Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.
The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.
Technology is playing a vital role in the improvement of medical field resulting in high life expectancy. The use of wireless networks is one of the modern and efficient ways to monitor health problems remotely. In this context, various wireless monitoring standards are developed to facilities the patient monitoring. Some key standards include IEEE 802.15.4 Low-Rate Wireless Personal Area Network (LR−WPAN), IEEE 802.15.6 Wireless Body Area Network (WBAN) and ETSI smartBAN. These standards consist of multiple sensors that are used to monitor, process and transmit the vitals to the proper destination. Each standard offers some advantages and limitations over the other standard depending on the scenarios. In this paper, all the above-mentioned standards are compared and analyzed on different parameters such as network type, density, functionality, size and energy efficiency.
Gold was the one of the long-term investment commodities that were considered as the safe heaven for investors. The gold price was strongly influenced by global socioeconomic that causing fluctuations in price changes. The Fluctuations of gold price would be causing the denying of homogeneous variance assumption (heteroscedasticity). The purpous of This study was to apply Generalized Autoregressive Conditional Heteroscedasticity (GARCH) to model the fluctuations of gold prices. GARCH was the development of Autoregressive Conditional Heteroscedasticity (ARCH) model which was used to model the heterogeneous variance of the mean model. The data used in this study was the daily gold price data from May 5 th , 2015 to May 27 th , 2020. The results of this study showing the best model based on the smallest AIC value of -6.
The Open Research Knowledge Graph is an infrastructure for the production, curation, publication and use of FAIR scientific information. Its mission is to shape a future scholarly publishing and communication where the contents of scholarly articles are FAIR research data.
Spline regression is a nonparametric regression method that estimates data patterns that do not form certain patterns with the help of knots. The best model is obtained from the optimal knot. There are several methods that can be used to select optimal knots, including Generalized Cross-Validation (GCV) and Unbiassed Risk (UBR). The best model selection criteria used are based on the Mean Squared Error (MSE) and R-Square values. This study discusses the comparison of spline regression models using the UBR and GCV methods as a method for selecting optimal knots in data generation simulations. This research resulted in the best nonparametric spline regression model from the UBR method obtained by using three knots which produced an MSE value of 738.67 and R -Square of 85.65%. Whereas, the best nonparametric spline regression model of the GCV method was obtained using three knots which produced an MSE value of 121.43 and R-Square of 97.64%. It can be concluded that the more appropriate method used for the selection of optimal knot is the GCV method because it produces a smaller MSE value and a larger R-Square compared to the UBR method.
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