Heat stroke can be potentially damaging for people while exercising in hot environments. To prevent this dangerous situation, we designed a wearable heat-stroke-detection device (WHDD) with early notification ability. First, we used several physical sensors, such as galvanic skin response (GSR), heart beat, and body temperature, to acquire medical data from exercising people. In addition, we designed risk evaluation functional components that were based on fuzzy theory to detect the features of heat stroke for users. If a dangerous situation is detected, then the device will activate the alert function to remind the user to respond adequately to avoid heat stroke.

When exercising in a high-temperature environment, heat stroke can cause great harm to the human body. However, runners may ignore important physiological warnings and are not usually aware that a heat stroke is occurring. To solve this problem, this study evaluates a runner’s risk of heat stroke injury by using a wearable heat stroke detection device (WHDD), which we developed previously. Furthermore, some filtering algorithms are designed to correct the physiological parameters acquired by the WHDD. To verify the effectiveness of the WHDD and investigate the features of these physiological parameters, several people were chosen to wear the WHDD while conducting the exercise experiment. The experimental results show that the WHDD can identify high-risk trends for heat stroke successfully from runner feedback of the uncomfortable statute and can effectively predict the occurrence of a heat stroke, thus ensuring safety.

The study aims to provide an ease-of-use approach for senior patients to utilize remote healthcare systems. An ease-of-use remote healthcare system (RHS) architecture using RFID (Radio Frequency Identification) and networking technologies is developed. Specifically, the codes in RFID tags are used for authenticating the patients' ID to secure and ease the login process. The patient needs only to take one action, i.e. placing a RFID tag onto the reader, to automatically login and start the RHS and then acquire automatic medical services. An ease-of-use emergency monitoring and reporting mechanism is developed as well to monitor and protect the safety of the senior patients who have to be left alone at home. By just pressing a single button, the RHS can automatically report the patient's emergency information to the clinic side so that the responsible medical personnel can take proper urgent actions for the patient. Besides, Web services technology is used to build the Internet communication scheme of the RHS so that the interoperability and data transmission security between the home server and the clinical server can be enhanced. A prototype RHS is constructed to validate the effectiveness of our designs. Testing results show that the proposed RHS architecture possesses the characteristics of ease to use, simplicity to operate, promptness in login, and no need to preserve identity information. The proposed RHS architecture can effectively increase the willingness of senior patients who act slowly or are unfamiliar with computer operations to use the RHS. The research results can be used as an add-on for developing future remote healthcare systems.

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Existing health promotion systems (HPSs) have some shortcomings, such as lacking in the ability to plan exercise prescriptions (EPs) for individuals automatically, difficulty in acquiring in a timely manner the physiologic signals of users who are doing exercise, and having no efficient mechanisms for notifying medical personnel of users' emergent conditions. Aimed at addressing these shortcomings, in this study, we leverage the technologies of wireless sensor network (WSN), mobile communication, and cloud computing to develop a cloud-based HPS. The HPS contains a health promotion cloud service platform that can construct users' physical fitness models and then generate appropriate EPs for the users to perform various exercises. On the user side, in this study, we design an exercise-sensing device that can timely acquire the physiologic signals and global positioning system (GPS) positioning information of users who are doing exercise through the WSN. Several sensors, such as a GPS sensor, are designed into the exercise-sensing device. The exercise-sensing device can then send those data to the cloud service platform through mobile phone communication to allow the users to master their exercise records. In this study, we also design a cloud alarm mechanism that can efficiently notify medical personnel and family members of the user's emergent states through mobile phone push service. In addition, on the basis of fuzzy inference, we develop an EP adaptation mechanism that can accept the feedback information from the user and can then automatically adjust the user's EP to an appropriate strength level. The results of this study can be a useful reference for constructing new-generation HPSs.