Search Results (73)

Hand motion monitoring plays a vital role in medical rehabilitation, sports training, and human–computer interaction. High-sensitivity wearable biosensors are essential for accurate gesture recognition and precise motion analysis. In this work, we propose a high-sensitivity wearable glove based on a push–pull optical fiber sensor, designed to enhance the sensitivity and accuracy of hand motion biosensing. The sensor employs diagonal core reflectors fabricated at the tip of a four-core fiber, which interconnect symmetric fiber channels to form a push–pull sensing mechanism. This mechanism induces opposite wavelength shifts in fiber Bragg gratings positioned symmetrically under bending, effectively decoupling temperature and strain effects while significantly enhancing bending sensitivity. Experimental results demonstrate superior bending-sensing performance, establishing a solid foundation for high-precision gesture recognition. The integrated wearable glove offers a compact, flexible structure and straightforward fabrication process, with promising applications in precision medicine, intelligent human–machine interaction, virtual reality, and continuous health monitoring. Full article

Worldwide, the healthcare industry produces massive quantities of medical waste (MW), most of which is incinerated, releasing large quantities of dioxins, mercury, and other pollutants. Despite this, only a limited number of studies have explored the incorporation of MW into construction materials, with a special focus on cement-based construction materials (CB-CMs). However, to the best of the authors’ knowledge, no existing review formally structures, summarizes, correlates, and discusses the findings of previous studies on MW in CB-CMs to encourage further research and applications of this promising alternative. Therefore, the added value of this study lies in providing an innovative and critical analysis of existing research on the use of MW in CB-CMs, consolidating and evaluating dispersed findings through a systematic literature review, enhancing understanding of the topic, and identifying knowledge gaps to guide future research. A robust systematic literature review was conducted, encompassing 40 peer-reviewed research articles, retrieved from the Web of Science Core Collection database. The methodology involved a three-stage process: a descriptive analysis of the included articles, the identification and synthesis of key thematic areas, and a critical evaluation of the data to ensure a rigorous and systematic report. The selection criteria prioritized peer-reviewed research articles in English with full text availability published in the last 7 years, explicitly excluding conference papers, book chapters, short reports, and articles not meeting the language or accessibility requirements. The results indicate that the influence of MW in CB-CM varies significantly. For example, while the incorporation of face masks as fiber reinforcement in concrete generally enhances its mechanical and durability properties, the use of gloves is less effective and not always recommended. Finally, it was found that further research is needed in this field due to its novelty. Full article

In the field of rehabilitation exoskeletons, addressing the needs of users and healthcare professionals is essential for the development of effective medical technologies. This study presents a user-centered evaluation of the ARTH-aid ExoGlove, a proposed rehabilitation device for muscle atrophy caused by rheumatoid arthritis. This study assesses usability and user experience through therapist and patient feedback, focusing on ergonomic and functional aspects. Ensuring compliance with usability and adaptability criteria is crucial before implementation to enhance effectiveness in clinical settings. While this study includes user feedback, further technical validation and refinement of the system are needed. The findings provide insights into patients’ perceptions of usability and impact on mobility, as well as therapists’ perspectives on its potential integration into rehabilitation protocols. These results highlight the importance of iterative validation and reinforce the role of rehabilitation technologies in complementing traditional therapeutic approaches and advancing patient-centered innovation in biomedical design. Full article

Medical palpation is a task that traditionally requires a skilled practitioner to assess and diagnose a patient through direct touch and manipulation of their body. In regions with a shortage of such professionals, robotic hands or sensorized gloves could potentially capture the necessary haptic information during palpation exams and relay it to medical doctors for diagnosis. From an engineering perspective, a comprehensive understanding of the relevant motions and forces is essential for designing haptic technologies capable of fully capturing this information. This study focuses on thyroid examination palpation, aiming to analyze the hand motions and forces applied to the patient’s skin during the procedure. We identified key palpation techniques through video recordings and interviews and measured the force characteristics during palpation performed by both non-medical participants and medical professionals. Our findings revealed five primary palpation hand motions and characterized the multi-dimensional interaction forces involved in these motions. These insights provide critical design guidelines for developing haptic sensing and display technologies optimized for remote thyroid nodule palpation and diagnosis. Full article

Personal protective equipment (PPE) is crucial for infection prevention and is effective only when worn correctly and consistently. Health organizations often use education or inspections to mitigate non-compliance, but these are costly and have limited success. This study developed a novel on-device, AI-based computer vision system to monitor healthcare worker PPE adherence in real time. Using a custom-built image dataset of 7142 images of 11 participants wearing various combinations of PPE (mask, gloves, gown), we trained a series of binary classifiers for each PPE item. By utilizing a lightweight MobileNetV3 model, we optimized the system for edge computing on a Raspberry Pi 5 single-board computer, enabling rapid image processing without the need for external servers. Our models achieved high accuracy in identifying individual PPE items (93–97%), with an overall accuracy of 85.58 ± 0.82% when all items were correctly classified. Real-time evaluation with 11 unseen medical staff in a cardiac intensive care unit demonstrated the practical viability of our system, maintaining a high per-item accuracy of 87–89%. This study highlights the potential for AI-driven solutions to significantly improve PPE compliance in healthcare settings, offering a cost-effective, efficient, and reliable tool for enhancing patient safety and mitigating infection risks. Full article

The study investigated the determinants of safe pesticide handling and application among farmers in rural communities of Oyo State, ssouthwestern Nigeria. A cross-sectional design utilizing 2-stage cluster sampling techniques was used to select Ido and Ibarapa central Local Government Areas and to interview 383 farmers via a structured questionnaire. Data were analyzed using descriptive statistics and logistic regression at p = 0.05. Results showed that 41.8% of the farmers had been working with pesticides on farms for at least 5 years, 33.0% attended training on pesticide application, 73.5% had good safety and health knowledge, and 72.3% had safe pesticide handling and application practices. About half (50.2%) stated that they wear coveralls, gloves, and masks to protect their body, face, and hands when applying pesticides, 9.8% use empty pesticide containers for other purposes in the house/farm, while 11.5% blow the nozzle with their mouth to unclog it if it becomes blocked. The three major health symptoms reported by the participants were skin irritation (65.0%), itchy eyes (51.3%), and excessive sweating (32.5%). Having attended training on pesticide application and use enhanced (OR = 2.821; C.I = 1.513–5.261) practicing safe pesticide handling and application. Farmers with good knowledge (OR = 5.494; C.I = 3.385–8.919) were more likely to practice safe pesticide handling and application than those with poor knowledge about pesticide use. It is essential to develop and deliver mandatory comprehensive training programs for farmers on impacts of pesticides on health and environment, along with sustainable safe handling, application, and disposal of pesticides using proper waste management techniques and recognizing early signs and seeking medical assistance. The urgent need to strengthen policy to regulate pesticide use and limit farmers’ access to banned products is also key. Full article

Hand−tracking systems are widely employed for telemanipulating grippers with high degrees of freedom (DOFs) such as an anthropomorphic robotic hand (ARH). However, tracking human thumb motion is challenging due to the complex motion of the carpometacarpal (CMC) joint. Existing hand−tracking systems can track the motion of simple joints with one DOF, but most fail to track the motion of the CMC joint, or to do so, there is a need for expensive and intricately set up hardware systems. This research introduces and realizes an affordable and personalizable tracking device to capture the CMC joint Flexion/Extension and Abduction/Adduction motions. Tracked human thumb motion is mapped to a robot thumb in a hybrid approach: the proposed algorithm maps the CMC joint motion to the first two joints of the robot thumb, while joint mapping is established between the metacarpophalangeal and interphalangeal joints to the last two joints. When the tracking device is paired with a flex glove outfitted with bend sensors, the developed system provides the means to telemanipulate an ARH with a four-DOF thumb and one-DOF underactuated digits. A three-stage framework is proposed to telemanipulate the fully actuated robot thumb. The tracking device and framework were evaluated through a device operation and personalization test, as well as a framework verification test. Two volunteers successfully personalized, calibrated, and tested the device using the proposed mapping algorithm. One volunteer further evaluated the framework by performing hand poses and grasps, demonstrating effective control of the robot thumb for precision and power grasps in coordination with the other digits. The successful results support expanding the system and further evaluating it as a research platform for studying human–robot interaction in grasping tasks or in manufacturing, assistive, or medical domains. Full article

A therapeutic glove, which enables medical non-professionals to perform physiotherapeutic gripping and holding movements on patients, would significantly improve the healthcare situation in physiotherapy. The glove aims to detect the orthogonal pressure load and provide feedback to the user. The use of textile materials for the glove assures comfort and a good fit for the user. This, in turn, implies a textile realization of the sensor system in order to manufacture both the glove and the sensor system in as few process steps as possible, using only one textile manufacturing technique. The flat knitting technology is an obvious choice here. The aim of the study is to develop a textile capacitive pressure sensor that can be integrated into the fingertips of a glove using flat knitting technology and to evaluate its sensor properties with regard to transmission behavior, hysteresis and drift. It was shown that the proposed method of a flat knitting sensor fabrication is suitable for producing both the sensors and the glove in one single process step. In addition, the implementation of an entire glove with integrated pressure sensors, including the necessary electrical connection of the sensor electrodes via knitted conductive paths in three fingers, was successfully demonstrated. Full article

The restoration of fine motor function in the hand is crucial for stroke survivors with hemiplegia to reintegrate into daily life and presents a significant challenge in post-stroke rehabilitation. Current mirror rehabilitation systems based on wearable devices require medical professionals or caregivers to assist patients in donning sensor gloves on the healthy side, thus hindering autonomous training, increasing labor costs, and imposing psychological burdens on patients. This study developed a low-load wearable hand function mirror rehabilitation robotic system based on visual gesture recognition. The system incorporates an active visual apparatus capable of adjusting its position and viewpoint autonomously, enabling the subtle monitoring of the healthy side’s gesture throughout the rehabilitation process. Consequently, patients only need to wear the device on their impaired hand to complete the mirror training, facilitating independent rehabilitation exercises. An algorithm based on hand key point gesture recognition was developed, which is capable of automatically identifying eight distinct gestures. Additionally, the system supports remote audio–video interaction during training sessions, addressing the lack of professional guidance in independent rehabilitation. A prototype of the system was constructed, a dataset for hand gesture recognition was collected, and the system’s performance as well as functionality were rigorously tested. The results indicate that the gesture recognition accuracy exceeds 90% under ten-fold cross-validation. The system enables operators to independently complete hand rehabilitation training, while the active visual system accommodates a patient’s rehabilitation needs across different postures. This study explores methods for autonomous hand function rehabilitation training, thereby offering valuable insights for future research on hand function recovery. Full article

(1) Background: As digital health technology evolves, the role of accurate medical-gloved hand tracking is becoming more important for the assessment and training of practitioners to reduce procedural errors in clinical settings. (2) Method: This study utilized computer vision for hand pose estimation to model skeletal hand movements during in situ aseptic drug compounding procedures. High-definition video cameras recorded hand movements while practitioners wore medical gloves of different colors. Hand poses were manually annotated, and machine learning models were developed and trained using the DeepLabCut interface via an 80/20 training/testing split. (3) Results: The developed model achieved an average root mean square error (RMSE) of 5.89 pixels across the training data set and 10.06 pixels across the test set. When excluding keypoints with a confidence value below 60%, the test set RMSE improved to 7.48 pixels, reflecting high accuracy in hand pose tracking. (4) Conclusions: The developed hand pose estimation model effectively tracks hand movements across both controlled and in situ drug compounding contexts, offering a first-of-its-kind medical glove hand tracking method. This model holds potential for enhancing clinical training and ensuring procedural safety, particularly in tasks requiring high precision such as drug compounding. Full article

Study Design: Face and content validation of a surgical simulation model. Objective: Accidental transection of the inferior alveolar nerve (IAN) during bilateral sagittal split osteotomies (BSSO) has a reported incidence of up to 7%, determining important sensory disturbances in patients. Proper repair demands the need of microsurgical anastomosis skills. No previous training models have been described to simulate this. Therefore, we present a validated simulation model for intraoral repair of transected IAN. Methods: A CT scan of an orthognathic surgery patient was modified and a 3D model of a mandible with BSSO was printed. Chicken thigh anatomy was reviewed, and 2.5 mm sciatic nerves were dissected and mounted in the model. In order to simulate intraoral work depth, it was put inside a dental phantom or medical glove box. The model was tested by a group of experts (n = 12), simulating a transected IAN repair inside the mouth with both loupes and a double visor surgical training microscope. A survey was conducted to assess Face and Content validity. Results: The model was named RIANOS after Repair of Inferior Alveolar Nerve in Orthognathic Surgery Simulator. The printing cost of each model was approximately US$3 and the design file is open-source and available for download. All experts “Strongly Agreed” that the model was useful for training inferior alveolar nerve microsurgical repair and would consider implementing it with their residents. Conclusions: We developed a low cost, reproducible, open-source simulator for IAN injury repair training during BSSO. Face and Content validity was achieved through evaluation by a group of experts. Full article

Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance. Full article

4-Nonylphenol belongs to the alkylphenol group of chemicals, and its high occurrence in the environment can cause an adverse effect on human health. Breast milk can serve as a marker to take measure of human exposure to these chemicals through different routes of exposure. In this work, the influence of selected factors (the kind of water drank by the mothers; the consumption of fish, pork, and beef; wearing gloves; using nail polish, gel nails, vitamins, and medication) on the concentration on 4-nonylphenol in 89 breast milk samples was studied. The concentrations of nonylphenol in breast milk were determined by HPLC with fluorescence detection. The lowest and highest concentrations of 4-nonylphenol in breast milk were 0.97 ng/mL and 4.37 ng/mL, respectively. Statistical significance was observed for the consumption of pork (p = 0.048) and fish (0.041) in relation to the 4-nonylphenol concentration. Certain parameters (use of gel nails, beef consumption, and vitamin supplementation) were at the border of statistical significance (p = 0.06). Other parameters did not show any statistical significance. The results showed that breast milk in Slovakia does not contain a harmful dose of 4-nonylphenol and does not cause health problems. But it is necessary to continue this research and perform extended screening on a larger number of samples. Full article

Natural rubber (NR) latex derived from Hevea brasiliensis is a complex colloid comprising mainly rubber hydrocarbons (latex particles) and a multitude of minor non-rubber constituents such as non-rubber particles, proteins, lipids, carbohydrates, and soluble organic and inorganic substances. NR latex is susceptible to enzymatic attack after it leaves the trees. It is usually preserved with ammonia and, to a lesser extent, with other preservatives to enhance its colloidal stability during storage. Despite numerous studies in the literature on the influence of rubber proteins on NR latex stability, issues regarding the effect of protein hydrolysis in the presence of ammonia on latex stability during storage are still far from resolved. The present work aims to elucidate the interplay between protein hydrolysis and ammoniation in NR latex stability. Both high- and low-ammonia (with a secondary preservative) NR latexes were used to monitor the changes in their protein compositions during storage. High-ammonia (FNR-A) latex preserved with 0.6% (v/v) ammonia, a low 0.1% ammonia/TMTD/ZnO (FNR-TZ) latex, and a deproteinized NR (PDNR) latex were labeled with fluorescence agents and observed using confocal laser scanning microscopy to determine their protein composition. Protein hydrolysis was confirmed via sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). The results revealed that protein hydrolysis increased with the storage duration. The change in protein composition accompanying hydrolysis also allows the spatial distribution of allergenic proteins to be estimated in the latex. Concurrently, the latex stability increased with the storage duration, as measured by the latex’s mechanical stability time (MST) and the zeta potential of the latex particles. As monitored by AFM, the surface roughness of the NR latex film increased markedly during extended storage compared with that of the DPNR latex, which remained smooth. These results underscore the pivotal role of ammonia in bolstering NR latex stability brought on by protein hydrolysis, which greatly impacts latex film’s formation behavior. NR latex stability underpins the quality of latex-dipped goods during manufacturing, particularly those for medical gloves. Full article

Natural rubber, sourced from Hevea brasiliensis trees mainly in southeast Asia, is a critically important resource for transportation, national security, and medical products, among other uses. The guayule shrub is a domestic alternative source of natural rubber that is emerging with advantages over Hevea since it is well-suited for many medical and consumer applications. Biochar is a sustainable form of carbon made from biomass that is a potential replacement for petroleum-sourced carbon black, the most common filler for rubber composites. The coppiced-wood species hybrid poplar (Populus × canadensis) and Paulownia elongata are both rapidly growing hardwoods that have shown promise as feedstocks for biochar that can be used as fillers in common rubber composites such as Hevea natural rubber, styrene-butadiene, and polybutadiene. In this work, poplar and paulownia biochars were used to partially replace carbon black as filler in guayule rubber composites. Guayule composites with up to 60% of the carbon black replaced with poplar or paulownia biochar had higher tensile strength, elongation, and toughness compared to the 100% carbon black-filled control. These composites would be excellent candidates for rubber applications such as gloves, belts, hoses, and seals, while reducing dependence on fossil fuels and Hevea natural rubber. Full article