![]() ![]() The main contributions of this paper are listed as follows.įor home-based sleep monitoring applications, in addition to the standard PSQI test, the proposed Fuzzy/FSM sleep stage estimation scheme could be employed as an assistive tool to provide more convincible and reasonable results with measured physical signals to support the sleep estimation. Moreover, the proposed system is not intended to substitute current standard methods, such as the PSG test, but to present an assistive approach for long-term sleep monitoring. Then, the measured data would be wirelessly transmitted to a centralised home server, on which the sleep stage estimation would be carried out by the proposed Fuzzy/FSM approach. As shown in Figure 1, the data acquisition is performed by using an HR monitor and a force sensor array under the pillow. In this paper, an integrated approach has been proposed for sleep stage estimation using fuzzy inference systems (FIS) and finite state machines (FSM). Although the PSQI is attempted to be a standardized form for assessing sleep quality, the personal and backward evaluation without measured biosignals to support it may lead to a limited and intuitive result. A higher score represents poorer subjective sleep quality. The measure is composed of 19 individual questions items, producing seven parts that generate one global score of sleep disturbance between 0 and 21. ![]() On the other hand, the Pittsburgh sleep quality index (PSQI) is a self-assessment form for sleep quality assessment. Therefore, the PSG test is pretty inconvenient to apply to sleep monitoring in the daily life. In addition, as sleeping in a hospital with abundant sensors uncomfortably attached to the human body, the recorded data may not exactly reflect the actual sleeping conditions. Even though the PSG provides an accurate and dependable estimation of sleep quality, the examination is inconvenient and the cost is very high. During the PSG test, multiple physiologic variables, including the electroencephalograph (EEG), electrooculography (EOG), electrocardiography (ECG), electromyography (EMG), nose and mouth airflow, blood pressure, and heart rate (HR), are measured and recorded. Polysomnography (PSG) is a standard test for the sleep study and usually performed overnight in a hospital or a specialised clinic under the guidance of a sleep specialist. Since the sleep disorder is caused by a multitude of factors, a simple approach with economical devices to long-term sleep monitoring could provide useful information for the proper selection of sleep-related treatments. However, several people suffer from insomnia at various degrees of severity. The principal function of sleep is to permit the brain to recover and repair itself and hence maintain the health of both mind and body. In the human life, adults generally spend approximately one-third of each day asleep. Experimental results show that the developed platform not only reduces the burden of PSG measurements, but also provides more convincible and reasonable results, presenting as an assistive tool of the conventional PSQI tests. Also, the fuzzy inference system is applied to the sleep depth evaluation, and then, the finite state machine is utilised to estimate the sleep stage. ![]() In the proposed platform, the sleep conditions, including the heart rate (HR) and body movement, are collected by an HR monitor and a force sensor array, respectively. Therefore, this paper is intended to develop a sleep stage estimation system for home health care services. On the other hand, although the Pittsburgh sleep quality index (PSQI) is a standardized form for sleep quality assessment, the subjective and backward evaluation may lead to intuitive results. However, PSG is not suitable to be used at home due to its complicated operation and expensive cost. Among them, the polysomnography (PSG) is performed more accurately. In recent years, research on sleep monitoring and analysis has attracted many scholars. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing. ![]() CAAI Transactions on Intelligence Technology. ![]()
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