Article

Classification of lying position using load cells under the bed.

Biomedical Engineering department, Oregon Health & Science University.3303 SW Bond Avenue, Portland, OR 97239, USA.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:474-7. DOI: 10.1109/IEMBS.2011.6090068
Source: PubMed

ABSTRACT Individuals who suffer from acid reflux at night, who snore chronically, or who have sleep apnea are frequently encouraged to sleep in a particular lying position. Side sleeping decreases the frequency and severity of obstructive respiratory events (e.g. apnea and hypopnea) in patients with positional sleep apnea. It has been suggested that individuals with Gastroesophageal Reflux Disease sleep on their left sides in order to help minimize symptoms. In this paper, we present a method of predicting the position of an individual lying on the bed using load cells placed under each of the bed supports. Our results suggest that load cells utilized in this manner could be successfully implemented into a system that tracks or helps train individuals to sleep in a particular lying position.

0 Bookmarks
 · 
71 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Efficient, low cost and easily deployable monitoring systems for elderly people are becoming every day more and more important if considering that the increase in longevity has been raising the average age of the population all over the world. For elderly people, wrong postural behaviors can indicate a main and preliminary marker of disease. It is possible to recognize the signs of this kind of problems by developing smart home monitoring technologies: the body posture can be modeled using information resulting from specific low cost sensors embedded in a bed or an armchair. This paper presents a low cost Smartphone application, able to transform a bed or an armchair into an intelligent device. A friendly interface has been defined in order to make this application also suitable for unskilled users.
    19th IMEKO TC 4 Symposium and 17th IWADC Workshop Advances in Instrumentation and Sensors Interoperability, Barcelona, Spain; 06/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Poor quality of sleep increases the risk of many adverse health outcomes. Some measures of sleep, such as sleep efficiency or sleep duration, are calculated from periods of time when a patient is asleep and awake. The current method for assessing sleep and wakefulness is based on polysomnography, an expensive and inconvenient method of measuring sleep in a clinical setting. In this paper, we suggest an alternative method of detecting periods of sleep and wake that can be obtained unobtrusively in a patient's own home by placing load cells under the supports of their bed. Specifically, we use a support vector machine to classify periods of sleep and wake in a cohort of patients admitted to a sleep lab. The inputs to the classifier are subject demographic information, a statistical characterization of the load cell derived signals, and several sleep parameters estimated from the load cell data that are related to movement and respiration. Our proposed classifier achieves an average sensitivity of 0.808 and specificity of 0.812 with 90% confidence intervals of (0.790, 0.821) and (0.798, 0.826), respectively, when compared to the "gold-standard" sleep/wake annotations during polysomnography. As this performance is over 27 sleep patients with a wide variety of diagnosis levels of sleep disordered breathing, age, body mass index, and other demographics, our method is robust and works well in clinical practice.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:5254-7. DOI:10.1109/EMBC.2012.6347179
  • [Show abstract] [Hide abstract]
    ABSTRACT: in this paper, we design and present a novel real-time sleeping position monitoring system. The sensing modules composed of 3-axis accelerometers are placed on object's forehead and chest to monitor the object's position during sleeping by calculating the angles between gravity vector and its three axes. System is driven by an inexpensive and low power microcontroller. In this system, we implement a proposed novel CORDIC-based algorithm on the embedded microcontroller so that the system is capable of transferring the raw data of the accelerometer from motion domain to angular domain in-line, and the system can provide the inclination or tilt angle information by itself in real-time. The sleeping position information is integrated into polysomnography (PSG) to cooperate the study of obstructive sleep apnea (OSA) syndrome.

Preview

Download
2 Downloads
Available from