Identification of nocturnal movements during sleep using the non-contact under mattress bed sensor

Walsh, Lorcan; Moloney, E; McLoone, Seán

doi:10.1109/iembs.2011.6090478

Cited by 13 publications

(10 citation statements)

References 10 publications

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“…For a more thorough description of the UMBS, including a description of the extraction of respiration rate and body movement, the reader is referred to previous work [14], [16]. Other work elsewhere is examining the use of the UMBS for in-bed mobility monitoring during postural transitions [42].…”

Section: Under Mattress Bed Sensormentioning

confidence: 99%

“…This contribution builds on previous research where novel algorithms were found to reliably measure breathing rate and body movement from UMBS data [14], and a preliminary study of a cohort of community based older adults [15] that showed high correlation between wrist actigraphy and motion metrics derived from UMBS data. This paper initially details a number of movement features which may be extracted during sleep building on previous research [16]. Subsequently, a classification setup is detailed which optimises the reliability and accuracy of the discrimination of sleep and wake using a large and rigorous research clinic-based dataset.…”

Section: Introductionmentioning

confidence: 99%

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Noncontact Pressure-Based Sleep/Wake Discrimination

Walsh

McLoone

Ronda

et al. 2017

IEEE Trans. Biomed. Eng.

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Poor sleep is increasingly being recognised as an important prognostic parameter of health. For those with suspected sleep disorders, patients are referred to sleep clinics which guide treatment. However, sleep clinics are not always a viable option due to their high cost, a lack of experienced practitioners, lengthy waiting lists and an unrepresentative sleeping environment. A home-based non-contact sleep/wake monitoring system may be used as a guide for treatment potentially stratifying patients by clinical need or highlighting longitudinal changes in sleep and nocturnal patterns. This paper presents the evaluation of an under-mattress sleep monitoring system for non-contact sleep/wake discrimination. A large dataset of sensor data with concomitant sleep/wake state was collected from both younger and older adults participating in a circadian sleep study. A thorough training/testing/validation procedure was configured and optimised feature extraction and sleep/wake discrimination algorithms evaluated both within and across the two cohorts. An accuracy, sensitivity and specificity of 74.3%, 95.5%, and 53.2% is reported over all subjects using an external validation dataset (71.9%, 87.9% and 56%, and 77.5%, 98% and 57% is reported for younger and older subjects respectively). These results compare favourably with similar research, however this system provides an ambient alternative suitable for long term continuous sleep monitoring, particularly amongst vulnerable populations.

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Section: Under Mattress Bed Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noncontact Pressure-Based Sleep/Wake Discrimination

Walsh

McLoone

Ronda

et al. 2017

IEEE Trans. Biomed. Eng.

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“…For example, for measuring sleep activity, instead of wearing an actigraph on the wrist, an accelerometer can be attached to the bed. Walsh et al [2] used a noncontact grid of 24 fiberoptic-based pressure sensors under a foam mattress to monitor sleep activity, restlessness in sleep, and sleeping patterns. In preliminary tests on a handful of patients, the system was shown to outperform wrist actigraphy and passive infrared motion detectors.…”

Section: By Gari D Clifford and Elnaz Gederimentioning

confidence: 99%

Out of Touch: From audio recordings to phone apps to mattress sensors, noncontact systems offer a less cumbersome way to monitor sleep

Clifford

Gederi

2014

IEEE Pulse

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The impact of poor and disrupted sleep on an individual is significant, affecting the quality of life physiologically, psychologically, and financially. It is estimated that a large population of people who suffer from sleep disorders is unaware of the condition and remains undiagnosed [1], creating a need (and desire) to self-monitor. However, sleep screening is generally cumbersome and complex, requiring multiple wearable sensors (and associated wires) and experts to interpret the large volumes of data.

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“…Sleep pattern monitoring is useful for the monitoring of many medical conditions as well as for quality of sleep [3] [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Bed occupancy monitoring: Data processing and clinician user interface design

Pouliot

Joshi

Goubran

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Unobtrusive and continuous monitoring of patients, especially at their place of residence, is becoming a significant part of the healthcare model. A variety of sensors are being used to monitor different patient conditions. Bed occupancy monitoring provides clinicians a quantitative measure of bed entry/exit patterns and may provide information relating to sleep quality. This paper presents a bed occupancy monitoring system using a bed pressure mat sensor. A clinical trial was performed involving 8 patients to collect bed occupancy data. The trial period for each patient ranged from 5-10 weeks. This data was analyzed using a participatory design methodology incorporating clinician feedback to obtain bed occupancy parameters. The parameters extracted include the number of bed exits per night, the bed exit weekly average (including minimum and maximum), the time of day of a particular exit, and the amount of uninterrupted bed occupancy per night. The design of a clinical user interface plays a significant role in the acceptance of such patient monitoring systems by clinicians. The clinician user interface proposed in this paper was designed to be intuitive, easy to navigate and not cause information overload. An iterative design methodology was used for the interface design. The interface design is extendible to incorporate data from multiple sensors. This allows the interface to be part of a comprehensive remote patient monitoring system.

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Identification of nocturnal movements during sleep using the non-contact under mattress bed sensor

Cited by 13 publications

References 10 publications

Noncontact Pressure-Based Sleep/Wake Discrimination

Noncontact Pressure-Based Sleep/Wake Discrimination

Out of Touch: From audio recordings to phone apps to mattress sensors, noncontact systems offer a less cumbersome way to monitor sleep

Bed occupancy monitoring: Data processing and clinician user interface design

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