Search Results (214)

Working with a chainsaw is a risk for arborists. When handling a chainsaw, serious injuries can occur, particularly to the arms. For this reason, arborists must wear personal protective equipment (PPE) against chainsaws. This protective equipment corresponds to PPE category 3. Current protective gloves have several textile layers as protection. These gloves offer protection up to a chain speed of 24 m/s (class 2). No protective gloves for class 3 are available. A new approach is the solution presented in the paper, in which a smart glove and a modified electrical chainsaw can close this gap. For the development of the glove, the typical work situation, current accident situations, and accident statistics were analyzed. The legal requirements and standards for the European market and the wearing comfort are discussed; based on these data, gloves were designed that included electronics, and a chainsaw was configured accordingly. The glove was then tested under laboratory conditions to see whether the electronic functions in the glove could switch off the saw as soon as the glove came too close to it. The project showed the potential for smart textiles to overcome the limits in the layering of protective textiles. Full article

In this article, we present a model for analyzing the co-occurrence count data derived from practical fields such as user–item or item–item data from online shopping platforms and co-occurring word–word pairs in sequences of texts. Such data contain important information for developing recommender systems or studying the relevance of items or words from non-numerical sources. Different from traditional regression models, there are no observations for covariates. Additionally, the co-occurrence matrix is typically of such high dimension that it does not fit into a computer’s memory for modeling. We extract numerical data by defining windows of co-occurrence using weighted counts on the continuous scale. Positive probability mass is allowed for zero observations. We present the Shared Parameter Alternating Tweedie (SA-Tweedie) model and an algorithm to estimate the parameters. We introduce a learning rate adjustment used along with the Fisher scoring method in the inner loop to help the algorithm stay on track with optimizing direction. Gradient descent with the Adam update was also considered as an alternative method for the estimation. Simulation studies showed that our algorithm with Fisher scoring and learning rate adjustment outperforms the other two methods. We applied SA-Tweedie to English-language Wikipedia dump data to obtain dense vector representations for WordPiece tokens. The vector representation embeddings were then used in an application of the Named Entity Recognition (NER) task. The SA-Tweedie embeddings significantly outperform GloVe, random, and BERT embeddings in the NER task. A notable strength of the SA-Tweedie embedding is that the number of parameters and training cost for SA-Tweedie are only a tiny fraction of those for BERT. 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

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging key low-cost components such as Arduino Nano to process data, MPU6050 to detect hand movements, flexible sensors for easy throttle control, and the nRF24L01 module for wireless communication. The proposed research highlights the design methodology of reporting flight tests associated with simulation findings to demonstrate the characteristics of Smart Glove v1.0 in terms of intuitive, responsive, and hands-free piloting gesture interface. We aim to make the drone piloting experience more enjoyable and leverage ergonomics by adapting to the pilot’s preferred position. The overall research project points to a seedbed for future solutions, eventually extending its applications to medicine, space, and the metaverse. Full article

In this work, a conductive composite film composed of multi-walled carbon nanotubes (MWCNTs) and multi-layer Ti₃C₂Tx MXene nanosheets is used to construct a strain sensor on sandpaper Ecoflex substrate. The composite material forms a sophisticated conductive network with exceptional electrical conductivity, resulting in sensors with broad detection ranges and high sensitivities. The findings indicate that the strain sensing range of the Ecoflex/Ti₃C₂Tx/MWCNT strain sensor, when the mass ratio is set to 5:2, extends to 240%, with a gauge factor (GF) of 933 within the strain interval from 180% to 240%. The strain sensor has demonstrated its robustness by enduring more than 33,000 prolonged stretch-and-release cycles at 20% cyclic tensile strain. Moreover, a fast response time of 200 ms and detection limit of 0.05% are achieved. During application, the sensor effectively enables the detection of diverse physiological signals in the human body. More importantly, its application in a data glove that is coupled with machine learning and uses the Support Vector Machine (SVM) model trained on the collected gesture data results in an impressive recognition accuracy of 93.6%. Full article

Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove’s soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to $85.3\%$, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient’s attempted movements using pure thinking through a non-intrusive brain–computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings. Full article

Background: Cerebrovascular accident, commonly known as stroke, Parkinson’s disease, and multiple sclerosis represent significant neurological conditions affecting millions globally. Stroke remains the third leading cause of death worldwide and significantly impacts patients’ hand functionality, making hand rehabilitation crucial for improving quality of life. Methods: A comprehensive literature review was conducted analyzing over 300 papers, and categorizing them based on mechanical design, mobility, and actuation systems. To evaluate each device, a database with 45 distinct criteria was developed to systematically assess their characteristics. Results: The analysis revealed three main categories of devices: rigid exoskeletons, soft exoskeletons, and hybrid devices. Electric actuation represents the most common source of power. The dorsal placement of the mechanism is predominant, followed by glove-based, lateral, and palmar configurations. A correlation between mass and functionality was observed during the analysis; an increase in the number of actuated fingers or in functionality automatically increases the mass of the device. The research shows significant technological evolution with considerable variation in design complexity, with 29.4% of devices using five or more actuators while 24.8% employ one or two actuators. Conclusions: While substantial progress has been made in recent years, several challenges persist, including missing information or incomplete data from source papers and a limited number of clinical studies to evaluate device effectiveness. Significant opportunities remain to improve device functionality, usability, and therapeutic effectiveness, as well as to implement advanced power systems for portable devices. Full article

Wearable motion capture gloves enable the precise analysis of hand and finger movements for a variety of uses, including robotic surgery, rehabilitation, and most commonly, virtual augmentation. However, many motion capture gloves restrict natural hand movement with a closed-palm design, including fabric over the palm and fingers. In order to alleviate slippage, improve comfort, reduce sizing issues, and eliminate movement restrictions, this paper presents a new low-cost data glove with an innovative open-palm and finger-free design. The new design improves usability and overall functionality by addressing the limitations of traditional closed-palm designs. It is especially beneficial in capturing movements in fields such as physical therapy and robotic surgery. The new glove incorporates resistive flex sensors (RFSs) at each finger and an inertial measurement unit (IMU) at the wrist joint to measure wrist flexion, extension, ulnar and radial deviation, and rotation. Initially the sensors were tested individually for drift, synchronisation delays, and linearity. The results show a drift of 6.60°/h in the IMU and no drift in the RFSs. There was a 0.06 s delay in the data captured by the IMU compared to the RFSs. The glove’s performance was tested with a collaborate robot testing setup. In static conditions, it was found that the IMU had a worst case error across three trials of 7.01° and a mean absolute error (MAE) averaged over three trials of 4.85°, while RFSs had a worst case error of 3.77° and a MAE of 1.25° averaged over all five RFSs used. There was no clear correlation between measurement error and speed. Overall, the new glove design proved to accurately measure joint angles. Full article

As we move towards the commercialization and upscaling of perovskite solar cells, it is essential to fabricate them in ambient environment rather than in the conventional glove box environment. The efficiency of ambient-processed perovskite solar cells lags behind those fabricated in controlled environments, primarily owing to external environmental factors such as humidity and temperature. In the case of device fabrication in ambient environments, relying solely on a single parameter, such as temperature or humidity, is insufficient for accurately characterizing environmental conditions. Therefore, the dew point is introduced as a parameter which accounts for both temperature and humidity. In this study, a machine learning model was developed to predict the efficiency of ambient-processed perovskite solar cells based on meteorological data, particularly the dew point. A total of 238 perovskite solar cells were fabricated, and their photovoltaic parameters and dew points were collected from March to December 2023. The collected data were used to train various tree-based machine learning models, with the random forest model achieving the highest accuracy. The efficiencies of the perovskite solar cells fabricated in January and February 2024 were predicted with a MAPE of 4.44%. An additional Shapley Additive exPlanations analysis confirmed the significance of the dew point in the performance of perovskite solar cells. Full article

This study describes the design, simulation, and development process of a rehabilitation glove driven by soft pneumatic actuators. A new, innovative finger soft actuator design has been developed through detailed kinematic and workspace analysis of anatomical fingers and their actuators. The actuator design combines cylindrical and ribbed geometries with a reinforcing element—a thicker, less extensible structure—resulting in an asymmetric cylindrical bellow actuator driven by positive pressure. The performance of the newly designed actuator for the rehabilitation glove was validated through numerical simulation in open-source software. The simulation results indicate actuators’ compatibility with human finger trajectories. Additionally, a rehabilitation glove was 3D-printed from soft materials, and the actuator’s flexibility and airtightness were analyzed across different wall thicknesses. The 0.8 mm wall thickness and thermoplastic polyurethane (TPU) material were chosen for the final design. Experiments confirmed a strong linear relationship between bending angle and pressure variations, as well as joint elongation and pressure changes. Next, pseudo-rigid kinematic models were developed for the index and little finger soft actuators, based solely on pressure and link lengths. The workspace of the soft actuator, derived through forward kinematics, was visually compared to that of the anatomical finger and experimentally recorded data. Finally, an ergonomic assessment of the complete rehabilitation glove in interaction with the human hand was conducted. Full article

Sentiment classification plays a crucial role in evaluating user feedback. Today, online media users can freely provide their reviews with few restrictions. User reviews on social media are often disorganized and challenging to classify as positive or negative comments. This task becomes even more difficult when dealing with large amounts of data, making sentiment classification necessary. Automating sentiment classification involves text classification processes, commonly performed using deep learning methods. The classification process using deep learning models is closely tied to text representation. This step is critical as it affects the quality of the data being processed by the deep learning model. Traditional text representation methods often overlook the contextual meaning of sentences, leading to potential misclassification by the model. In this study, we propose a novel fusion text representation model, GloWord_biGRU, designed to enhance the contextual understanding of sentences for sentiment classification. Firstly, we combine the advantages of GloVe and Word2Vec to obtain richer and more meaningful word representations. GloVe provides word representations based on global frequency statistics within a large corpus, while Word2Vec generates word vectors that capture local contextual relationships. By integrating these two approaches, we enhance the quality of word representations used in our model. During the classification stage, we employ biGRU, considering the use of fewer parameters, which consequently reduces computational requirements. We evaluate the proposed model using the IMDB dataset. Several scenarios demonstrate that our proposed model achieves superior performance, with an F1 score of 90.21%. Full article

Flexible electronics is pursuing a new generation of electronic skin and human–computer interaction. However, effectively detecting large dynamic ranges and highly sensitive human movements remains a challenge. In this study, flexible strain sensors with a self-assembled PDMS/MXene/MWCNT structure are fabricated, in which MXene particles are wrapped and bridged by dense MWCNTs, forming complex sliding conductive networks. Therefore, the strain sensor possesses an impressive sensitivity (gauge factor = 646) and 40% response range. Moreover, a fast response time of 280 ms and detection limit of 0.05% are achieved. The high performance enables good prospects in human detection, like human movement and pulse signals for healthcare. It is also applied to wearable smart data gloves, in which the CNN algorithm is utilized to identify 15 gestures, and the final recognition rate is up to 95%. This comprehensive performance strain sensor is designed for a wide array of human body detection applications and wearable intelligent systems. Full article

Hand paralysis, caused by conditions such as spinal cord injuries, strokes, and arthritis, significantly hinders daily activities. Wearable exo-gloves and telerehabilitation offer effective hand training solutions to aid the recovery process. This study presents the development of lightweight wearable exo-gloves designed for finger telerehabilitation. The prototype uses NiTi shape memory alloy (SMA) actuators to control five fingers. Specialized end effectors target the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints, mimicking human finger tendon actions. A variable structure controller, managed through a web-based Human–Machine Interface (HMI), allows remote adjustments. Thermal behavior, dynamics, and overall performance were modeled in MATLAB Simulink, with experimental validation confirming the model’s efficacy. The phase transformation characteristics of NiTi shape memory wire were studied using the Souza–Auricchio model within COMSOL Multiphysics 6.2 software. Comparing the simulation to trial data showed an average error of 2.76°. The range of motion for the MCP, PIP, and DIP joints was 21°, 65°, and 60.3°, respectively. Additionally, a minimum torque of 0.2 Nm at each finger joint was observed, which is sufficient to overcome resistance and meet the torque requirements. Results demonstrate that integrating SMA actuators with telerehabilitation addresses the need for compact and efficient wearable devices, potentially improving patient outcomes through remote therapy. Full article

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance. Full article

The paper investigates the relationship between written reviews and numerical ratings of vegan and vegetarian restaurants, aiming to develop a predictive model that accurately determines numerical ratings based on review content. The dataset was obtained by scraping reviews from November 2022 until January 2023 from the TripAdvisor website. The study applies multidimensional scaling and clustering using the KNN algorithm to visually represent the textual data. Sentiment analysis and rating predictions are conducted using neural networks, support vector machines (SVM), random forest, Naïve Bayes, and BERT models. Text vectorization is accomplished through term frequency-inverse document frequency (TF-IDF) and global vectors (GloVe). The analysis identified three main topics related to vegan and vegetarian restaurant experiences: (1) restaurant ambiance, (2) personal feelings towards the experience, and (3) the food itself. The study processed a total of 33,439 reviews, identifying key aspects of the dining experience and testing various machine learning methods for sentiment and rating predictions. Among the models tested, BERT outperformed the others, and TF-IDF proved slightly more effective than GloVe for word representation. Full article