A system for ubiquitous health monitoring in the bedroom via a Bluetooth network and wireless LAN

Novel design of smart sleep-lighting system for improving the sleep environment of children

BACKGROUND

Children struggle to fall asleep by themselves because of their physiological characteristics. Therefore, research has been carried on various devices (such as a smartphone) to assist in improving the sleep quality of children. However, all such devices need to be controlled by parents and do not have functions for monitoring the sleep environment.

OBJECTIVE

In this paper, a smart sleep-lighting system that includes a sleep-lighting device and a smartphone dongle is developed to improve the sleep environment of children.

METHODS

The temperature, humidity, and luminance of the sleep environment are monitored and analyzed by the sleep-lighting device to control multi-color light and audio components. The colored light emitted by the multi-color light can be adjusted to improve the sleep atmosphere. Also, the audio component can play white noise to induce sleep. In addition, parents can use a smartphone dongle with a multi-channel wireless communication method to monitor and control one or more lighting devices in different locations in real time.

RESULTS

For environmental monitoring, average difference between proposed device and commercial sensor from chamber setting temperature 15∘C to 35∘C was 0.588∘C ± 0.10∘C, and average error value of the humidity measurement was 0.74% at 40% ∼ 60% RH. Also, the manufactured sleep-lighting device shows good performance in multi-color light emission, and playing of white noise. As result, the smartphone connected to the proposed smartphone dongle enables monitoring and control of the proposed lighting device in a wireless well.

CONCLUSIONS

The manufactured sleep-lighting device has a high-precision temperature and humidity sensor and a luminance sensor that can accurately monitor the sleeping environment. The lighting device can play white noise to induce sleep in children. Also, a multi-color LED light is operated via a smartphone application to improve the sleep atmosphere. The measured data will be sent to the lighting device and processed together with sleep environment data in order to improve the sleep quality. Additionally, the final system will be tested for real end-users with clinical experiments by sleep research center of a university hospital.

The use of self-quantification systems for personal health information: big data management activities and prospects

BACKGROUND

Self-quantification is seen as an emerging paradigm for health care self-management. Self-quantification systems (SQS) can be used for tracking, monitoring, and quantifying health aspects including mental, emotional, physical, and social aspects in order to gain self-knowledge. However, there has been a lack of a systematic approach for conceptualising and mapping the essential activities that are undertaken by individuals who are using SQS in order to improve health outcomes. In this paper, we propose a new model of personal health information self-quantification systems (PHI-SQS). PHI-SQS model describes two types of activities that individuals go through during their journey of health self-managed practice, which are 'self-quantification' and 'self-activation'.

OBJECTIVES

In this paper, we aimed to examine thoroughly the first type of activity in PHI-SQS which is 'self-quantification'. Our objectives were to review the data management processes currently supported in a representative set of self-quantification tools and ancillary applications, and provide a systematic approach for conceptualising and mapping these processes with the individuals' activities.

METHOD

We reviewed and compared eleven self-quantification tools and applications (Zeo Sleep Manager, Fitbit, Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, uBiome, Digifit, BodyTrack, and Wikilife), that collect three key health data types (Environmental exposure, Physiological patterns, Genetic traits). We investigated the interaction taking place at different data flow stages between the individual user and the self-quantification technology used.

FINDINGS

We found that these eleven self-quantification tools and applications represent two major tool types (primary and secondary self-quantification systems). In each type, the individuals experience different processes and activities which are substantially influenced by the technologies' data management capabilities.

CONCLUSIONS

Self-quantification in personal health maintenance appears promising and exciting. However, more studies are needed to support its use in this field. The proposed model will in the future lead to developing a measure for assessing the effectiveness of interventions to support using SQS for health self-management (e.g., assessing the complexity of self-quantification activities, and activation of the individuals).

Wireless connectivity for health and sports monitoring: a review

This is a review of health and sports monitoring research that uses or could benefit from wireless connectivity. New, enabling wireless connectivity standards are evaluated for their suitability, and an assessment of current exploitation of these technologies is summarised. An example of the application is given, highlighting the capabilities of a network of wireless sensors. Issues of timing and power consumption in a battery-powered system are addressed to highlight the benefits networking can provide, and a suggestion of how monitoring different biometric signals might allow one to gain additional information about an athlete or patient is made.