Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition.

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

Ex-vivo antihypertensive and calcium channel blocking activity of Androsace foliosa n-hexane leaves fraction on isolated rabbit aorta.

Hypertension is persistent elevation in blood pressure for 3-4 weeks. Estimated global prevalence of hypertension suggested that by the Year 2025 (29%) of adult worldwide are suffering from hypertension (1.56 billion). Hypertension complications are hemorrhage, atherosclerosis, renal artery stenosis, angina pectoris end organ damage, cardiomyopathy, myocardial infarction and retinopathy. Along with other drug class Calcium channel blocker are also used for the treatment of hypertension. In this study the possible action of the n-hexane leaves fraction of the Androsace foliosa on isolated rabbit aorta was examined. Antihypertensive activity was examined in the existence of standard agonist like phenylephrine and antagonist like Verapamil. Phenylephrine (PE 1µM) high K+ was used to steady the tissue materials. Additionally to observe the calcium channel blocking effect the tissues were treated with n-hexane segment of A. foliosa leaves. Aortic tissues were treated 4-5intervals with Ca+2- free preparation earlier to control calcium reaction curve (CRCs). Verapamil is utilized as standard calcium channel inhibitory mediator and is used as an antagonist. The Af. n-hexane leaves fraction completely inhibited the precontractions induced by Phenylephrine (1µM) and K+ (80 mM) precontractions, with EC50 standards of 1.0mM (0.3-1.0mg/mL) and 4.90mM (1-3mg/mL), respectively. Androsace foliosa n-hexane leaves fraction was tested for calcium channel inhibitory effect on isolated rabbit aorta. A. foliosa n- hexane leaves segment at the dosage of 1mg/mL block the calcium channel approximately (35±5%). Consequence indicates that A. foliosa n-hexane leaves segment block calcium channel in the similar manner as compared to the standard calcium channel blocker drug (verapamil).

Real-world effectiveness of the inactivated COVID-19 vaccines against variant of concerns: meta-analysis.

BACKGROUND: COVID-19 has killed over 6 million people worldwide, making it the worst global health disaster since the 1918 influenza pandemic. Experts have worked to establish the source, track and analyse the disease, and produce treatment and preventative guidelines. Inactivated vaccines have little evidence of efficacy compared to mRNA and adenoviral vector vaccines; however, three doses of both mRNA and inactivated vaccines appear to provide significant and lasting protection against severe disease and mortality. This study examines inactivated vaccine effectiveness data by disease status, age, gender, primary immunisation, booster doses, and SARS-CoV2 virus types. METHODS: We conducted a quantitative epidemiological meta-analysis study to assess the vaccine effectiveness of inactivated COVID-19 vaccines. Data extraction was performed on the selected studies, and data analysis was conducted using a random-effects model to determine consolidated assessments of vaccine effectiveness. Subgroup analyses were conducted for gender, age, disease level, and vaccine status, and sensitivity analyses were conducted to assess the robustness of the results. RESULTS: The overall effect size of inactivated COVID-19 vaccinations was statistically significant (p-value<0.05), suggesting that complete vaccination should be the primary method of vaccination. Partial vaccination was associated with lower levels of vaccine effectiveness (70.18 95% CI 57.33-83.02) than complete vaccination (79.52 95% CI 67.88-91.71)) and booster vaccination (84.22 95% CI 74.34-94.10), suggesting that it is essential to finish the recommended vaccine series and receive booster doses. Fig.-3: Partially vaccinated individuals showed a vaccine effect size of 70.18 (95% CI 57.33-83.02), indicating that the vaccine was moderately effective in preventing COVID-19 among this group. Fully vaccinated individuals showed a vaccine effect size of 79.52 (95% CI 67.88-91.71), indicating a higher level of vaccine effectiveness. Finally, booster-vaccinated individuals showed a vaccine effect size of 84.22 (95% CI 74.34-94.10), indicating the highest level of vaccine effectiveness. CONCLUSION: Inactivated COVID-19 vaccines are highly effective in preventing COVID-19, and complete vaccination and booster vaccination are associated with higher levels of vaccine effectiveness compared to partial vaccination. These findings highlight the importance of completing the recommended vaccine series and receiving booster doses to provide greater protection against COVID-19.

Toward explainable AI-empowered cognitive health assessment.

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.

Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification.

The systems of sensing technology along with machine learning techniques provide a robust solution in a smart home due to which health monitoring, elderly care, and independent living take advantage. This study addresses the overlapping problem in activities performed by the smart home resident and improves the recognition performance of overlapping activities. The overlapping problem occurs due to less interclass variations (i.e., similar sensors used in more than one activity and the same location of performed activities). The proposed approach overlapping activity recognition using cluster-based classification (OAR-CbC) that makes a generic model for this problem is to use a soft partitioning technique to separate the homogeneous activities from nonhomogeneous activities on a coarse-grained level. Then, the activities within each cluster are balanced and the classifier is trained to correctly recognize the activities within each cluster independently on a fine-grained level. We examine four partitioning and classification techniques with the same hierarchy for a fair comparison. The OAR-CbC evaluates on smart home datasets Aruba and Milan using threefold and leave-one-day-out cross-validation. We used evaluation metrics: precision, recall, F score, accuracy, and confusion matrices to ensure the model's reliability. The OAR-CbC shows promising results on both datasets, notably boosting the recognition rate of all overlapping activities more than the state-of-the-art studies.

Glenohumeral tuberculous arthritis complicated with beta haemolytic streptococcus: An extraordinary rare association: A case report.

INTRODUCTION: Septic arthritis of the glenohumeral joint is a rare entity and its diagnosis is difficult with a superadded infection in the presence of underlying tuberculosis. We report the first case of group B beta haemolytic streptococcal glenohumeral arthritis with underlying tuberculosis. CASE PRESENTATION: A 40 year old lady previously diagnosed to have poliomyelitis, rheumatoid arthritis, hepatitis C, and diabetes mellitus for the last 10 years, presented to the emergency room with diabetic ketoacidosis. Two weeks prior to presentation she developed fever along with pain and swelling in left shoulder with uncontrolled blood sugars. Local examination of the shoulder revealed global swelling with significant restricted range of motion. MRI showed a large multiloculated collection around the left shoulder joint extending into the axilla, and proximal arm. Urgent arthrotomy performed and about 120ml thick pus was drained. The patient was started on clindamicin and antituberculous chemotherapy and her symptoms dramatically improved. DISCUSSION: Bone and joint involvement accounts for approximately 2% of all reported cases of tuberculosis (TB), and it accounts for approximately 10% of the extra pulmonary cases of TB. Tuberculosis of the shoulder joint constitutes 1-10.5% of skeletal tuberculosis. Classical symptoms of fever, night sweats, and weight loss may be absent, and a concurrent pulmonary focus may not be evident in most cases. CONCLUSION: Despite acute presentation of septic arthritis, in areas endemic for tuberculosis and particularly in an immunocompromised patient, workup for tuberculosis should be part of the routine evaluation.