A Passive and Portable System for Monitoring Heart Rate and Detecting Sleep Apnea and Arousals: Preliminary Validation

Mack, David; Alwan, Majd; Turner, B.; Suratt, Paul M.; Felder, Robin A.

doi:10.1109/ddhh.2006.1624795

Cited by 43 publications

(24 citation statements)

References 1 publication

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“…The pressure sensitive mattress is an alternative non-intrusive approach to identify occurrence of movements, and the technique has been proposed for monitoring patients' respiratory activities [22]. However, to the authors' best knowledge, the pressure sensitive mattress approach has not been utilized to analyze body movements.…”

Section: Related Techniquesmentioning

confidence: 99%

Real time pose recognition of covered human for diagnosis of sleep apnoea

Wang¹,

Hunter²,

Gravill³

et al. 2010

Computerized Medical Imaging and Graphics

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Section: Related Techniquesmentioning

confidence: 99%

Real time pose recognition of covered human for diagnosis of sleep apnoea

Wang¹,

Hunter²,

Gravill³

et al. 2010

Computerized Medical Imaging and Graphics

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“…Although some sensors can be like motion detectors naturally described by symbolic data, theirs, such as the bed sensor, can be quantified. This was our case bed motion sensor for which the movement of the bed was empirical divided [11] into 4 categories: less than 3s, 3-7s, 7-14s, plus only 14 s (sensor ID 1-4 in Table I). Given two discrete time series S1 = {(s11 t11)... (s1n t1n)} of length n and s2 = {(s21 t21), ..., (s2m t2m)} of length m, with s1i, s2j € ∑ ,,, we can calculate their TSW similarity, TSW (S1, S2), using the following algorithm [26]:…”

Section: A Temporal Similarity Of Smith-watermanmentioning

confidence: 99%

“…MIT Place Lab, House, Independent lifestyle assistant in Honeywell, WA Are examples of such [5,6,7,8]. A number of activity identification Methods of assessing the ability of individuals to complete them The activities of everyday life such as eating, Cooking, drinking and medicine have been reported in Literature [9,10,11,12]. Key differences in reports The Activity Recognition methodologies are attributable to basic sensor technology, Automated and realistic learning models for experimental groups On [13,42].…”

Section: Introductionmentioning

confidence: 99%

The Similarity Quantification of Multidimensional Time Series Data Sets in Surveillance Applications for Health Monitoring System

Hasan¹

2017

ijarcs

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Abstract:In the last decade, Data mining techniques have been applied for sensor data in a wide range of applications. Like health care monitoring systems, manufacturing process. Intruder detection, database management and other. A lot of data mining engineering is based on the calculation of the similarity between two models of sensor data. A number of representations and Equality measures for multi -assign time series was suggested in the literature. In this paper, we describe a new way of calculating whether two similarities in the series of multiple series are based on the temporal version of Smith-Waterman (SW), a known information algorithm. Next, we apply our method to detect data on the demand for care of the elderly to early detection of the disease. Our procedure absorber is difficulties linked to the data uncertainty and aggregation that often occurs during treatment sensor data. The trials will take place one aging-in-place installation, Tiger Place placed in Columbia, MO. To validate our method we used data on nine no-portable a sensor for one-p located in TigerPlace apartments, combined with information of one Electronic Health Record (EHR).We deliver a set of experiments studying the temporal version of SW properties, with experiences on TigerPlace dataset.

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“…In [11], the authors use pressure and temperature sensors laid out in a grid pattern in the mattress to determine quality of sleep. NAPS ( [16]) is a low-cost physiological sensor-suite that can passively acquire important physiological and environmental characteristics. The NAPS suite allows subjects to simply lie on a mattress pad, embedded with vibration sensors, to obtain multidimensional data (e.g., body temperature, heart rate, respiration rate, positional mapping and movement).…”

Section: Related Workmentioning

confidence: 99%

Monitoring body positions and movements during sleep using WISPs

Hoque

Dickerson

Stankovic

2010

Wireless Health 2010

101

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Sleep monitoring is very important for elderly people as inadequate and irregular sleep are often related to serious diseases such as depression and diabetes. In many cases, it is necessary to monitor the body positions and movements made while sleeping because of their relationships to particular diseases (i.e., sleep apnea and restless legs syndrome). Analyzing movements during sleep also helps in determining sleep quality and irregular sleeping patterns. This paper presents a sleep monitoring system based on the WISP platform -active RFID-based sensors equipped with accelerometers. We show how our system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress. Movements and their duration are also detected by the system. We present the results of our empirical study from 10 subjects on three different mattresses in controlled experiments to show the accuracy of our inference algorithms. Finally, we evaluate the accuracy of the movement detection and body position inference for six nights on one subject, and compare these results with two baseline systems: one that uses bed pressure sensors and the other is an iPhone application.

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A Passive and Portable System for Monitoring Heart Rate and Detecting Sleep Apnea and Arousals: Preliminary Validation

Cited by 43 publications

References 1 publication

Real time pose recognition of covered human for diagnosis of sleep apnoea

Real time pose recognition of covered human for diagnosis of sleep apnoea

The Similarity Quantification of Multidimensional Time Series Data Sets in Surveillance Applications for Health Monitoring System

Monitoring body positions and movements during sleep using WISPs

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