Human activity recognition (HAR) is a prominent research direction in ubiquitous computing. Current state-of-the-art HAR models achieve great success by learning the correlations between the regions of the body parts by using the attached sensing devices for feature extraction. However, explicitly computing the correlations between whole body parts and whole sub-body parts, which is crucial for extracting discriminatory features for some activities, has not been investigated due to lack of sensing devices that capture the movements of the whole (sub-)body parts. This study proposes an effective yet lightweight VirtualHAR framework, which automatically models correlations between the whole body parts, whole sub-body parts, and regions based on the concept of virtual sensing devices. The VirtualHAR framework mainly encompasses three modules. The Backbone Feature Extraction module extracts the features from a physical sensing device, based on which the Multi-purpose Correlations Learning module constructs virtual sensing devices for body parts and sub-body parts and then exploits the explicit correlations between body parts, sub-body parts as well as in regions by using their attached physical sensing devices. Finally, the Global Aggregation module learns the global aggregation representation for each physical sensing device by collecting the learned correlated representation from each virtual sensing device and physical sensing device. Comprehensive experiments on benchmark HAR datasets and a resource-constrained device confirm that VirtualHAR outperforms SOTA models in recognition performance and computational complexity. Through thorough quantitative and qualitative analysis, we validate the proposed VirtualHAR framework’s effectiveness and efficiency.

Leukemia, a prevalent childhood cancer affecting the blood and bone marrow, necessitates a proactive approach to risk mitigation through lifestyle adaptations. Despite previous investigations into similar aspects across different cancer types, a comprehensive inquiry focusing on all leukemia subtypes within Pakistan remains a significant research gap. Acknowledging the influence of regional variations on individuals' lifestyles, this study aims to identify lifestyle and demographic factors associated with leukemia development and predict specific leukemia subtypes using clinical data. Our data collection included 364 leukemia cases and 896 control subjects, gathered from different cancer or tertiary care hospitals in Islamabad and Peshawar, Pakistan. The data was meticulously categorized into laboratory results, demographic characteristics, and lifestyle parameters. For demographic and lifestyle analysis, we employed advanced techniques of Machine Learning, and statistical and graph-based methodologies to assess leukemia development risk. This study highlights factors associated with a higher risk of leukemia, including passive smoking, rural residence, and poor nutrition. These insights emphasize the promotion of healthier lifestyle choices to potentially reduce leukemia incidences. Additionally, we transformed the clinical dataset into graph data, which was utilized for leukemia classification and subtype prediction. We conducted classification on both graph and structured (tabular) data, with the structured data, achieving a 96% accuracy rate, notably on oversampled data. To enhance the interoperability of our classification outcomes, we employed the SHapley Additive exPlanations (SHAP) algorithm to explain the classification, offering comprehensive insights into the rationale behind leukemia classification.

Human Activity Recognition (HAR) through wear-able sensors greatly improves the quality of human life through its multiple applications in health monitoring, assisted living, and fitness tracking. For HAR, multi-sensor channel information is vital to performance. Current work states that applying an attention neural network to prioritize discriminatory sensor channels helps the model classify activity more precisely. However, getting discriminatory information from multisensory channels is not always trivial. For example, when collecting data from elderly hospitalized patients. In this context, existing HAR methods struggle to classify activities, particularly activities with similar natures. Moreover, HAR deep models predominantly suffer from overfitting due to small datasets, which leads to poor performance. Data augmentation is a viable solution to this problem. However, currently available data augmentation methods to HAR have various drawbacks, including the pos-sibility of being domain-dependent, and resulting in distorted models for test sequences. To address the aforementioned HAR problems, we propose a novel framework that primarily focuses on two aspects. First, enhancing the latent information across each sensor channel and learning to exploit the relation among multiple latent features and the ongoing activity. Consequently, this enriches the discriminatory feature representations of each activity. Second, a new augmentation strategy is introduced to address the shortcomings of existing multi-sensor channel data augmentation to generalize our HAR model. Our model outperforms existing state-of-the-art approaches on the four most commonly used HAR datasets from diverse domains. We exten-sively demonstrate the effectiveness of the proposed framework through detailed quantitative analysis of experimental results and ablation studies.