box and black-box attack experiments conducted on three widely used databases clearly show the effectiveness of the proposed network., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The sign language uses a combination of complex finger and wrist configurations. The frequency of use of a particular sign is highly dependent on its physiological difficulty. However, no method allows to quantify accurately this difficulty. In the context of paleolithic negative hand paintings this absence of methods is problematic since the hand signs which are painted may be related to a primitive hand sign language. The objective of this study was to develop and validate a method based on electromyography recordings for quantifying sign language difficulty. Electromyography of the six main hand muscles were recorded and analyzed to determine individual muscle activity, summed muscle activity and muscle coactivation. Those results were correlated to subjective scales of difficulties to determine the electromyographic variables and/or the combinations of them which are good candidates for determining hand sign difficulties. Among all variables the summed muscle activities and the thumb muscle coactivation presented the most promising criterion. On the top of that, those criterions presented encouraging correlation with the frequence of occurrence of ten hand paintings of the Gargas Cave which open further studies for analyzing the origin of negative hand paintings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Hand motion intention recognition has been considered as one of the crucial research fields for prosthetic control and rehabilitation medicine. In recent years, surface electromyogram (sEMG) signals that directly reflect human motion information are ideal input sources for prosthetic control and rehabilitation. However, how to effectively extract components from sEMG signals containing abundant limb movement information to improve the accuracy of hand recognition still is a difficult problem. To achieve this goal, this paper proposes a novel hand motion recognition method based on variational mode decomposition (VMD) and ReliefF. First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. Then, four features representing hand motion intentions are extracted from the VMFs to form the initial feature space. Next, the ReliefF algorithm is used to remove redundant features from the feature space. In order to select a feature space that can effectively reflect the intention of hand movements, the hand movement recognition performance of 8 low-dimensional feature spaces is evaluated. Finally, three machine learning methods are used to recognize hand movements. The proposed method was tested on the sEMG for Basic Hand movements Data Set and achieved an average accuracy of 99.14%. Compared with existing research, the proposed method achieves better hand motion recognition performance, indicating the potential for healthcare and rehabilitation applications., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yue Yuan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective . Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spatial feature extraction, capturing temporal dependencies, and representing underlying signal dynamics. Approach . This paper introduces a novel multi-model fusion approach, FusionNet-Long Short-Term Memory (LSTM), designed to address these issues. Specifically, it integrates Convolutional Neural Networks for spatial feature extraction, Gated Recurrent Units and LSTM networks for capturing temporal dependencies, and Autoregressive (AR) models for representing signal dynamics. Main results . Compared to single models and state-of-the-art methods, this fusion approach demonstrates substantial improvements in classification accuracy. Experimental results show that the proposed model achieves an accuracy of 87.1% in cross-subject data classification and 99.1% in within-subject data classification. Additionally, Gradient Boosting Trees were employed to evaluate the significance of various EEG features to the model. Significance . This study highlights the advantages of integrating multiple models and introduces a superior classification model, which is pivotal for the advancement of BCI systems., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance.

To act effectively, one must select actions that satisfy performance criteria. The more important the criteria, the more important it is to satisfy them. This idea leads to the suggestion that a goal of research on action selection should be determining the relative importance of different performance criteria. We pursued this aim by focused on a physical action task: walking to a table, picking up a box with two hands, and moving the box back and forth between two positions. In earlier work (Ozana et al., 2023), university students who did this task stood as they wished when they arrived at the table. It was found that participants adjusted the separation between their feet depending on the inter-target distance and required back-and-forth rate, timed with a metronome. This result was taken to suggest that hand-move distance and rate were prioritized over foot separation in this context. In the two experiments reported here, we asked participants to adopt wide or narrow foot stances. In Experiment 1, we asked participants, with wide or narrow foot stances, to move the box back and forth at a high or low displacements rate specified by a metronome, covering whatever distance they wished. We found that the freely chosen hand-move distances depended on the hand-move rate but hardly depended on the foot separation. In Experiment 2, we asked new participants to adopt the wide or narrow foot spread and to move the box over two required distances, freely choosing the box-move rate. In this case, we found that participants chose hand-move rates that depended on the required box-move distance but hardly depended on the foot separation. We interpret the results of both the previous and current study to suggest that constraints imposed by the postural control system are relatively less important than those related to manual dexterity in whole-body object manipulation tasks. Instead, it appears that the balance and postural control system functions as an agile support to the manual control system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This paper is intended to support hand surgeons who, at the beginning of their research career, are planning a clinical study. Besides establishing the research methodology of the study, the organizational planning of the work itself is essential. A feasibility analysis carried out before or during the writing of the study protocol helps to estimate the required resources and duration of a project. We highlight some tips and tricks as well as provide checklists that outline the important points to consider before starting a study., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Copyright of Basrah Journal of Science / Magallat Al-Barat Li-L-ulum is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, COVID-19 is posing a serious threat to global human health. The features of hand veins in infrared environments have many advantages, including non-contact acquisition, security, privacy, etc., which can remarkably reduce the risks of COVID-19. Therefore, this paper builds an interactive system, which can recognize hand gestures and track hands for palmvein recognition in infrared environments. The gesture contours are extracted and input into an improved convolutional neural network for gesture recognition. The hand is tracked based on key point detection. Because the hand gesture commands are randomly generated and the hand vein features are extracted from the infrared environment, the anti-counterfeiting performance is obviously improved. In addition, hand tracking is conducted after gesture recognition, which prevents the escape of the hand from the camera view range, so it ensures that the hand used for palmvein recognition is identical to the hand used during gesture recognition. The experimental results show that the proposed gesture recognition method performs satisfactorily on our dataset, and the hand tracking method has good robustness. [ABSTRACT FROM AUTHOR]

This paper presents Multi-view Leap2 Hand Pose Dataset (ML2HP Dataset), a new dataset for hand pose recognition, captured using a multi-view recording setup with two Leap Motion Controller 2 devices. This dataset encompasses a diverse range of hand poses, recorded from different angles to ensure comprehensive coverage. The dataset includes real images with the associated precise and automatic hand properties, such as landmark coordinates, velocities, orientations, and finger widths. This dataset has been meticulously designed and curated to maintain a balance in terms of subjects, hand poses, and the usage of right or left hand, ensuring fairness and parity. The content includes 714,000 instances from 21 subjects of 17 different hand poses (including real images and 247 associated hand properties). The multi-view setup is necessary to mitigate hand occlusion phenomena, ensuring continuous tracking and pose estimation required in real human-computer interaction applications. This dataset contributes to advancing the field of multimodal hand pose recognition by providing a valuable resource for developing advanced artificial intelligence human computer interfaces., (© 2024. The Author(s).)

This article explores the role of sensorimotor engagement in students' learning of a challenging science, technology, engineering, and math-related concept. Previous research has failed to distinguish two features commonly associated with embodiment: sensorimotor engagement and visuospatial concreteness. In the current research, we ask whether sensorimotor engagement-operationalized as watching a video of hands manipulating paper representations-offers unique benefits beyond the visuospatial concreteness of a dynamic visualization of the same process. Participants were randomly assigned to one of three conditions to learn about the shuffle() function in R: a Watch Hands Moving Objects group, which watched a video with hands; a Watch Moving Objects group, which watched a video with a dynamic visualization in which objects moved without hands; or a control group, which watched a live-coding video that did not include either hands or visuospatial representations. Results revealed that only participants in the Watch Hands Moving Objects group demonstrated significantly superior performance compared with both the Watch Moving Objects group and control groups. These findings highlight the unique benefit of sensorimotor engagement for learning, contributing to a deeper understanding of how embodiment can enhance the learning process. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: The Hand10 questionnaire is a short, easy-to-understand, visual questionnaire developed for the functional assessment of upper extremity disorders. It consists of visuals of each item as well as facial expressions that reflect the degree of difficulty during the function. It has been stated that the Hand10 questionnaire is suitable for application in the elderly population due to its stated features., Objective: This study aimed to evaluate the validity of the Hand10 questionnaire in hand osteoarthritis, the prevalence of which increases with age., Methods: Patients filled the Hand10, Disabilities of the Arm, Shoulder, and Hand (DASH), and Australian/Canadian Osteoarthritis Hand Index (AUSCAN) questionnaires, once. Validity of Hand10 was determined with DASH and AUSCAN questionnaires using Pearson Correlation Coefficient analysis., Results: Sixty patients were enrolled in the study. The Hand10 score of the patients was 45.15 ± 25.81; DASH score was 76.31 ± 26.37, and AUSCAN score was 32.75 ± 15.58. The analysis revealed that Hand10 scores showed a positive correlation with DASH scores at a "very good" level (r = 0.71; p < 0.001; 95% confidence interval (CI) [0.55, 0.82]) and AUSCAN scores at an "excellent" level (r = 0.76; p < 0.001; 95% CI [0.63, 0.85])., Conclusion: The Hand10 questionnaire is a valid tool in patients with hand OA. This questionnaire, which consists of ten items and includes pictures that have a positive effect on repeatability, is very practical in evaluating hand function in upper extremity disorders, especially in older age individuals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: There is currently a lack of easy-to-use and effective robotic devices for upper-limb rehabilitation after stroke. Importantly, most current systems lack the provision of somatosensory information that is congruent with the virtual training task. This paper introduces a novel haptic robotic system designed for upper-limb rehabilitation, focusing on enhancing sensorimotor rehabilitation through comprehensive haptic rendering., Methods: We developed a novel haptic rehabilitation device with a unique combination of degrees of freedom that allows the virtual training of functional reach and grasp tasks, where we use a physics engine-based haptic rendering method to render whole-hand interactions between the patients' hands and virtual tangible objects. To evaluate the feasibility of our system, we performed a clinical mixed-method usability study with seven patients and seven therapists working in neurorehabilitation. We employed standardized questionnaires to gather quantitative data and performed semi-structured interviews with all participants to gain qualitative insights into the perceived usability and usefulness of our technological solution., Results: The device demonstrated ease of use and adaptability to various hand sizes without extensive setup. Therapists and patients reported high satisfaction levels, with the system facilitating engaging and meaningful rehabilitation exercises. Participants provided notably positive feedback, particularly emphasizing the system's available degrees of freedom and its haptic rendering capabilities. Therapists expressed confidence in the transferability of sensorimotor skills learned with our system to activities of daily living, although further investigation is needed to confirm this., Conclusion: The novel haptic robotic system effectively supports upper-limb rehabilitation post-stroke, offering high-fidelity haptic feedback and engaging training tasks. Its clinical usability, combined with positive feedback from both therapists and patients, underscores its potential to enhance robotic neurorehabilitation., (© 2024. The Author(s).)

Hand gesture recognition plays a significant role in human-to-human and human-to-machine interactions. Currently, most hand gesture detection methods rely on fixed hand gesture recognition. However, with the diversity and variability of hand gestures in daily life, this paper proposes a registerable hand gesture recognition approach based on Triple Loss. By learning the differences between different hand gestures, it can cluster them and identify newly added gestures. This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Additionally, it proposes a normalization method for transforming hand gesture data and a FingerComb block for combining and extracting hand gesture data to enhance features and accelerate model convergence. It also improves ResNet and introduces FingerNet for registerable single-hand gesture recognition. The proposed model performs well on the RGDS dataset. The system is registerable, allowing users to flexibly register their own hand gestures for personalized gesture recognition.

Introduction: Several studies have assessed and validated the impact of exoskeletons on back and shoulder muscle activation; however, limited research has explored the role that exoskeletons could play in mitigating lower arm-related disorders. This study assessed the impact of Ironhand, an active hand exoskeleton (H-EXO) designed to reduce grip force exertion, on worker exertion levels using a two-phase experimental design., Method: Ten male participants performed a controlled, simulated drilling activity, while three male participants completed an uncontrolled concrete demolition activity. The impact of the exoskeleton was assessed in terms of muscle activity across three different muscles using electromyography (EMG), perceived exertion, and perceived effectiveness., Results: Results indicate that peak muscle activation decreased across the target muscle group when the H-EXO was used, with the greatest reduction (27%) observed in the Extensor Carpi Radialis (ECR). Using the exoskeleton in controlled conditions did not significantly influence perceived exertion levels. Users indicated that the H-EXO was a valuable technology and expressed willingness to use it for future tasks., Practical Applications: This study showcases how glove-based exoskeletons can potentially reduce wrist-related disorders, thereby improving safety and productivity among workers. Future work should assess the impact of the H-EXO in various tasks, different work environments and configurations, and among diverse user groups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

The cold-induced vasodilation (CIVD) response of the human body to Arctic-like environments helps delay or prevent cold injuries to peripheral regions, such as the hands and feet. To more comprehensively predict the thermal responses of these body regions to cold stress, here we extended our previously developed and validated anatomically accurate three-dimensional whole-body thermoregulatory human model by incorporating a new phenomenological formulation of the CIVD mechanism. In this formulation, we modulated the cyclic vasodilation and vasoconstriction flow of warm blood from the body core to the peripheral regions solely by determining the heat-transfer exchange between the skin and the surrounding environment, and deactivated it when the core body temperature decreased to 36.5 °C. In total, we calibrated and validated the model using eight distinct studies involving 153 unique male subjects exposed to 10 diverse experimental conditions, including cold-air exposure of the whole body as well as air exposure and cold-water immersion of the hand or the foot. With CIVD incorporated, the model predictions generally yielded root mean square errors (RMSEs) of <3.0 °C for skin temperature, which represented a reduction of up to 3.6 °C compared to when we did not consider CIVD. Similarly, the incorporation of CIVD increased the fraction of predictions within two standard errors of the measured data by up to 63 %. The model predictions yielded RMSEs for core body temperature of <0.2 °C. The model can be used to provide guidelines to reduce the risk of cold-related injuries during prolonged exposures to very-cold environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced the work reported in this paper., (Published by Elsevier Ltd.)

In real-time human-machine interaction (HMI) applications, hand gesture recognition (HGR) requires high accuracy with low latency. Surface electromyography (sEMG), a physiological electrical signal reflecting muscle activation, is extensively used in HMI. Recently, transient sEMG, generated during the gesture transitions, has been employed in HGR to achieve lower observational latency compared to steady-state sEMG. However, the use of long feature windows (up to 200 ms) still make it less desirable in low-latency HMI. In addition, most studies have relied on remote computing, where remote data processing and large data transfer result in high computation and network latency. In this paper, we proposed a method leveraging transient high density sEMG (HD-sEMG) and in-sensor computing to achieve low-latency HGR. An sEMG contrastive convolution network (sCCN) was proposed for HGR. The mean absolute value and its average integration were used to train the sCCN in a contrastive learning manner. In addition, all signal acquisition, data processing, and pattern recognition processes were deployed within designed sensor for in-sensor computing. Compared to the state-of-the-art study using multi-channel 200-ms transient sEMG, our proposed method achieved a comparable HGR accuracy of 0.963, and a 58% lower observational latency of only 84 ms. In-sensor computing realizes a 4 times lower computation latency of 3 ms, and significantly reduces the network latency to 2 ms. The proposed method offers a promising approach to achieving low-latency HGR without compromising accuracy. This facilitates real-time HMI in biomedical applications such as prostheses, exoskeletons, virtual reality, and video games.

In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor coordinated drive mechanism, a thumb coupling transmission mechanism and a thumb steering mechanism are proposed. These innovative designs allow for more effective actuation in each finger, enhancing the load capacity of the robotic hand while maintaining key performance indicators such as dexterity and adaptability. A mechanical model of the robotic finger was made to determine the application limitations and load capacity. The robotic hand was then prototyped for a set of experiments. The experimental results showed that the proposed theoretical model were reliable. Also, the fingertip force of the robotic finger could reach up to 10.3 N, and the load force could reach up to 72.8 N. When grasping target objects of different sizes and shapes, the robotic hand was able to perform the various power grasping and precision grasping in the Cutkosky taxonomy. Moreover, the robotic hand had good flexibility and adaptability by means of adjusting the envelope state autonomously., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.

Background: Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for the automatic and objective disease evaluation from video recordings of standardised motor tasks, which are the foundation of neurological examinations. In view of this scenario, this narrative review aims to describe the state of the art and the future perspective of DL frameworks for hand tracking in video-based PD assessment., Methods: A rigorous search of PubMed, Web of Science, IEEE Explorer, and Scopus until October 2023 using primary keywords such as parkinson, hand tracking, and deep learning was performed to select eligible by focusing on video-based PD assessment through DL-driven hand tracking frameworks RESULTS:: After accurate screening, 23 publications met the selection criteria. These studies used various solutions, from well-established pose estimation frameworks, like OpenPose and MediaPipe, to custom deep architectures designed to accurately track hand and finger movements and extract relevant disease features. Estimated hand tracking data were then used to differentiate PD patients from healthy individuals, characterise symptoms such as tremors and bradykinesia, or regress the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) by automatically assessing clinical tasks such as finger tapping, hand movements, and pronation-supination., Conclusions: DL-driven hand tracking holds promise for PD assessment, offering precise, objective measurements for early diagnosis and monitoring, especially in a telemedicine scenario. However, to ensure clinical acceptance, standardisation and validation are crucial. Future research should prioritise large open datasets, rigorous validation on patients, and the investigation of new frontiers such as tracking hand-hand and hand-object interactions for daily-life tasks assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

According to the predictive coding account, the attenuation of tactile perception on the hand exposed to the visuo-tactile Rubber Hand Illusion (vtRHI) relies on a weight increase of visual information deriving from the fake hand and a weight decrease of tactile information deriving from the individual's hand. To explore if this diametrical modulation persists in the absence of vision when adopting the somatic RHI (sRHI), we recorded tactile acuity measures before and after both RHI paradigms in 31 healthy individuals, hypothesizing a weight decrease for somatosensory information deriving from the hand undergoing the illusion and a weight increase for those deriving from the contralateral hand in the sRHI. Our results showed a significant overall decrease in tactile acuity on the hand undergoing the illusion whilst no changes emerged on the contralateral hand during sRHI. Since the sRHI was not accompanied by the hand spatial remapping, despite the generation of the feeling of ownership toward the fake hand, we hypothesized spatial remapping might play a pivotal role in determining sensory information weight attribution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The natural occurrence of 16 inorganic ions relevant to forensic explosives investigations on human hands was studied to support the evaluation of activity-level propositions when such traces are found on the hands or in the fingerprints of a suspect. A total of 594 hand swab extracts from 297 participants throughout Europe and the United States of America were analyzed using Ion Chromatography - Mass Spectrometry. The data provides a reference framework for future covert investigations and forensic casework. The results indicate that thiocyanate, chlorate, nitrite, lithium, strontium, and barium are rarely detected on the hands of individuals who have had no direct contact with explosives (P<0.03) and in quantities below 6 µg. Perchlorate contamination sporadically occurs without deliberately handling perchlorates (P=0.03), albeit at low levels (<12 µg). It also seems that the presence of perchlorate on hands is generally related to professions that involve explosives. Detecting substantial amounts of any of these rare ions on a suspect's hands would require a specific explanation. Because legitimate activities exist that can also result in elevated levels of ions of interest on hands, the context surrounding their presence has to be carefully assessed for each individual case., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The automation of hand hygiene monitoring is critical in healthcare for ensuring clean hands and preventing infectious disease spread. While advancements have been made, existing methods have limitations in accurately detecting and classifying handwashing actions. This paper addresses these limitations and introduces the Residual-Based Multilevel Fused Network (ResMFuse-Net) as a novel approach to automate the quality assurance of hand hygiene procedures. Our model integrates advanced techniques, including feature fusion, model compression, a feature fusion block (FFB), and a modified separable residual block (SE-ResB). The proposed model fused two networks into one trainable feature extraction pipeline, and applies model compression to retain the core blocks that are crucial for propagating strong and robust features while conserving a significant fraction of the computing resources. Additionally, we introduce a FFB that includes ConvLSTM and alpha dropout to learn spatial dependencies, establish correlations between frames in a video, and mitigate overfitting. This paper introduces a SE-ResB, which is a customized residual component composed of separable convolutions and LeakyReLU activation. The SE-ResB is incorporated to handle the fused features and generate a more diverse set of features, leading to considerable performance enhancements. This study also includes an ablation analysis that highlights the importance of each component. The proposed ResMFuse-Net is evaluated on two datasets: a newly created handwashing dataset (451 videos) and a publicly available dataset (656 videos). Achieving a recognition accuracy of 97.61% on the handwashing dataset and 98.69% on the other dataset, the ResMFuse-Net outperforms previous methods with fewer parameters and FLOPs, demonstrating its efficiency and cost-effectiveness. [ABSTRACT FROM AUTHOR]