COVID-19 vaccines have met varying levels of acceptance and hesitancy in different parts of the world, which has implications for eliminating the COVID-19 pandemic. The aim of this systematic review is to examine how and why the rates of COVID-19 vaccine acceptance and hesitancy differ across countries and continents. PubMed, Web of Science, IEEE Xplore and Science Direct were searched between 1 January 2020 and 31 July 2021 using keywords such as “COVID-19 vaccine acceptance”. 81 peer-reviewed publications were found to be eligible for review. The analysis shows that there are global variations in vaccine acceptance among different populations. The vaccine-acceptance rates were the highest amongst adults in Ecuador (97%), Malaysia (94.3%) and Indonesia (93.3%) and the lowest amongst adults in Lebanon (21.0%). The general healthcare workers (HCWs) in China (86.20%) and nurses in Italy (91.50%) had the highest acceptance rates, whereas HCWs in the Democratic Republic of Congo had the lowest acceptance (27.70%). A nonparametric one-way ANOVA showed that the differences in vaccine-acceptance rates were statistically significant (H (49) = 75.302, p = 0.009*) between the analyzed countries. However, the reasons behind vaccine hesitancy and acceptance were similar across the board. Low vaccine acceptance was associated with low levels of education and awareness, and inefficient government efforts and initiatives. Furthermore, poor influenza-vaccination history, as well as conspiracy theories relating to infertility and misinformation about the COVID-19 vaccine on social media also resulted in vaccine hesitancy. Strategies to address these concerns may increase global COVID-19 vaccine acceptance and accelerate our efforts to eliminate this pandemic.

Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have demonstrated success in the prediction of skin diseases and can also lead to the classification of different types of psoriasis. Hence, we propose a deep learning-based application for effective classification of five types of psoriasis namely, plaque, guttate, inverse, pustular, and erythrodermic as well as the prediction of normal skin. We used 172 images of normal skin from the BFL NTU dataset and 301 images of psoriasis from the Dermnet dataset. The input sample images underwent image preprocessing including data augmentation, enhancement, and segmentation which was followed by color, texture, and shape feature extraction. Two deep learning algorithms of convolutional neural network (CNN) and long short-term memory (LSTM) were applied with the classification models being trained with 80% of the images. The reported accuracies of CNN and LSTM are 84.2% and 72.3%, respectively. A paired sample T-test exhibited significant differences between the accuracies generated by the two deep learning algorithms with a . The accuracies reported from this study demonstrate potential of this deep learning application to be applied to other areas of dermatology for better prediction.

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and -nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample -test showed significant differences between the two accuracies with a . SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.

Prostheses control using electromyography signals have shown promising aspects in various fields including rehabilitation sciences and assistive technology controlled devices. Pattern recognition and machine learning methods have been observed to play a significant role in evaluating features and classifying different limb motions for enhanced prosthetic executions. This paper proposes feature extraction and evaluation method using intramuscular electromyography (iEMG) signals at different arm positions and hand postures based on the RES Index value statistical criterion method. Sixteen-time domain features were selected for the study at two main circumstances; fixed arm position (FAP) and fixed hand posture (FHP). Eight healthy male participants (30.62 ± 3.87 years) were asked to execute five motion classes including hand grip, hand open, rest, hand extension, and hand flexion at four different arm positions that comprise of 0°, 45°, 90°, and 135°. The classification process is accomplished via the application of the k-nearest neighbor (KNN) classifier. Then RES index was calculated to investigate the optimal features based on the proposed statistical criterion method. From the RES Index, we concluded that Variance (VAR) is the best feature while WAMP, Zero Crossing (ZC), and Slope Sign Change (SSC) are the worst ones in FAP conditions. On the contrary, we concluded that Average Amplitude Change (AAC) is the best feature while WAMP and Simple Square Integral (SSI) resulted in least RES Index values for FHP conditions. The proposed study has possible iEMG based applications such as assistive control devices, robotics. Also, working with the frequency domain features encapsulates the future scope of the study.

An individual who is in good physical health tends to exhibit an internal core temperature of 37°C and a heart rate of 60–100 beats per minute. Increase in the temperature of the surrounding environment can serve as the basis for the onset of the condition of Hypothermia. Hypothermia acts as one of the most significant barriers being faced by winter athletes and starts initially with an increase in the heart and breathing rate. However, if the condition persists it can lead to reduction in the heart and breathing rate and ultimately results in cardiac failure. Although, jackets are commercially available, they tend to operate manually and furthermore, do not serve the primary purpose of counteracting the condition of hypothermia, particularly experienced by athletes taking part in winter sports. The objective of this study is to design a heating jacket that enables effective counteraction of the condition of Hypothermia. It enables precise measurement of the of core body temperature with the aid of a pyroelectric sensor. Along with this, a pulse rate sensor for detecting the accurate heart rate has been incorporated on the index finger. Five heating pads would get activated to attain optimal temperature, in case the core body temperature of <37°C is detected. If the condition of hypothermia advances to the moderate stage, two additional heating pads will get activated and provide extra warmth to attain normal heart rate along with core body temperature. Overall, this wearable technology serves as a definitive solution to counteract the condition of hypothermia only when the internal parameters exhibit that you actually have it. The results of the study exhibited that this prototype can be utilized for detecting and treating the condition of Hypothermia.

Muscular weakness tends to increase very rapidly due to various medical illnesses such as stroke, paralysis, fibromyalgia, etc. In order to keep tracks of the rehabilitative progress of patients who are suffering from such diseases, it is necessary to acquire data pertaining to finger movements including flexion and extension. Along with range of motions of proximal interphalangeal (PIP), distal interphalangeal (DIP) and meta-capo phalangeal joints, pinching strength is also vital in assessing the progress of rehabilitative therapies. Hence, our objective is to develop an assistive technology in the form of a smart glove comprising of flex and force sensors for measuring flexion and extension movements as well as the pinching strength. To the best of author’s knowledge, commercially available rehabilitation gloves are expensive and have some limitations such as being non-portable, having an antenna mount on the gloves facing upward and so on. The smart glove was able to measure the flexion and extension of finger movements and pinch strength with low-power requirements and low cost associated with production. The flexion and extension of finger movements along with pinching strength of stroke survivors was measured with the aid of the glove and showed promising outcomes. Through the results achieved by our developed glove, we were able to analyze the rehabilitative progress of stroke survivors. Moreover, the data is monitored continuously through liquid crystal display for rehabilitation purposes. Notably, this low cost glove was designed with the aid of flex sensors and force sensors that enabled the effective measurement of flexion, extension and pinching strength of stroke survivors.