In recent years, WiFi has been shown to be a viable technology to enable a wide range of sensing applications, such as device-free localization, motion recognition, or human identification. Due to the growing interest in WiFi sensing, Task Group IEEE 802.11bf (TGbf) was formed to develop an amendment to the IEEE 802.11 standard that will enhance its ability to support WiFi sensing and applications. In this article, we identify and describe the main definitions and features of the IEEE 802.11bf amendment as defined in its D0.5 draft. Our focus is on the Wireless Local Area Network (WLAN) sensing procedure, which supports bistatic and multistatic WiFi sensing in license-exempt frequency bands below 7 GHz. We also present an overview of basic sensing principles, and provide a detailed discussion of features defined in the IEEE 802.11bf amendment that enhance client-based WiFi sensing.

Recognizing activities of daily living is an important research topic for health monitoring and elderly care. However, most existing activity recognition models only work with static and pre-defined sensor configurations. Enabling an existing activity recognition model to adapt to the emergence of new sensors in a dynamic environment is a significant challenge. In this paper, we propose a novel feature incremental learning method, namely the Feature Incremental Random Forest (FIRF), to improve the performance of an existing model when a small amount of data on newly appeared features. It consists of two important components - 1) a mutual information based diversity generation strategy (MIDGS) and 2) a feature incremental tree growing mechanism (FITGM). MIDGS enhances the internal diversity of random forests, while FITGM improves the accuracy of individual decision trees. To evaluate the performance of FIRF, we conduct extensive experiments on three well-known public datasets for activity recognition. Experimental results demonstrate that FIRF is significantly more accurate and efficient compared with other state-of-the-art methods. It has the potential to allow the dynamic exploitation of new sensors in changing environments.

Ubiquitin-Conjugating Enzyme E2 C (UBE2C) is a gene that encodes protein. Disorders associated with UBE2C include methotrexate-related lymphatic hyperplasia and complement component 7 deficiency. The encoded protein is necessary for the destruction of mitotic cell cyclins and cell cycle progression, and may be involved in cancer progression. In this paper, on the basis of public databases, we study the expression differential mechanism of gene expression of UBE2C in various tumors and the performance of prognosis, clinical features, immunity, methylation, etc.

Changes in motor pattern have been shown to be a useful advanced indicator of cognitive disorders, such as cerebral small vessel disease (SVD) and stroke. It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to analyze subtle changes in cognitive abilities, thereby providing personalized interventions before the actual onset of such conditions. However, this goal is very challenging because: 1) the size of motor pattern data from motion sensing devices is small, and 2) the available data tend to be highly imbalanced (mostly from normal subjects). In order to effectively deal with these challenges to infer cognitive abilities from motor patterns with high accuracy, we propose the MOtor-Cognitive Analytics (MOCA) framework. The core of this framework is a novel feature selection method IRFFS-O and a novel classification approach b-WELM which, together, address the above-mentioned challenges. Experimental results on two real-world datasets show that MOCA can effectively select the most significant features for specific disease detection, and significantly outperforms state-of-the-art methods in inferring people's cognitive capabilities. This work opens up opportunities for population-level pre-screening using motion sensing devices and can inform current discussions on reforming the healthcare infrastructure.