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Top1. Introduction
Wireless Sensor Networks (WSNs) are spatially distributed sensors systems that represent architecture of choice for distributed monitoring due to the ease of deployment and configuration. This is an active and emerging research area with applications in military, environmental monitoring, industrial control, home automation, health monitoring, and other applications. It is a promising technology for ubiquitous health monitoring and management, and monitoring of activities of daily living (Jovanov, Poon, Yang & Zhang, 2009; Jovanov & Milenkovic, 2011; Milenković, Otto, & Jovanov, 2006). In most of the applications, sensor nodes are deployed in harsh or changing environmental conditions (Chand Gautam & Chand Kaushal, 2014). In most of the WSN, sensor nodes cooperatively send data to a central device, such as smartphone gateway, that can be used to control and configure the network (Jovanov, Milenkovic, Otto, & de Groen, 2005).
WSNs sense and collect data that must be time synchronized to enable essential functions such as sleep scheduling of the nodes, localization, data fusion, coordination among nodes etc. In WSN, it is required to maintain a common notion of time among the sensor nodes (Huang, Zomaya, Delicato, & Pires, 2014). In a centralized wired system, the need for synchronized time is less stringent because of the lower latencies (Chand Gautam & Chand Kaushal, 2014). A combination of a wired system with wireless distributed monitoring system forms a Hybrid Sensor Network (HSN). Each sensor node (wired or wireless) in the network has its own local clock and they certainly have clock drift caused by environmental changes, such as pressure, temperature, and battery voltage (Sundararaman, Buy, & Kshemkalyani, 2005).
The sleep and user's activity monitoring, using small wearable sensors and a smartphone, has been increasingly used in many applications. Approaches for automatic sleep activity monitoring used by researchers vary in number, type, and placement of utilized sensors, as well as in processing of recorded signals. It has been demonstrated that smartphone sensing and processing can be used to monitor movement of chest and abdomen caused by breathing (Nandakumar, Gollakota, & Watson, 2015). iSleep uses a smartphone's microphone to monitor snoring and body movements and infer quantitative measures of sleep quality (Hao, Xing, & Zhou, 2013). Smartphone applications frequently include sleep diary (eDiary) for sleep related questionnaire and assessment (Yousaf, Hamida, & Ahmed, 2014).
In recent years, several sleep monitoring systems have been developed (Hamida, Hamida, & Ahmed, 2015) that are highly automated, and portable for use at home. However, the entire setup integrates a number of devices, sensors, and wires that has been proven to significantly disturb sleep of users, particularly during the first night of monitoring (McCall & McCall, 2012). Moreover, wired sensors and electrodes are not only inconvenient and uncomfortable but also affect the subject’s health and mood (Hamida et al., 2015). Hence introducing wireless sensor technology in sleep monitoring would significantly improve the comfort of users and the quality of monitoring. In the new era of sleep monitoring, wearable sensors, advanced sensing and wireless technology should be utilized to significantly improve the diagnosis and treatment of sleep related problems.
We developed a hybrid monitoring system to facilitate sleep studies using wireless inertial monitoring sensors (SP-10C) (Sensoplex, 2014) and a smartphone with a standard Polysomnography (PSG) sleep monitoring system. Smartphone has been used to facilitate time synchronization between wireless inertial sensors and the PSG system. Since standard PSG monitors use wired sensors for monitoring of sleep activities and Periodic Leg Movement (PLM), which is inconvenient to the users, we developed our system to monitor PLMs wirelessly to improve user's convenience.