Glove Technique Research Articles

Electromyography signal based hand gesture classification system using Hilbert Huang transform and deep neural networks

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts. They could be useful in developing control protocols for bio-control systems, such human-computer interaction and upper limb prostheses. When focusing on hand gestures, data gloves and vision-based approaches are often used. The data glove technique requires tedious and unnatural user engagement, whereas the vision-based solution requires significantly more expensive sensors. This research offered a Deep Neural Network (DNN) automated hand gesticulation recognition system based on electromyography to circumvent these restrictions. This work primarily aims to augment the concert of the hand gesture recognition system via the use of an artificial classifier. To advance the recognition system's classification accuracy, this study explains how to build models of neural networks and how to use signal processing methods. By locating the Hilbert Huang Transform (HHT), one may get the essential properties of the signal. When training a DNN classifier, these characteristics are sent into it. The investigational results reveal that the suggested technique accomplishes a better categorization rate (98.5 % vs. the alternatives).

Sentiment classification on online product reviews using dwarf mongoose optimization with attention based deep learning model

Opinion Mining and Sentiment Analysis acts as a pivotal role in facilitating businesses to actively operate on enhancing the business strategies and accomplish detailed insights of the consumer’s feedback regarding the products. In recent times, deep learning (DL)technique has been used for many sentiment analysis tasks and has attained effective outcomes. Huge quantity of product reviews is being posted by the customer on different e-commerce and social networking platforms which can assist the developers to improve the quality of the products. The study focuses on the design of Sentiment Classification on Online Product Reviews using Dwarf Mongoose Optimization with Attention based Deep Learning (DMO-ABDL) model. The proposed DMO-ABDL technique analyzes the product reviews for the identification of sentiments. To accomplish this, the DMO-ABDL technique performs different stages of preprocessing to transform the actual data into suitable format. Furthermore, the Glove technique is employed for word embedding process. Moreover, attention based long short-term memory (ALSTM) approach was exploited for sentiment classification and its hyperparameters can be optimally chosen by the DMO technique. A comprehensive set of experiments were performed in order to guarantee the enhanced sentiment classification performance of the DMO-ABDL algorithm. A brief comparative study highlighted the supremacy of the DMO-ABDL technique over other existing approaches under different measures.

Poster 7 - Examining the impact of a double glove technique on infection rates of peritoneal dialysis catheter insertion sites

Poster 7 - Examining the impact of a double glove technique on infection rates of peritoneal dialysis catheter insertion sites

Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification

A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically. From part-of-speech (POS) parsing and tagging to machine translation and dialogue systems, NLP enables computers to carry out various natural language-related activities at all levels. In this research, we compared word embedding techniques FastText and GloVe, which are used for text representation. This study aims to evaluate and compare the effectiveness of word embedding in text classification using LSTM (Long Short-Term Memory). The research stages start with dataset collection, pre-processing, word embedding, split data, and the last is deep learning techniques. According to the results of the experiments, when compared to the glove technique it seems that FastText is superior, the accuracy obtained reaches 90%. The number of epochs did not significantly improve the accuracy of the LSTM model with GloVe and FastText. It can be concluded that the FastText word embedding technique is superior to the GloVe technique.

The Efficacy of Using a Sterile Glove Technique for Retrieval of the Gall Bladder Through the Epigastric Port in Preventing Post-Operative Infections During Laparoscopic Cholecystectomy

Objectives: To compare the efficacy of using sterile surgical gloves to retrieve the gall bladder through the epigastric port in laparoscopic cholecystectomy as compared to the direct retrieval technique in terms of surgical port site infections. Study Design: Randomized Controlled Trial Setting: Department of Surgery, Khyber Teaching Hospital, Peshawar. Duration: From 1st August, 2022, to 31st January, 2023. Methodology: After attaining approval from the Hospital ethical committee all patients meeting the inclusion criteria were included in the study. The patients were allocated in two groups through blocked randomization: Group A (use of sterile glove for extraction) and Group B (direct removal of gall bladder). Results were analyzed on Statistical Package for Social Sciences (SPSS) Version 23 and depicted in the form of description and statistical tables. Results: The mean age was 39.64 years ± 3.22 in group A and 38.25 years ±13.3 in group B and most patients were females in both groups, group A (n=40, 80%) and group B (n= 69, 86.3%).In group, A ,11 patients were diabetic (22%) and 5(10%) of the patients developedPSI at the epigastric port, 3(27.3%) in diabetics and 2(5.1%) in non-diabetics, p-value 0.031. In group B, 19 patients were diabetic (23.8%) and10 (12.5%) patients developed PSI, seen on 7th day follow up, 8 in diabetics (80%) and 2 non-diabetics(20%), p-value <0.01. The correlation between diabetes mellites and PSIs showed significant correlation p-value <0. 001.However,no significant correlation was seen between both groups in terms of PSIs, p-value 0.530. Conclusion: Even though it was observed that using retrieval bags relatively decreases the incidence of port site infections in laparoscopic cholecystectomy, the percentage is not notable enough to justify for its impact on operative time. Keywords: Laparoscopic Cholecystectomy, Epigastric Port,Glove Technique, Direct Removal, Port Site Infections.

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embedding process. Then, N-gram based feature extraction technique is derived to generate feature vectors. Lastly, WELM model is applied for the detection and classification of fake news, in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm. The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions. The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.

Rubber gloves in orthopaedic surgery (part II): Cooke and Goodyear; Halsted and Caroline's gloves of love; from cotton to rubber after Perthes' experiments; double glove technique with Urist.

This study determines the timeline for surgeons adopting rubber gloves and the double glove technique in the operating room for orthopaedic surgery. Using the vague historical terms of discovery, acceptance, commonplace, and consistency, we analyzed the influence of the different actors in each period. Cotton or silk was used for early gloves; they were permeable, sometimes coated with paraffin. Uses of rubber date to the 1600s when the Mesoamericans used rubber to make shoes. After the discovery of rubber in 1735 by the French scientist Charles de la Condamine in Peru, the rubber glove was imagined in 1834 and done for the first time by R. F. Cooke. The acceptance of rubber gloves arrived when the Goodyear-Rubber Society began to manufacture rubber gloves. Halsted, at Johns Hopkins Hospital, negotiated in 1889 with the Goodyear Rubber Company to produce thin rubber gloves to protect his nurse's hands from the dermatologic effects of the carbolic acid used to sterilize instruments. Commonplace to protect patients from bacteria of hand surgeons necessitated several decades. Dr. Joseph Bloodgood (Halsted's senior resident) remarked that gloving the surgeon with rubber gloves reduced the infection rate. Still, surgeons were reluctant to wear gloves that impaired the sense of touch. Laboratory experiments performed by G. Perthes on cotton and rubber gloves were necessary to generalize rubber gloves for practice in orthopaedic surgery. Consistency of the double-glove technique arrived during World War II when M. Urist proposed the double protection against bites from bone or bullet fragments while exploring war wounds. Only in 1965, when The Ansell Rubber Company utilized gamma irradiation as a low-cost method of sterilizing the gloves they produced, did disposable gloves become sterile. This article helps to understand the detailed discussions before surgical gloves' modern operating equipment.

Modeling of Optimal Fully Connected Deep Neural Network based Sentiment Analysis on Social Networking Data

Abstract In recent times, the user reviews and opinions postedon social-media like Twitter, Facebook, and Google+, and so on have received considerable attention in the domain of sentiment analysis (SA), offering valued feedback to public as well as private organizations. The examination of reviews roles an essential role for enhancing the product and service qualities. Although numerous SA methods are available in the literature, it is still needed to boost the outcome of the SA for understanding the customer feedbacks and thereby enhances the product quality. The presented article proposesan optimum fully connected deep neural network (OFCDNN) based SA, called OFCDNN-SA technique on social networking data. The OFCDNN-SA technique encompasses distinct procedureslike feature extraction, classification, preprocessing, and parameter optimization. Additionally, Glove technique is applied for the transformation of input data into feature vectors. Moreover, salp swarm optimization (SSO) based hyperparameter optimization technique is derived for maximally selecting the hyperparameters utilized in the DL method. Finally, the FCDNN methodis employed for classification purposes. To investigate the supreme achievement of the OFCDNN-SA method, a wide-ranging simulation assessment is performed and the experimental outputs highlighted the betterment over current methods by means of diverse measures.

Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification

Hate speech has become a hot research topic in the area of natural language processing (NLP) due to the tremendous increase in the usage of social media platforms like Instagram, Twitter, Facebook, etc. The facelessness and flexibility provided through the Internet have made it easier for people to interact aggressively. Furthermore, the massive quantity of increasing hate speech on social media with heterogeneous sources makes it a challenging task. With this motivation, this study presents an Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification (ESGONLP-HSC) model. The major intention of the presented ESGONLP-HSC model is to identify and classify the occurrence of hate speech on social media websites. To accomplish this, the presented ESGONLP-HSC model involves data pre-processing at several stages, such as tokenization, vectorization, etc. Additionally, the Glove technique is applied for the feature extraction process. In addition, an attention-based bidirectional long short-term memory (ABLSTM) model is utilized for the classification of social media text into three classes such as neutral, offensive, and hate language. Moreover, the ESGO algorithm is utilized as a hyperparameter optimizer to adjust the hyperparameters related to the ABLSTM model, which shows the novelty of the work. The experimental validation of the ESGONLP-HSC model is carried out, and the results are examined under diverse aspects. The experimentation outcomes reported the promising performance of the ESGONLP-HSC model over recent state of art approaches.

Hand-assisted Laparoscopic Surgery: experience with an original technique

Hand-assisted laparoscopic colorectal surgery (HALS) is an alternative to straight laparoscopic approach (SL) that requires the use of a specific device to maintain the pneumoperitoneum. Our group has described an original double glove technique to replace it. Our purpose was to compare perioperative outcomes of patients undergoing HALS using this original technique vs SL. Retrospective review of a prospective database including patients who underwent elective laparoscopic colorectal resections between 2004 to 2020 at the Hospital Italiano, Argentina. Logistic regression analysis, propensity score matching, and inverse probability weighting were used to estimate adjusted treatment effects for perioperative outcomes. HALS (n=458) and SL (n=1692) cases were demographically similar. HALS was associated with a shorter operative time (170.3 vs 206.9 minutes, p<0.001). Such difference was even more pronounced in obese (44.1 min), large patients (37.5 min), complex procedures (33.8 min) and surgeries carried out by non-trained surgeons (57,6 vs 31,6 minutes, p<0.001). Hand-assisted was associated with a lower conversion rate (5% vs 9.9%, p<0.001) with an adjusted odds ratio of 0.45 (95%CI 0.28-0.73). No difference in hospital stay, morbidity, and readmission rates was found. HALS with double-glove technique is comparable to SL in terms of postoperative outcomes keeping reduced operative time and conversion rates, especially in obese patients undergoing complex procedures.

Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media

World Wide Web enables its users to connect among themselves through social networks, forums, review sites, and blogs and these interactions produce huge volumes of data in various forms such as emotions, sentiments, views, etc. Sentiment Analysis (SA) is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive, negative, and neutral. However, Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing (NLP). Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications. So, there is a need exists to develop a proper technique for both identification and classification of sentiments in social media. To get rid of these problems, Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability. The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification (SOADL-SAC) for social media. The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media. In order to attain this, SOADL-SAC model carries out data preprocessing to clean the input data. In addition, Glove technique is applied to generate the feature vectors. Moreover, Self-Head Multi-Attention based Gated Recurrent Unit (SHMA-GRU) model is exploited to recognize and classify the sentiments. Finally, Seeker Optimization Algorithm (SOA) is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results. In order to validate the enhanced outcomes of the proposed SOADL-SAC model, various experiments were conducted on benchmark datasets. The experimental results inferred the better performance of SOADL-SAC model over recent state-of-the-art approaches.

Effectiveness And Adherence To Hospital Infection Control Preventive Practice In Cardiac Implantable Electronic Devices

Abstract Background Cardiac implantable electronic device (CIED) have been increasingly used in the past years, There is arise in CIED related complications in the past years with the increase in the number of devices implanted, CIED associated infection is challenging in its management, including system removal (generator and leads ), antimicrobial therapy, replantation at another site. Objectives The aim of the study is to evaluate the adherence to the steps of infection control protocol in cardiac device implantation related infection . Patients and Methods One hundred patients referred for cardiac device implantation was enrolled in the study and prospectively evaluated regarding applying of infection control measures and followed up for six months to study effect of adherence to these measures in prevention of postoperative device related infection. Results analysis of all factors of infection control protocol revealed significant correlation between postoperative device related infection and the following factors, Age ( p value = 0.010) , DM ( p value = 0.024) and number of Operators≥4.0 ( p = 0.001) as well as duration of sterilization ( p = 0.001), wearing double gloves (p = &amp;lt;0.001). Conclusion Our results proved certain factors as significant risk factors for device related infection, they are either patient related factors including and diabetes mellitus or device related factors reflecting higher incidence with CRT devices (p = 0.025), others related to applying anti septic measures namely double glove technique and duration of skin disinfect prior to the procedure. Abbreviation list; CIED (cardiac implantable electronic devices), DRI (device related infection), CKD (chronic kidney disease), DM (diabetes mellitus), HTN (hypertension), CRT (cardiac resynchronization therapy), DDD (dual chamber device), VVI (ventricular demand pacing), ICD (implantable cardioverter defibrillator).

Standardization of laparoscopic reversal of the Hartmann procedure: A single-center report.

Reestablishing continuity after the Hartmann procedure, the Hartmann reversal has been recognized as a complex procedure with a high morbidity rate. Laparoscopic reversal of the Hartmann procedure (LHR) is technically challenging, although good short-term results have been reported. We formulated this technique in 2013 and have been gradually devising and standardizing it. Ten patients who had undergone the Hartmann procedure from January 2013 to December 2019 and subsequently LHR were retrospectively examined. During the procedure, a circular incision was made at the original site of the colostomy to safely reach the abdominal cavity, and pneumoperitoneum was performed using the glove technique. Next, pelvic adhesions and the descending colon were dissected. If the rectal stump was difficult to identify, an intraoperative endoscope was used. Finally, either a Gambee or stapled anastomosis without tension was performed. The median surgical time was 265 minutes (range, 160-435 minutes), and the median blood loss was 100 mL (range, 10-700 mL). The median postoperative hospital stay was 11 days (range, 8-14 days). In one case, laparotomy was performed because of severe intra-abdominal adhesion. No major complication was observed during or after surgery. Therefore, LHR can be performed safely. Standardizing this procedure could render it minimally invasive, although a high level of evidence is needed.

Data Glove Technique based Gesture Recognition System

Communication is one of the important bonds in relationships, but for deaf, dumb and paralyzed persons this is very difficult and thus they communicate by gestures. It is a mode of communication between persons and for each gesture there is a meaning. Here the gestures are converted to a meaningful sentence or word and it is given as an output through external speakers with the help of using a microcontroller. Simply a gesture recognition or identification system using a microcontroller named ATMEGA with the help of a data glove is being used. An auxiliary communication tool by the help of a data glove is aimed for speech and hearing impaired people so that there is an effective communication between them and the others. The data sent from flex sensor and 3-axis accelerometer (ADXL330) is an analog voltage, which is further converted to digital signal using a microcontroller Atmega16 (Port A). The results identified by the controller are displayed on Android phones using the working application (.apk) which can be downloaded from play store. Thus enhanced effective communication between differently abled and normal person under any circumstances can be established. This concept provides an ease of operation for controlling appliances in a household for immobile persons. This device can be proved as a boon for partially disabled people to raise the quality of their life and become self-sufficient in the society.

Pragmatic recommendations for intubating critically ill patients with suspected COVID-19.

Pragmatic recommendations for intubating critically ill patients with suspected COVID-19.

Surgical Glove Technique for Laparoscopic Liver Resection

BackgroundLiver tumors located in segments 7 and 8 pose a technical challenge to standard laparoscopic liver resection. Intraoperative placement of a surgical glove behind the right liver after the right triangular ligament and coronary ligament are divided facilitates liver exposure during parenchymal transection and control of the bleeding at the deeper part of the parenchymal plane. The aim of this study was to describe our standardized technique in detail and to assess the feasibility of this technique in patients with different clinical backgrounds and clarify the limits of this technique. MethodsMedical records of 20 consecutive patients considered for laparoscopic liver resection using the surgical glove technique were reviewed. ResultsAll patients had malignant disease and the vast majority of patients had colorectal metastatic tumors. Overall, 65% of patients had tumors located in segment 8. Placing the surgical water glove could be achieved without complication in all 20 patients. One surgical glove was used in this series (usually size 6.5 to 7.5 is an adequate size). Time for preparing the surgical green water glove was estimated to be less than 1 min. The mean duration of operation ranged from 136 to 332 min (median, 240 min). Intermittent Pringle’s maneuver was applied in all patients with a median time of 33 min. No additional intercostal trocars were required. There was no intraoperative blood transfusion or conversion to open surgery. The median maximum size of the tumor was 23 mm. There was no operative mortality. Overall morbidity was 30%. Surgical margins were negative in 80% of patients. ConclusionsThe surgical glove technique is easy, reproducible, effective, and safe and can be applied to both laparoscopic and robotic liver resection.

Single-incision laparoscopic surgery using the glove technique with improved maneuverability achieved through fulcrum creation via a simple ring technique.

Single-incision laparoscopic surgery (SILS) is widely used in many surgical procedures in various specialties. Among the access methods used in SILS, the glove technique, which is typically similar to the multi-trocar approach, is simple, convenient, and inexpensive. However, given the absence of a fulcrum supporting the endoscopic surgical forceps, the technique is less advantageous than methods using commercially available access devices because of the poor maneuverability of the forceps. By creating a fulcrum as a support to use with existing surgical forceps in the glove technique, we designed and used a novel method known as the "ring technique." This technique aimed to improve the maneuverability of endoscopic surgical forceps. Placing a fulcrum resulted in the improvement of the forceps' maneuverability, which helped to ameliorate a weakness of the glove technique. The ring technique could be a useful option as a method of access in SILS.

Comparison of in Clinic-Based Fecal Microbiome Collection Techniques for Increase in Study Participation and Utilization of Microbiome Analysis

We compared the collection techniques of fecal specimens for DNA extraction and fecal microbiome analysis by utilizing the glove from a standard-of-care digital rectal exam (DRE) and the rectal swab from a pre-prostate biopsy bacterial rectal culture collected in clinical care settings. DNA yield from the swab technique compared to the glove technique yielded similar amounts of DNA (18.1 vs. 13.1 ng/μL, p = 0.06), slightly favoring the swab technique. However, utilizing DNA yield cutoffs of 15 ng/μL (37% vs. 29%, p = 0.18) and 30 ng/μL (15% and 9%, p = 0.16), we identified no differences in yield between the swab versus glove technique, respectively. Absorbance values for overall DNA quality were significantly different in favor of the glove technique (mean 1.6 vs. 2.0, p < 0.001). Using an absorbance value of 1.5 as an indication of DNA quality, only 26% (19/91) met the cutoff value using the swab group compared to 47.3% (53/112) if the glove technique was used (p < 0.001). Similar results occurred for the RNA quality with an absorbance value cutoff of 2.0 (2.2% vs. 30.4%, p < 0.001). To increase sampling feasibility and improve population sampling, gloves used from a DRE may be utilized as a consistent and efficient fecal DNA collection technique for fecal microbiome analysis. DNA yield and quality from the glove technique are comparable to—if not better than—rectal swab collection.

Stripped screwhead removal - a simple glove technique.

Stripped screwhead removal - a simple glove technique.