Physical fitness and health of white collar business person is getting worse and worse in recent years. Therefore, it is necessary to develop a system which can enhance physical fitness and health for people. Although the exercise prescription can be generated after diagnosing for customized physical fitness and healthcare. It is hard to meet individual execution needs for general scheduling of physical fitness and healthcare system. So the main purpose of this research is to develop an intelligent scheduling of execution for customized physical fitness and healthcare system. The results of diagnosis and prescription for customized physical fitness and healthcare system will be generated by fuzzy logic Inference. Then the results of diagnosis and prescription for customized physical fitness and healthcare system will be scheduled and executed by intelligent computing. The scheduling of execution is generated by using genetic algorithm method. It will improve traditional scheduling of exercise prescription for physical fitness and healthcare. Finally, we will demonstrate the advantages of the intelligent scheduling of execution for customized physical fitness and healthcare system.

With the advent of the era of global high-tech industry and commerce and its associated sedentary lifestyle, opportunities for physical activity are reduced. People's physical fitness and health is deteriorating. Therefore, it is necessary to develop a system that can enhance people's physical fitness and health. However, it is difficult for general physical fitness and healthcare systems to meet individualized needs. The main purpose of this research is to develop a method of intelligent diagnosis and prescription for a customized physical fitness and healthcare system. The proposed system records all processes of the physical fitness and healthcare system via a wireless sensor network and the results of the diagnosis and prescription will be generated by fuzzy logic inference. It will improve individualized physical fitness and healthcare. Finally, we demonstrate the advantages of intelligent diagnosis and prescription for a customized physical fitness and healthcare system.

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This study proposes a health assessment and predictive assistance system for intelligent health monitoring. Through machine learning, the tool features a customized set of quantitative measurements and web analysis systems for physical and mental fitness. The system replaces the manpower and time requirements of the past necessary to conduct interviews and keep paper records, allowing users to observe and analyze physical and mental fitness status through the webpage. To achieve this, ECG, EEG, and EMAS are used to follow physiological, psychological, and meridian energy states. ASP.NET software is used as a development tool for the system cloud page, which constructs, documents, evaluates, and predicts functions for the smart health assistance system. The measurement data is entered and recorded in the cloud database. The data is used to construct an assessment and prediction of the user’s state of mind and body through machine learning methods, as well as the individual’s physical and mental fitness.

Abstract. With the advent of global high-tech industry and commerce era, the sedentary reduces opportunities of physical activity. And physical fitness and health of people is getting worse and worse. At present, the shortage of physical fitness instructors greatly affected the effectiveness of health promotion. Therefore, it is necessary to develop an auxiliary system which can reduce the workload of instructors and enhance physical fitness and health for people. But current general physical fitness and healthcare system is hard to meet individualized needs. The main purpose of this research is to develop an intelligent auxiliary system for customized physical fitness and healthcare. It records all processes of physical fitness and healthcare system by wireless sensors network. The results of intelligent auxiliary systems for customized physical fitness and healthcare will be generated by fuzzy logic Inference. It will improve individualized physical fitness and healthcare. Finally, we will demonstrate the advantages of the intelligent auxiliary system for customized physical fitness and healthcare.

In recent years, it is quite important to develop a customized system which can enhance physical fitness and health for people. And the system reliability is more important. In the paper, a fool-proofing design and crisis management for customized physical fitness and healthcare system is proposed. It is designed to prevent the failure of the various mechanisms of customized physical fitness and healthcare system, including records, surveillance, assessments, predictions, diagnosis, prescription, and scheduling. It is separated into (1) fool-proofing design module (2) crisis management module. The fool-proofing indexes are set to prevent the failure of the various mechanisms. The states of the various mechanisms are managed by the auto-checked fool-proofing indexes. If mistakes prevention was fail, we have to execute the crisis management for stopping harmful results. The crisis management will find the error level and response the solution by using fuzzy method. By the experiments, we can find the advantages of the fool-proofing design and crisis management for customized physical fitness and healthcare system. And it is effective to prevent the failure of the various mechanisms of intelligent customized physical fitness and healthcare system.