Purpose Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users. Design/methodology/approach MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored. Findings According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems. Originality/value This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.
Background: The Global Navigation Satellite System (GNSS) has great potentials in next generation railway train control systems. Considering the fail-safe characteristics of train control, the threat from GNSS interference may result in an increasing likelihood of outages of train positioning or even safety risks to the railway system. Objective: The interference protection solutions are investigated and demonstrated for achieving the resilient train positioning using GNSS. Methods: This paper describes the main types of GNSS interference and investigates the impact on Location Determination Unit (LDU) in the GNSS-based train control system. Specific architectures and solutions for interference detection and protection in both the position domain and measurement domain are presented. Results: Interference injection simulations are performed with both the GNSS spoofing and jamming signals, which evaluate the effects of interferences and demonstrate the protection performance of the presented solutions under GNSS attack scenarios. Conclusion: The interference protection solutions within both the position domain and measurement domain are effective and significant to mitigate the effects from the GNSS interference, which enables resilient train positioning to achieve the safe train operation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.