Intelligent Health Management System Research Articles

Design of Physical Training Assistant System Based on Intelligent Health Management System

Design of Physical Training Assistant System Based on Intelligent Health Management System

Application of Smart Wearable Fitness Equipment and Smart Health Management Based on the Improved Algorithm.

IoT technology has scientific, advanced, and practical characteristics in wearable fitness equipment and artificial intelligence health management. On this basis, this paper discusses the intelligent health management system based on IoT technology. By building an IoT health management system, the design principles and algorithm design of wearable fitness equipment are introduced around the device, and various noises that affect the accuracy of the equipment and how to better filter out various noises are also analyzed. The authors selected cloud service platforms and also investigated and analyzed the application prospects of wearable fitness equipment in the community. From the perspective of real-time monitoring, scientific management, and intelligent decision-making, we ensure high-quality, standardized, scientific, efficient, and accurate wearable fitness equipment applications and intelligent health management. In addition to simply viewing IoT technology as an information dissemination platform, IoT technology can also expand many other functions. According to the survey results of this article, 90% of people will use the APP with the function of sports and fitness in social media. One of the most concerned issues in the current society is the application of social media in national fitness activities.

Synthesis of decision making in a distributed intelligent personnel health management system on offshore oil platform

This paper proposes a methodological approach for the decision synthesis in a geographically distributed intelligent health management system for oil workers working in offshore industry. The decision-making methodology is based on the concept of a person-centered approach to managing the health and safety of personnel, which implies the inclusion of employees as the main component in the control loop. This paper develops a functional model of the health management system for workers employed on offshore oil platforms and implements it through three phased operations that is monitoring and assessing the health indicators and environmental parameters of each employee, and making decisions. These interacting operations combine the levels of a distributed intelligent health management system. The paper offers the general principles of functioning of a distributed intelligent system for managing the health of workers in the context of structural components and computing platforms. It presents appropriate approaches to the implementation of decision support processes and describes one of the possible methods for evaluating the generated data and making decisions using fuzzy pattern recognition. The models of a fuzzy ideal image and fuzzy real images of the health status of an employee are developed and an algorithm is described for assessing the deviation of generated medical parameters from the norm. The paper also compiles the rules to form the knowledge bases of a distributed intelligent system for remote continuous monitoring. It is assumed that embedding this base into the intelligent system architecture will objectively assess the trends in the health status of workers and make informed decisions to eliminate certain problems

An Architecture for Intelligent Systems Based on Smart Sensors

Based on requirements for a next-generation rocket test facility, elements of a prototype intelligent rocket test facility (IRTF) have been implemented. The preliminary results provide the basis for future advanced development and validation using rocket test stand facilities at Stennis Space Center (SSC). Key components include distributed smart sensor elements integrated using a knowledge-driven environment. One of the specific goals is to imbue sensors with the intelligence needed to perform self-diagnosis of health and to participate in a hierarchy of health determination at sensor, process, and system levels. We have identified issues important to further development of health-enabled networks, which should be of interest to others working with smart sensors and intelligent health management systems.

One summer at NASA's John C. Stennis Space Center

The test stands at SSC are enormous structures, each with several hundred - even thousands - of sensors. With such a high sensor count and variety it is incredibly difficult to do anything useful with the influx of data in real time beyond recording it for future examination. However, it would be ideal if the test controller could not only view all of the data in an intuitive and information rich environment but also have the complementary sensor data fused together to provide more robust information. Enter the integrated intelligent health management system (IIHMS). The IIHMS is a collection of smart sensors, each aware of the local process they are measuring, controlled by an expert system with a global view of the entire system. The long-range goal is to develop an expert system to provide the test controller with a fully immersed environment in which to monitor the condition of every system component and receive advice on anomalies and proper courses of action. The IIHMS is now termed intelligent system health management (ISHM). Dave was assigned the hardware design of a smart temperature sensor that would form the foundation layer of the IIHMS; Don was responsible for the firmware that would add the intelligence to Dave's hardware by enabling it to monitor the health (i.e., quality) of the sensor and the quality of measurements in real time; Jon was left the task of designing the link between the smart sensor and the expert system. This work discusses the summer experience of the authors at SSC.