Social Support, Trust and Purchase Intention in Social Commerce Era

Due to increasing numbers of purchases in theonlineindustry has created trust as a critical path in an online environment. In fact, it is more critical when trust has identified as crucial in online commerce. Consumers are reluctant to have a purchase intention when they distrust towards the website.Consumers nowadays, who represent the future buyers, seem to have reasons how they can trust in online commerce and ultimately lead them to have purchase intention. Drawn from social support theory, trust and purchase intention, this research empiricallyis to test which characters of social support (emotional and informational support) have significant influence purchase intention and to test whether the trust has a significant influence on purchase intention.Furthermore, to test the mediatingeffects oftrust in social commerce.The research conducted in thequantitativeapproach and used non-probability (convenience sampling) by using questionnaire surveys. A correlation and multiple regression analyses were applied. A total of 200 respondents participated. Our results shed some lights on social commerce literature. The result confirms that there is a relationship between social supports such as emotional and informational support on purchase intention. Finding also revealed that trust as fully mediates the relationship between the variables. This research canentirelycontribute to the literature by providing and introducing to both marketers and consumers by identifying the factors influencing purchase intention in social commerce

Cognitive healthcare system and its application in pill-rolling assessment

Directional antennas have been extensively used in wireless sensor networks (WSNs) for various applications. This work presents the application of a four‐beam patch antenna as a sensor node to assess the pill‐rolling effect in Parkinson disease. The four‐beam patch is small in size, highly directive, and can suppress the multipath fading encountered in indoor settings that adversely affects the measurements. The pill‐rolling effect refers to tremors in the hands, particularly in the forefinger and the thumb, which the patient involuntary rubs together. The core idea is to develop a low‐cost framework that effectively evaluates the particular movement disorder to assist doctors or clinicians in carrying out an objective assessment using the S‐band sensing technique leveraging small wireless devices operating at 2.4 GHz. The proposed framework uses the perturbations in amplitude and phase information to efficiently identify tremors and nontremors experienced in the fingers. The unique imprint induced by each body motion is used to determine the particular body motion disorder. The performance of the framework is evaluated using the support vector machine algorithm. The results indicate that the framework provides high classification accuracy (higher than 90%)

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed

Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm

Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor “XeThru X4M200” is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model’s performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%

Impact of COVID-19 national lockdown on asthma exacerbations: interrupted time-series analysis of English primary care data

Background: The impact of Covid-19 and ensuing national lockdown on asthma exacerbations is unclear. Methods: We conducted an interrupted time-series (lockdown on 23rd March as point of interruption) analysis in asthma cohort identified using a validated algorithm from a national-level primary care database, the Optimum Patient Care Database (OPCRD). We derived asthma exacerbation rates for every week and compared exacerbation rates in the period: January-August 2020 with a pre-Covid-19 period; January-August 2016-2019). Exacerbations were defined as asthma-related hospital attendance/admission (including accident and emergency visit), or an acute course of oral corticosteroids with evidence of respiratory review, as recorded in primary care. We used a generalised least squares modelling approach and stratified the analyses by age, sex, English region, and healthcare setting. Results: From a database of 9,949,487 patients, there were 100,165 asthma patients who experienced at least one exacerbation during 2016-2020. Of 278,996 exacerbation episodes, 49,938 (17.1%) required hospital visit. Comparing pre-lockdown to post-lockdown period, we observed a statistically significant reduction in the level (-0.196 episodes per person-year; p-value<0.001; almost 20 episodes for every 100 asthma patients per year) of exacerbation rates across all patients. The reductions in level in stratified analyses were: 0.005-0.244 (healthcare setting, only those without hospital attendance/admission were significant), 0.210-0.277 (sex), 0.159-0.367 (age), 0.068-0.371 (region). Conclusions: There has been a significant reduction in attendance to primary care for asthma exacerbations during the pandemic. This reduction was observed in all age groups, both sexes, and across most regions in England

Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

The health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform assessment of this health status. The core idea is to develop a generalized system where the data obtained for ADL can be portable across different environments and groups of subjects, and critical events such as falls in mature individuals can be detected. In this context, we have conducted comprehensive experimental campaigns at nine different locations including four laboratory environments and five elderly care homes. A total of 99 subjects participated in the experiments where 1453 micro-Doppler signatures were recorded for six activities. Different machine learning, deep learning algorithms and transfer learning technique were used to classify the ADL. The support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) provided adequate classification accuracies for particular scenarios; however, the autoencoder neural network outperformed the mentioned classifiers by providing classification accuracy of ~ 88%. The proposed system for fall detection in elderly people can be deployed in care centers and is application for any indoor settings with various age group of people. For future work, we would focus on monitoring multiple older adults, concurrently in indoor settings using continuous radar sensor data stream which is limitation of the present system