Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor

Abstract

Sleep disorders are a common affliction for many people even though sleep is one of the most important factors in maintaining good physiological and emotional health. Numerous researchers have proposed various approaches to monitor sleep, such as polysomnography and actigraphy. However, such approaches are costly and often require overnight treatment in clinics. With this in mind, the research presented here has emerged from the question: “Can data be easily collected and analyzed without causing discomfort to patients?” Therefore, the aim of this study is to provide a novel monitoring system for quantifying sleep quality. The data acquisition system is equipped with multimodal sensors, including a three-axis accelerometer and a pressure sensor. To identify sleep quality based on measured data, a novel algorithm, which uses numerous physiological parameters, was proposed. Such parameters include non-REM sleep time, the number of apneic episodes, and sleep durations for dominant poses. To assess the effectiveness of the proposed system, three participants were enrolled in this experimental study for a duration of 20 days. From the experimental results, it can be seen that the proposed monitoring system is effective for quantifying sleep quality.

Proposta de um Dispositivo Não Invasivo para Monitoramento do Ambiente do Sono

“…Os mapas de pressão de alta resolução gerados podem ser utilizados para o monitoramento do sono. Já o [Nam et al 2016] desenvolveu um sistema de monitoramento para quantificar a qualidade do sono.…”

Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

“…The collection of sensory data, from IoT, mobile, or wearable devices, generates time-series measurements of physical quantities such as velocity and acceleration that offers tremendous opportunities for applications. For example, monitoring different physiological parameters using accelerometer and pressure sensor can create time-series for monitoring users' health status [12]. However, malicious programs can secretly collect the time-series and use 1 All the code and data used in this paper is publicly available and can be obtained from: https://github.com/mmalekzadeh/replacement-autoencoder them to discover a wide range of sensitive information about users' activities [13].…”

Wearable accelerometer based extended sleep position recognition

“…The second approach uses wearable devices to gather data from 3-axis acceleration sensors.Nam et al [14] developed a sleep quality calculation method including the sleep positions and stages, apnea, and sleep time. The four main sleeping poses are tracked from data gathered from a threeaxis accelerometer placed on the hip and evaluated with a portable diagnostic system.…”

“…Furthermore, this is an immobile system as it requires sensors to be installed in or on the bed. The second category uses wearable devices [2], [14], [15], [21], which provide a mobile solution and are able to monitor different people in the same bed. A disadvantage is that the sensors have to be worn on the body, which can possibly lead to discomfort.…”