Snehal R. Sawale et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.4, April- 2014, pg. 564-571 © 2014, IJCSMC All Rights Reserved 564 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 3, Issue. 4, April 2014, pg.564 – 571 RESEARCH ARTICLE Multimodality Sensor System for Sleep- Quality Monitoring Ms. Snehal R. Sawale 1 , Prof. Vijay S. Gulhane 2 ¹Student of ME 2 nd sem, Computer Engg., Sipna College of Engineering and Technology, Amravati ,Maharashtra, India ²Associate Professor, Department of Computer Science and Engg., Sipna College of Engineering and Technology, Amravati ,Maharashtra, India 1 snehalsawale16@gmail.com; 2 v_gulhane@rediffmail.com Abstract— Multimodality is the mixture of textual, audio, and visual modes in combination with media and materiality to create meaning. The influence of sleep conditions to human health and performance is currently well known but still underestimated and monitoring devices are not widespread. This paper describes methodology and prototype design of a sleep monitoring. Sleep monitoring is an important issue and has drawn considerable attention in medicine and healthcare. Given that traditional approaches, such as polysomnography, are usually costly, and often require subjects to stay overnight at clinics, there has been a need for a low-cost system suitable for long-term sleep monitoring. In this paper, we propose a system using low-cost multimodality sensors such as video, passive infrared, and heart-rate sensors for sleep monitoring. We apply machine learning methods to automatically infer a person’s sleep state, especially differentiating sleep and wake states. This is useful information for inferring sleep latency, efficiency, and duration that are important for long-term monitoring of sleep quality in healthy individuals and in those with a sleep-related disorder diagnosis. Our experiments show that the proposed approach offers reasonable performance compared to an existing standard approach (i.e., actigraphy), and that multimodality data fusion can improve the robustness and accuracy of sleep state detection. I. INTRODUCTION Sleep scoring belongs currently to the most innovative multimodal diagnostic methods, and is investigated by dozens of scientists all around the World. Since during the sleep all regulatory functions are under the sole control of the autonomous nervous system, sleep scoring benefits from the absence of voluntary behavior control from the subject under investigation [12]. Sleep laboratories require expensive infrastructure and well trained laboratory staff to provide reliable patient description. Sleep occupies more than one-third of human life. Sleep deprivation due to sleep-related disorders may introduce severe physical effects, cognitive impairments, and mental health complications [1]. An economical and minimally-invasive method to monitor sleep state and differentiate it from waking can provide valuable information about a person’s health, an early wa rning for, and long-term monitoring of response to therapies for sleep-related disorders. In practice, self-rated questionnaires and sleep diaries are routinely used for the assessment of sleep quality [2], [3]. Among the questionnaires, the Pittsburgh Sleep Quality Index (PSQI) has been a most widely used instrument [3]. It contains nineteen self-rated questions which form seven component scores. Each component score has a range of 0–3.