Abstract. Wearable Technologies continue to dramatically change healthcare system in various ways. The proliferation of these wearable technologies used in healthcare has made the emerging discipline confusing to understand. To better understand the rapid, fast-moving change, we propose a taxonomy to classify wearable technologies in terms of three major dimensions: application, form, and functionality. This taxonomy is evaluated by conducting both literate and market mapping. By doing so, we were able to classify a number of existing wearable technologies in light of the taxonomy dimensions. This DSR project concludes with some practical implications as design principles.Keywords: Wearable computing · Wearable technology · Smart devices · Taxonomy · Classification framework IntroductionAs technology advances, miniaturization along with mobile computing are giving rise to a new form of computing that we call "wearable computing technology". Interesting applications of wearable technology in healthcare is beginning to appear. Wearable Technology holds a great promise not just to boost clinical applications but also to advance the general health and well-being of people across the globe. Many forms of wearable technologies continue to evolve and have the potential to revolutionize healthcare in various ways. Several terminologies have been coined, such as unobtrusive sensing, edible capsules, smart tattoos for glucose monitoring or skinstretchable materials such as the CNT-based strain sensor for human motion monitoring[1]. The proliferation of a variety of wearable technologies used in healthcare has made the emerging discipline confusing to understand. This calls for a classification scheme to explain the state-of-the-art, specify the underlying principles, and establish the major criteria to categorize wearable devices in healthcare. Designing a taxonomy that not only helps to better understand the phenomenon, but also provides tools to categorize concepts in an emerging field is an essential first step towards broader understanding.Given the enormous contribution of wearable technologies in healthcare, some previous attempts have been made to summarize the recent developments in wearable sensors and devices for various medical applications [1][2][3][4] . Each attempt focuses on one aspect of wearable computing. Nevertheless, little attention has been given to designing a comprehensive taxonomy of wearable technologies in healthcare.
One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure.
Mitochondria are highly dynamic cellular organelles with the ability to change size, shape, and position over the course of a few seconds. Mitochondrial organelle movement refers to the problem of finding fission and fusion and generates energy for the cell. In this paper, we proposed a deep learning method [mitochondrial organelle movement classification (MOMC)] for mitochondrial movement classification using a convolutional neural network. We present a three-step feature description strategy, such as local descriptions, which is first extracted via the GoogLeNet, followed by the production of mid-level features by ResNet-50 and global descriptor features by Inception-V3 model and final classification of the position of mitochondrial organelle movement. Our method consists of a deep classification network, MOMC for gathering the organelle position, and a verification network for classification accuracy by removing false positives. Using machine learning methods, logistic regression (LR), support vector machine (SVM), and convolutional neural networks (CNNs), we found that the CNN better classified the shape of mitochondrial organelles (fission and fusion). Employing 24 types (position) of images, a convolutional neural network was trained to identify mitochondrial organelle movement with 96.32% accuracy. This enabled the discovery of position, further advancing the clinical utility of human mitochondrial organelles. INDEX TERMS Shape movement classification, mitochondrial organelle, deep learning, fission and fusion.
Background Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease such as COVID-19. In Saudi Arabia, many attempts have been made to raise public awareness about COVID-19 infection and precautionary health measures. However, most of the health information delivered through the national dashboard and the COVID-19 awareness campaigns are generic and do not necessarily make the impact needed to be seen on individuals’ behavior. Health messages need to be applicable and reverent to the individual in the audience. Objective In light of Fogg-Behavior model, this research aims to build and validate a behavior-change-based messaging campaign to promote precautionary health behavior in individuals during the COVID-19 pandemic. Intervention messages can then be targeted appropriately during the pandemic. Methods An initial library of 32 text-based and video-based messages were developed and validated based on Fogg behavior model for behavior change. Based on this model, three groups of messages were created to reflect the model’s three theoretical concepts of motivation, ability and triggers. Each group of messages is designed to target different segment of the audience. The content of the messages was developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The validity of this content was evaluated by domain experts through the content validity index. Results Fogg-behavior model was used to segment the audience into three different groups based on their perceived ability and motivation. The three groups of messages designed for those groups were found relevant to Fogg theoretical concepts. Thirteen professional health care workers (n = 13) evaluated the content of the message libraries in Arabic and English. Thirty-two messages were found to have acceptable content validity (I-CVI = 0.87). Conclusions This research introduced Fogg Behavior Model as a behavior change model to develop targeted messages for three groups of the audience based on their motivation and ability level toward maintaining precautionary behavior during the pandemic. This targeted awareness messaging campaign can be utilized by health authorities to raise individuals' awareness about the precautionary measures that should be taken, maintain these measures and hence help in reducing the number of positive cases in the city of Jeddah.
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