The considerable risk of falls and the substantial increase in the elderly population make the automatic fall detection system become very important. Existing fall detection systems using accelerometer as the detector are often designed based on an empirical acceleration threshold to differentiate falls from normal activities. In this paper, we design the detection method under the Neyman-Pearson detection framework. An optimal detection threshold can be obtained which meets the specified false alarm rate while maximizing the detection probability. We use TelosW mote with accelerometer as the detector, which is attached to the waist of the old people to capture the movement data. Extensive experiments are conducted to evaluate the effectiveness of our method and the accuracy of the detection system.

No abstract

Background Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear. Methods The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by ‘limma’ and ‘weighted gene co-expression network analysis (WGCNA)’ package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein–protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation. Results We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively. Conclusions This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.

No abstract

No abstract