Unobtrusive Assessment of Motor Patterns During Sleep Based on Mattress Indentation Measurements

Verhaert, Vincent; Haex, Bart; Wilde, Tom De; Berckmans, Daniël; Vandekerckhove, Marie; Verbraecken, Johan; Sloten, Jos Vander

doi:10.1109/titb.2011.2131670

Cited by 26 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although it has been shown that during the sleep the amount of motion disturbances is minimal, about 3% as reported in [22], we still want heartbeat detection cope with those as efficiently as possible. Convergence tests in Section IV-C point out that an entirely missed detection may cause misplacement of not more than four subsequent heartbeat detections.…”

Section: Discussionmentioning

confidence: 99%

Optimization of Heartbeat Detection in Fiber-Optic Unobtrusive Measurements by Using Maximum A Posteriori Probability Estimation

Šprager

Zazula

2014

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

This paper deals with an optimized heartbeat detection multimethod by using maximum a posteriori probability (MAP). The approach was derived for unobtrusive fiber-optic measurements of cardiac activity. Multiple independent detection methods were selected and characterized by their delays and variability referring to cardiac electrical excitation (R waves). Validation of the approach was performed in two experiments involving 24 participants: 10 interferometric signals were recorded at rest, 14 with variable heart rate after physical exercise. The proposed MAP heartbeat detection was assessed by a cross validation in 250 iterations. Obtained results show the overall efficiency, which was estimated by a product of the sensitivity, precision, and variability of heartbeat detections, yields 97.04 ± 3.36% for the experiment with physical exercise and 97.07 ± 4.49% at rest. The method's accuracy guarantees that the heartbeat detections differ for 22 ± 5 ms and 22 ± 3 ms from the ECG reference in the two types of experiments, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimization of Heartbeat Detection in Fiber-Optic Unobtrusive Measurements by Using Maximum A Posteriori Probability Estimation

Šprager

Zazula

2014

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…Indentation was continuously measured and recorded throughout the nights at a sampling rate of 1 Hz. The main sleep postures (left/right lateral, prone, supine) were estimated from the indentation data using a Support Vector Machine (SVM) classifier, that used five features according to the classification scheme proposed by Verhaert et al [22]. This algorithm allows posture detection with an overall accuracy of 92%.…”

Section: Measurementsmentioning

confidence: 99%

Biomechanics-based active control of bedding support properties and its influence on sleep

Deun

Verhaert

Willemen

et al. 2012

Work

Self Cite

View full text Add to dashboard Cite

Proper body support plays an import role in the recuperation of our body during sleep. Therefore, this study uses an automatically adapting bedding system that optimises spinal alignment throughout the night by altering the stiffness of eight comfort zones. The aim is to investigate the influence of such a dynamic sleep environment on objective and subjective sleep parameters. The bedding system contains 165 sensors that measure mattress indentation. It also includes eight actuators that control the comfort zones. Based on the measured mattress indentation, body movements and posture changes are detected. Control of spinal alignment is established by fitting personalized human models in the measured indentation. A total of 11 normal sleepers participated in this study. Sleep experiments were performed in a sleep laboratory where subjects slept three nights: a first night for adaptation, a reference night and an active support night (in counterbalanced order). Polysomnographic measurements were recorded during the nights, combined with questionnaires aiming at assessing subjective information. Subjective information on sleep quality, daytime quality and perceived number of awakenings shows significant improvements during the active support (ACS) night. Objective results showed a trend towards increased slow wave sleep. On the other hand, it was noticed that % N1-sleep was significantly increased during ACS night, while % N2-sleep was significantly decreased. No prolonged N1 periods were found during or immediately after steering.

show abstract

“…The validated study found self-report to be accurate for the primary sleep postures of supine, sidelying and prone, but did not report any reliability data [25]. For postures described as intermediate postures, self-report has not been examined [27].…”

Section: Introductionmentioning

confidence: 99%

Examining the Relationship between Sleep Posture and Morning Spinal Symptoms in the Habitual Environment Using Infrared Cameras

Cary¹,

Collinson²

2016

J Sleep Disor: Treat Care

View full text Add to dashboard Cite

Introduction: Sleeping is generally considered a period for rest and recovery, however some people wake with spinal symptoms not present on going to sleep and seek treatment. It has been clinically postulated that some sleeping postures, especially those involving sustained end range rotation or extension, can provoke pain sensitive spinal tissues. While sleep research generally has blossomed, little attention has been paid to the physical effects of nocturnal posture on waking spinal symptoms. Furthermore, sleep research is generally conducted in high technology sleep laboratories that are expensive to operate and usually only accessible in metropolitan centers limiting availability to a broader population. We aimed to develop a recording protocol that was low cost, unobtrusive and portable, enabling sleep posture assessment to occur in a person's habitual environment. Method: Fifteen participants were recruited by word of mouth. Participants completed a Pre-Sleep Questionnaire. Two infrared cameras (placed overhead and foot end of bed) plus associated recording equipment were installed in their habitual sleeping area. One camera recorded continuously, the other camera was activated by motion detection. Recordings occurred over two consecutive nights, commencing automatically at 2000hrs and stopping at 0800hrs. Four sleeping postures were defined; supine, prone, supported sidelying, where the spine is neutral and ¾ sidelying, where the spine is rotated and extended. Recordings were viewed, posture classified and the time spent in each posture calculated. Time spent in each posture for night one and night two was analyzed to determine the presence of a first night effect. Results: The protocol was effective in capturing good quality video data. Utilising motion detection reduced analysis time by 50%. The classification system had high intra-rater reliability for all four postures (ICC > 0.91). No first night effect was detected. Participants' self-report was accurate for the proportion of the night spent in supine (ICC=0.7 95% CI 0.32 to 0.89) but not for the other three postures (ICC < 0.32 p ≤ 0.17). However when combining the two sidelying postures, self-report was accurate (ICC=0.57; 95%CI 0.10 to 0.83; p=0.01). There were no significant relationships found between the four postures and morning spinal symptoms.

show abstract

Unobtrusive Assessment of Motor Patterns During Sleep Based on Mattress Indentation Measurements

Cited by 26 publications

References 33 publications

Optimization of Heartbeat Detection in Fiber-Optic Unobtrusive Measurements by Using Maximum A Posteriori Probability Estimation

Optimization of Heartbeat Detection in Fiber-Optic Unobtrusive Measurements by Using Maximum A Posteriori Probability Estimation

Biomechanics-based active control of bedding support properties and its influence on sleep

Examining the Relationship between Sleep Posture and Morning Spinal Symptoms in the Habitual Environment Using Infrared Cameras

Contact Info

Product

Resources

About