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Sign languages has extensive applications among differently-abled to communicate with their surroundings. With the development of different sensing technologies, several new human-computer interaction techniques (HCI) have been established to recognize hand gestures. Computer vision-based methods have shown significant utility for such applications. However, these methods are strongly dependent on the lighting conditions. The surface electromyography (sEMG) technique is invariant to lighting conditions and can easily reflect human motion intention. In this work, sEMG based sign language recognition model was developed using an efficient machine learning pipeline. Two sEMG datasets were recorded for predefined hand gestures using wireless sensors. These signals were mainly acquired against 24 manual alphabets (ASL-24) and ten digits(ASL-10) of American Sign Language (ASL). The collected data sets were preprocessed, and around 450 well-established feature was extracted from each sEMG channel. We applied an ensemble feature selection approach combining four diverse filter-based feature selection methods (ANOVA, Chi-square, Mutual Info, ReliefF). A newly proposed feature combiner that exploits feature–feature and feature–class correlation thresholds is used to combine feature subsets formed across the ensemble. The resulting features comprise reduced & most representative feature subsets and are further used in the pipeline for classifying ASL gestures. Using the CatBoost algorithm, the pipeline presented excellent average classification accuracy(99.91% on ASL-24) and other performance parameters for recognizing ASL gestures. The pipeline was also applied and validated on a benchmark dataset (Ninapro database 5, exercise A) and achieved similar outcomes. The result highlights the feasibility of using sEMG based approach as better options to computer-vision-based techniques to build an accurate and robust Sign Language Recognition system (SLRS). Moreover, efforts were made to find the optimal number of sensors and features for recognition task on (ASL-10 dataset) without impacting the overall reliability and accuracy of the system. The experiments results can be used to enhance the performance of various wearable sEMG sensor based HCI applications. [ABSTRACT FROM AUTHOR]

To study the possible association between invasive fungal sinusitis (aspergillosis) and coronavirus disease. An observational study was conducted at a tertiary care centre over 6 months, involving all patients with aspergillosis of the paranasal sinuses suffering from or having a history of COVID-19 infection. 92 patients presented with aspergillosis, all had an association with COVID-19 disease. Maxillary sinus (100%) was the most common sinus affected. Intraorbital extension was seen in 34 cases, while intracranial extension was seen in 5 cases. Diabetes mellitus was present in 75 of 92 cases. All had a history of steroid use during their coronavirus treatment. New manifestations of COVID-19 are appearing over time. The association between coronavirus and aspergillosis of the paranasal sinuses must be given serious consideration. Uncontrolled diabetes and overzealous use of steroids are two main factors aggravating the illness, and both of these must be properly checked. [ABSTRACT FROM AUTHOR]

The aim of this study was to find out the association of sinonasal candidiasis and Covid-19 infection. A prospective observational study was conducted at a tertiary care centre from April to September 2021, involving all patients with invasive candidiasis of the paranasal sinuses having a history of Covid-19 infection. A total of 18 patients of covid associated sinonasal candidiasis among the 475 cases of fungal rhinosinusitis were studied. All patients had involvement of nose and sinuses and 2 patients had orbital involvement with no loss of vision, while 3 had intracranial extensions and 1 had pulmonary involvement. Mandible was involved in 1 patient alone, while the maxilla and palate were involved in 5 patients. 15 patients were hypertensive, 12 diabetics and 1 had aplastic anaemia. Cultures showed that 8 patients had C. parapsilosis, 5 had C. albicans, 3 had C. tropicalis and 2 had mixed fungal infections. All patients underwent surgical debridement and antifungal administration. They were followed up for a minimum of 3 months. There was only one mortality (with aplastic anaemia), rest 17 were disease free at the time of writing this article. This is perhaps the first case series of post covid sinonasal candidiasis in the world. Invasive sinonasal candidiasis is a newer sequela of COVID-19 infection. Uncontrolled diabetes and over-zealous use of steroids at the time of Covid-19 are few of the known risk factors. Early surgical intervention and anti-fungal treatment should be sought for management. [ABSTRACT FROM AUTHOR]

Energy efficiency is an existing research challenge in wireless sensor networks (WSNs); to make WSNs energy efficient, many researchers moved to low-duty cycle WSNs with different data forwarding schemes. Low-duty cycle is considered to be a promising approach to design routing protocols for resource-constrained WSNs. Many applications have real-time constraints, which requires an event must be reported to the sink before deadline. Furthermore, wireless links between low-power radios are highly unreliable, and end-to-end latency requirements are challenging due to multiple transmissions for a single packet delivery. By considering probabilistic delay (i.e., delay bounded data delivery with reliability constraints) and energy efficiency issues, little research has been done using different data forwarding schemes, but still, the problem exists. In this work, two strategies are proposed for data forwarding: an energy-optimal path and a delay-optimal path. The early-arrived packet follows an optimal energy route and a diverse path to achieve a delay guarantee with minimum transmission cost. The proposed scheme improves the performance of low-duty cycle WSNs in terms of delay, reliability, stability, and transmission cost. It is observed that under different low-duty cycles, the proposed scheme achieves up to 25% energy conservation compared with both MinEEC and MinEED. At last, undirected spanning trees are considered to balance communication costs. The simulation results of the proposed scheme are compared with the existing methods. [ABSTRACT FROM AUTHOR]