Proposal of a novel remote command and control configuration extension for interoperable Personal Health Devices (PHD) based on ISO/IEEE11073 standard

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Background

Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring.

Objective

This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered.

Methods

This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory.

Results

By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography.

Conclusions

Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

Analysis of ISO/IEEE 11073 built-in security and its potential IHE-based extensibility

The ISO/IEEE 11073 standard for Personal Health Devices (X73PHD) aims to ensure interoperability between Personal Health Devices and aggregators-e.g. health appliances, routers-in ambulatory setups. The Integrating the Healthcare Enterprise (IHE) initiative promotes the coordinated use of different standards in healthcare systems (e.g. Personal/Electronic Health Records, alert managers, Clinical Decision Support Systems) by defining profiles intended for medical use cases. X73PHD provides a robust syntactic model and a comprehensive terminology, but it places limited emphasis on security and on interoperability with IHE-compliant systems and frameworks. However, the implementation of eHealth/mHealth applications in environments such as health and fitness monitoring, independent living and disease management (i.e. the X73PHD domains) increasingly requires features such as secure connections to mobile aggregators-e.g. smartphones, tablets-, the sharing of devices among different users with privacy, and interoperability with certain IHE-compliant healthcare systems. This work proposes a comprehensive IHE-based X73PHD extension consisting of additive layers adapted to different eHealth/mHealth applications, after having analyzed the features of X73PHD (especially its built-in security), IHE profiles related with these applications and other research works. Both the new features proposed for each layer and the procedures to support them have been carefully chosen to minimize the impact on X73PHD, on its architecture (in terms of delays and overhead) and on its framework. Such implications are thoroughly analyzed in this paper. As a result, an extended model of X73PHD is proposed, preserving its essential features while extending them with added value.