Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN

Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches.Comment: \c{opyright} 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0

Exploring dominant factors for ensuring the sustainability of utilizing artificial intelligence in healthcare decision making: An emerging country context

Healthcare decision-making is a complicated aspect that requires collaboration among stakeholders, whilst ensuring its sustainability is essential for addressing the requirements of healthcare facilities. Artificial intelligence (AI) in healthcare decision-making based on clinical knowledge and data are gaining traction as a way to enhance healthcare delivery by making smart diagnosis and treatment decisions. However, there are indeed a number of factors that require comprehensive inspection to ensure a sustainable AI-based decision making system in the healthcare domain. Therefore, this research explores 15 key sustainability indicators for incorporating AI applications in healthcare decision-making and performs a systematic assessment to prioritize the indicators according to the viewpoints of 35 relevant experts in context of the Bangladeshi health industry. Professional judgements on the level of significance for each indicators have been converted into quantitative data and plotted graphically in terms of their relative importance and divergence of opinions. Furthermore, the indicators have been categorized into three groups using two types of clustering techniques: K-means and agglomerative clustering approaches. According to the findings of the investigation, among the three clusters, one of them consisting of six indicators have considerably greater relative importance values with lesser opinion divergence, and hence are extremely crucial factors for ensuring sustainability. Thus, this research will guide healthcare practitioners with deeper perspective in undertaking appropriate strategies, focusing on the critical indicators for embracing AI-based techniques in developing nations’ healthcare decision-making arena