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Rate and Distribution of Body Movements during Sleep in Humans

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Abstract

Body movements were measured during sleep with a mechanoelectrical transducer in 11 healthy adults. Also measured were the electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). Each subject slept alone in a quiet room for 21 to 44 consecutive nights. Body movements were classified as minor movements (actogram signal or head leads artifact), major movements (actogram signal plus head leads artifact), or movement time (MT). There was a strong relationship between rate of body movements and sleep stages, with the rate decreasing along the following sequence of stages: W greater than S1 greater than REM greater than S2 greater than S (3 + 4). If the body movements for all nights are pooled per subject, the distribution of body movement rates shows hardly any overlap for the Stages 1, REM, 2, and (3 + 4). The relative frequency of body movements seems to be regulated by a stage-dependent mechanism. The reliability of the body movement rate was determined by computing correlations between pairs of adjacent nights, which resulted in a rtt = .69. When 2 to 9 nights were pooled stepwise according to a split-half procedure, the mean rtt increased and reached values between .80 and .90, which means that body movements are a reliable sleep measure especially if the time base is large enough.
... Differentiating these stages is important but challenging due to similar features among them. In addition, medical research [20] shows that there is a strong relationship between body movement and sleep stage, i.e., the rate of body movements decreases with the stage in the following sequence: Wake >N1 >REM >N2 >N3. SMARS applies a simple threshold to represent motion statistics, which may not be able to efficiently capture body movements. ...
... Contactless sleep monitoring typically uses radar devices and mobile phone sensors. Radar devices have been used in DoppleSleep [28] and RF-Sleep [29] to acquire heart rate, respiratory rate, and body movement information more accurately [20], [21], AND [37] by high-frequency radio signals. RF-Sleep achieves an accuracy of 79.8% for four-class classification. ...
... III. SYSTEM OVERVIEW Studies [5], [6], [20], [37] show that sleep stages vary in the respiration rate, variability, fractional inspiration time (FIT), depth, and body movement rate. We summarize these differences in Table I. ...
Article
Sleep monitoring is essential to people’s health and well-being, which can also assist in the diagnosis and treatment of sleep disorder. Compared with contact-based solutions, contactless sleep monitoring does not attach any device to the human body, hence it has attracted increasing attention in recent years. Inspired by the recent advances in Wi-Fi based sensing, this paper proposes a low-cost and non-intrusive sleep monitoring system using commodity Wi-Fi devices, namely WiFi-Sleep. We leverage the fine-grained channel state information from multiple antennas and propose advanced fusion and signal processing methods to extract accurate respiration and body movement information. We introduce a deep learning method combined with clinical sleep medicine prior knowledge to achieve four-stage sleep monitoring with limited data sources (i.e., only respiration and body movement information). We benchmark the performance of WiFi-Sleep with polysomnography, the gold reference standard. Results show that WiFi-Sleep achieves an accuracy of 81.8%, which is comparable to the state-of-the-art sleep stage monitoring using expensive radar devices.
... According to [55], [74], [75], the sleep stages are related to the respiration and body movements. For instance, [75] claims that sleep can be measured through body movements due to the strong correlation between sleep stage and body movement rate. ...
... According to [55], [74], [75], the sleep stages are related to the respiration and body movements. For instance, [75] claims that sleep can be measured through body movements due to the strong correlation between sleep stage and body movement rate. As mentioned early, we do not use the motion-sensing module in our system. ...
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In this paper, we first present a single-input, multiple-output convolutional neural network that can estimate both heart rate and respiration rate simultaneously by exploiting the underlying link between heart rate and respiration rate. The inputs to the neural network are the amplitude and phase of channel state information collected by a pair of WiFi devices. Our WiFi-based technique addresses privacy concerns and is adapt- able to a variety of settings. This system overall accuracy for the heart and respiration rate estimation can reach 99.109% and 98.581%, respectively. Furthermore, we developed and analyzed two deep learning-based neural network classification algorithms for categorizing four types of sleep stages: wake, rapid eye movement (REM) sleep, non-rapid eye movement (NREM) light sleep, and NREM deep sleep. This system overall classification accuracy can reach 95.925%
... Thermoregulation is critical to sleep as a drop in core body temperature triggers sleep onset and a rise in temperature promotes wakefulness (Murphy and Campbell 1997). The rate of body movements during sleep is related to different sleep stages (Wilde-Frenz and Schulz 1983) and increased body movements (position changes) are a characteristic of poor sleep quality (Kaartinen et al. 2003). The primary sleeping positions are supine, prone, and lateral; with the latter being the most adopted position (Skarpsno et al. 2017). ...
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Purpose To determine the efficacy of decreasing spinal curvature – when sleeping laterally – in reducing low-back pain (LBP) and improving sleep quality in people with chronic LBP. Secondly, to investigate whether sleeping positions, nocturnal movements, and skin temperature are related to pain in people with chronic LBP. Methods Sixteen subjects with chronic LBP (50% female, mean age 45.6 ± 13.1 years) slept for one night on their own mattress, followed by three nights on an experimental mattress – designed to reduce spinal curvature in lateral sleeping positions – and then a final night again on their own mattress. Sleep positions, nocturnal movements, skin temperature, and room temperature were measured throughout the five nights. Numerical pain ratings for pain while lying, pain on rising, stiffness on rising, sleep quality, and mattress comfort were recorded for both mattresses. Results The experimental mattress was associated with 18% (p<.05) lower pain scores while lying and a 25% (p<.01) higher comfort rating. Pain on rising, stiffness on rising, and sleep quality were not different between own and experimental mattress. The relationship between sleep positions and pain scores was non-significant, but pain when rising was positively correlated with nocturnal movement (p<.05) and skin temperature was negatively correlated with pain while lying (p<0.05). Conclusion Pain while lying in bed decreased and comfort was higher for the experimental mattress compared to the participants’ own mattresses.
... In clinical environments such as operating rooms (ORs) and intensive care units (ICUs), key events during patient monitoring include: (1) Patient movements while lying in bed and in mobility within the room [1,2]; (2) Bedside monitor alarm triggers and noise pollution [2][3][4][5][6]; (3) Presence, absence and movement of clinical personnel in the patient's vicinity [7][8][9]; and (4) Variations in the ambient light, temperature, and humidity [2,6,10]. In home environments, key events that are generally untracked but are beneficial for patient monitoring include: (1) Patient bodily movement during sleep [11,12]; (2) Patient movement around their residence [13]; (3) Doorbell triggers, smoke-detector triggers, microwave beeps, and phone rings [14]; and (4) Changes in the ambient light, temperature, and humidity [15]. ...
Article
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The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.
... QS is defined as a sleep period during which an infant's eyes are closed, there is lack of eye and body movements, and respiration is regular. Following these definitions, QS cannot be directly compared to NREM sleep, since for the N1 and N2 types of NREM sleep movements and irregular respiration are common (Kirjavainen et al., 1996;Shimohira et al., 1998;Wilde-Frenz & Schulz, 1983), and respiration is often irregular (Kirjavainen et al., 1996). That a direct comparison is difficult is also visible in our results, where the kappa scores for comparing BSA to PSG according to Wake|REM + |NREM − were much higher F I G U R E 6 Inter-rater reliability (Cohen's Kappa coefficient) of annotations per behavioral state between two behavioral annotators (BSA1 and BSA2), and between each of them and a PSG annotator, in (a) Wake, (b) AS (and REM + in scheme A, REM in scheme B), and (c) QS (and NREM − in scheme A, NREM in scheme B). ...
Article
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In infants, monitoring and assessment of sleep can offer valuable insights into sleep problems and neuro‐cognitive development. The gold standard for sleep measurements is polysomnography (PSG), but this is rather obtrusive, and unpractical in non‐laboratory situations. Behavioral observations constitute a non‐obtrusive, infant‐friendly alternative. In the current methodological paper, we describe and validate a behavior‐based framework for annotating infant sleep states. For development of the framework, we used existing sleep data from an in‐home study with an unobtrusive test setup. Participants were 20 infants with a mean age of 180 days. Framework development was based on Prechtl's method. We added rules and guidelines based on discussions and consent among annotators. Key to using our framework is combining data from several modalities, for example, closely observing the frequency, type, and quality of movements, breaths, and sounds an infant makes, while taking the context into account. For a first validation of the framework, we set up a small study with 14 infants (mean age 171 days), in which they took their day‐time nap in a laboratory setting. They were continuously monitored by means of PSG, as well as by the test setup from the in‐home study. Recordings were annotated based both on PSG and our framework, and then compared. Data showed that for scoring wake vs. active sleep vs. quiet sleep the framework yields results comparable to PSG with a Cohen's Kappa agreement of ≥0.74. Future work with a larger cohort is necessary for further validating this framework, and with clinical populations for determining whether it can be generalized to these populations as well. We developed a new framework for annotating behavioral states in infants. It is based on existing frameworks, and extends these. We ran a small lab study as a first validation of the framework.
... For that reason, and to further investigate the influence of the inner clock on the awakening process, we extend the findings of existing work by comparing the CAR between spontaneous awakening and awakening by a known and an unknown alarm in a home environment. We complement this by additionally analyzing pre-awakening movement, assessed using a wristworn IMU sensor, since it was shown that movement is related to the current sleep stage [10] and could, therefore, also be an indicator for the initiation of the awakening process. To the best of our knowledge, our work is the first to assess the influence of the inner clock on pre-awakening movement and to assess the effect of an unknown alarm on the CAR in a home setting. ...
... The analysis of movements involving large muscle groups has proven effective and helpful in other conditions beyond RSD, such as central respiratory pauses 17 and Gilles de la Tourette syndrome, 18 as examples, and experts in the field have delineated their own methods to assess body movements during sleep. [19][20][21][22] A c c e p t e d M a n u s c r i p t ...
Article
There is a gap in the manuals for scoring sleep-related movements because of the absence of rules for scoring large movements. A taskforce of the International Restless Legs Syndrome Study Group elaborated rules that define the detection and quantification of movements involving large muscle groups. Consensus on each of the criteria in this paper was reached by testing the presence of consensus on a first proposal; if no consensus was achieved, the concerns were considered and used to modify the proposal. This process was iterated until consensus was reached. A preliminary analysis of the duration of movements involving large muscle groups was also carried out on data from two previous studies, which, however, used a visual analysis of video-polysomnographic recordings obtained from children or adults. Technical specifications and scoring rules were designed for the detection and quantification of large muscle group movements during sleep with a duration between 3 and 45 s in adults or 3 and 30 s in children, characterized by an increase in electromyographic activity and/or the occurrence of movement artifact in any combination of at least two recommended channels and not meeting the criteria for any other type of movement. Large muscle group movements are often accompanied by sleep stage changes, arousals, awakenings, and heart rate rises. The absence of clear and detailed rules defining them has likely impeded the development of studies that might disclose their clinical relevance; these new rules fill this gap.
... Methodologies to quantify sleep can be classified into two broad categories: in the first, physiological variables are monitored during sleep and used for sleep stage classification and sleep quality assessments [3], [4]; the second category of methodologies examines individuals' external body characteristics during sleep, by pose and movement detection [5], [6]. During a sleep episode, periods of immobility are interspersed with movements that may or may not lead to a change in body position or sleep pose and may also be associated with brief awakenings [7]. Body movements during sleep and brief awakenings are directly related to the perceived quality and depth of sleep [8]. ...
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Sleep quality is an important determinant of human health and wellbeing. Novel technologies that can quantify sleep quality at scale are required to enable the diagnosis and epidemiology of poor sleep. One important indicator of sleep quality is body posture. In this paper, we present the design and implementation of a non-contact sleep monitoring system that analyses body posture and movement. Supervised machine learning strategies applied to noncontact vision-based infrared camera data using a transfer learning approach, successfully quantified sleep poses of participants covered by a blanket. This represents the first occasion that such a machine learning approach has been used to successfully detect four predefined poses and the empty bed state during 8-10 hour overnight sleep episodes representing a realistic domestic sleep situation. The methodology was evaluated against manually scored sleep poses and poses estimated using clinical polysomnography measurement technology. In a cohort of 12 healthy participants, we find that a ResNet-152 pre-trained network achieved the best performance compared with the standard de novo CNN network and other pre-trained networks. The performance of our approach was better than other video-based methods for sleep pose estimation and produced higher performance compared to the clinical standard for pose estimation using a polysomnography position sensor. It can be concluded that infrared video capture coupled with deep learning AI can be successfully used to quantify sleep poses as well as the transitions between poses in realistic nocturnal conditions, and that this non-contact approach provides superior pose estimation compared to currently accepted clinical methods.
... When comparing the pulse results for the different sleep stages, it can be observed that the deeper sleep stages provide the best results. Body movements could be an explanation for this, as the movement characteristics differ between sleep stages as has been studied by Wilde-Frenz et al. [18]. There is a strong relationship between rate of body movements and sleep stages, with the rate decreasing along the following sequence of stages: Wake > N1 > REM > N2 > N3. ...
Article
Polysomnography (PSG) is the current gold standard for the diagnosis of sleep disorders. However, this multiparametric sleep monitoring tool also has some drawbacks, e.g. it limits the patient's mobility during the night and it requires the patient to come to a specialized sleep clinic or hospital to attach the sensors. Unobtrusive techniques for the detection of sleep disorders such as sleep apnea are therefore gaining increasing interest. Remote photoplethysmography using video is a technique which enables contactless detection of hemodynamic information. Promising results in near-infrared have been reported for the monitoring of sleep-relevant physiological parameters pulse rate, respiration and blood oxygen saturation. In this study we validate a contactless monitoring system on eight patients with a high likelihood of relevant obstructive sleep apnea, which are enrolled for a sleep study at a specialized sleep center. The dataset includes 46.5 hours of video recordings, full polysomnography and metadata. The camera can detect pulse and respiratory rate within 2 beats/breaths per minute accuracy 92% and 91% of the time, respectively. Estimated blood oxygen values are within 4 percentage-points of the finger-oximeter 89% of the time. These results demonstrate the potential of a camera as a convenient diagnostic tool for sleep apnea, and sleep disorders in general.
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This study aimed to identify the motion sequences and time-flow of roll-overs during sleep in women to develop a self-helped roll-over maneuver for elderly patients. Shifts from deeper sleep stages to lighter stages leading to arousal occur at roll-over-onset. Quick re-falling-asleep after completion may aid peaceful sleep continuation. Motion sequences of six women aged 43-65 years were examined at a sleep laboratory using polysomnography and infrared video. Relationships among phase I (before roll-over onset), II (roll-over movements), and III (roll-over end to re-falling-asleep onset) were determined. Mean total sleep time was 6.72±0.77 h, with over 80% sleep efficiency. Among 12 patterns examined, only supine-to-left and supine-to-right lateral roll-overs were classified: type A, sliding waist with pause (n=11, sleep); B, sliding waist without pause (n=56, sleep and arousal); and C, without sliding waist or pause (n=1, arousal). Time spent in phase I and III in type A were correlated (r=0.78, p=0.005), and were the shortest among the types. Discriminant analysis for type A (n=11) and B (n=20, sleep) showed 80.6% correct classification. In conclusion, type A roll-overs, involving efficient motion with quick re-falling-asleep, may be a useful foundation to develop self-helped roll-over maneuvers.
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The periodic alternation between REM and NREM sleep was analyzed. Usually, sleep records of consecutive nights of a subject are regarded to be independent events. However, it may be that consecutive nights are realizations of a continuously ongoing rhythm. This was tested in the present study. The temporal patterns of REM and NREM sleep in sequences of about 30 consecutive nights for 3 subjects were analyzed. The results show that only the onset of the first REM sleep phase during any one night may be predicted from the sleep onset time, whereas a systematic phase shift between consecutive nights was observed in the later REM sleep phases. Thus, the onset of later REM sleep phases is better predicted by assuming a rhythm with stable period length which controls the appearance of REM sleep phases in successive nights. Under the experimental conditions the phase shift was between 5 and 10 min per 24 hrs for the 3 subjects. The result is accordance with Kleitman's basic rest activity cycle (BRAC) hypothesis.
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• Human sleep is characterized by episodes of immobility and by major body movements occurring in phase with the EEG sleep cycle. Using time-lapse videotape recording and electrophysiological monitoring, we studied the sleep of four normal subjects to determine the relationship of the movements to sleep cycle phase and the consistency of the power of the movement data to quantify sleep parameters. We were able to measure sleep latency and to predict the time of occurrence of non—rapid eye movement sleep episodes in our subjects from the video movement data alone. In addition, we discovered evidence of small body movement specific to rapid eye movement sleep.
In 33 adults, discrete periods of rapid eye movement potentials were recorded without exception during each of 126 nights of undisturbed sleep. These periods were invariably concomitant with a characteristic EEG pattern, stage 1.Composite histograms revealed that the mean EEG, eye movement incidence, and body movement incidence underwent regular cyclic variations throughout the night with the peaks of eye and body movement coinciding with the lightest phase of the EEG cycles. A further analysis indicated that body movement, after rising to a peak, dropped sharply at the onset of rapid eye movements and rebounded abruptly as the eye movements ceased.Records from a large number of nights in single individuals indicated that some could maintain a very striking regularity in their sleep pattern from night to night.The stage 1 EEG at the onset of sleep was never associated with rapid eye movements and was also characterized by a lower auditory threshold than the later periods of stage 1. No dreams were recalled after awakenings during the sleep onset stage 1, only hypnagogic reveries.
The principle of 'a static charge sensitive bed' method for recording body movements during sleep is described. We made records during 30 nights and the measured total number of movements per night (80-200) is in agreement with the findings of studies based on a combination of direct observation, EMG and videotape. The method is simple, inexpensive and very sensitive to all kinds of movement.
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The actioculographic monitor (AOGM) is a newly developed, flexible system for sleep recording using three criteria: eye movements, body movements, and submental electromyogram. The signals are recorded on a four-channel Medilog casette tape and displayed by a mingograph at very low speed. The record is then scored after certain criteria for stages wake, nonrapid eye movement, and rapid eye movement sleep. High statistical correlations were obtained when scoring of the AOGM was compared to scoring of standard polygraphic sleep recordings.
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All-night polygraphic recordings were made on five normal human male subjects for two nights to study body motility quantitatively in relation to the sleep electroencephalogram. Body movements were significantly related temporally to preceding K-complexes during stage 2 sleep with a mean latency of 2.52 sec for 396 movements scored. This relationship was consistent for both nights one and two. The rate of body movements per minute was significantly lower in slow-wave sleep than in any other stage and was not different in stages 2 and REM. Movements in slow-wave sleep were more extensive and usually occurred at the end of periods, often heralding a change of stage. Brief isolated twitches of extremities were predominantly observed in stage REM. In all stages, movements of the face and mouth alone were frequent. An attempt was made to unify the known relations of K-complexes, body movements and autonomic activity and to organize them with respect to subcortical origins and electrophysiologic mechanisms.
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The reliability of sleep measures was calculated over two nights (and within the nights) for 20 young adult males. Percent time in stages 1, 2, 3, and 4, percent movement time, number of movements, and number of stage changes were significantly correlated between Ss over nights. The percent REM time and REM cycle duration were not significantly correlated over nights. Within Ss, the length of the REM period had a significant negative correlation with the length of the preceding NREM period but not with the following NREM period. These data raise questions as to the use of the standard sleep measures as reliable human traits in young male adults.
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Following 4 baseline nights, 7 Ss were deprived of REM sleep for 3 nights and 7 were deprived of stage 4 sleep. Both groups were then deprived of total sleep for 1 night and then allowed 2 nights of uninterrupted recovery sleep. Compared to baseline nights, on the first recovery night the number of body movements was significantly reduced in all sleep stages and for total sleep. On the second recovery night, the number of movements was back to baseline level. The increased amount of slow-wave sleep (stages 3 and 4) during recovery sleep was not the primary reason for the reduced body motility.
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Study of the transitory action phases (TAP) in the course of sleep, whether normal or disturbed by noise, allows the following statements to be made: the parameters of 'frequency', 'relative total duration' and 'average duration' of the TAP do not make it possible to reveal an effect due to the noise or a difference in sex functions, if the totality of the TAPs is considered. In both normal nights and in nights disturbed by noise there is a certain hierarchy between the stages of sleep in what concerns the frequency of the TAP, which increases progressively from stage (3 + 4) to stage 2, and from the latter to the PMO. Conversely, the average duration of the TAP increases progressively from the PMO to stage 2, and from the latter to stage (3 + 4). On considering separately in the disturbed night N2 the spontaneous TAPs and the concomitant provoked TAPs, it appears that there exists under the effect of the noise a certain compensation between these two types of TAP. If there is a certain number of concomitant provoked TAPs, they find their counterpart in a diminution of the spontaneous TAPs. In the same way the increase of the average duration of the concomitant provoked TAPs is compensated by diminution of the average duration of the spontaneous TAPs observed in the stages 2, (3 + 4) and the PMO. It is the existence of this phenomenon of compensation between the concomitant provoked TAPs and the spontaneous TAPs that renders inapparent the effect of the disturbance in N2 when the totality of the TAPs is considered. Moreover, this phenomenon of internal compensation constitutes an argument in favour of the intervention, in the course of the sleep, of a regulating mechanism that controls both the distribution and the duration of the TAPs if any external stimulations supervene.