Automated Detection of Movements During Sleep Using a 3D Time-of-Flight Camera: Design and Experimental Evaluation

Analyses of sleep-related movement disorders have gained importance due to an increase in life expectancy. The present approaches for measuring movements are based on electromyography or accelerometry and provide only local or specific results from muscles/limbs to which sensors have been attached. The motivation of this work was to investigate the detection of a more complete spectrum of sleep-related movements using a three-dimensional (3D) camera instead of the current conventional methods. In contrast to most of the previously published literature, this method allows for the detection of movements even when patients are covered with a blanket. This is the first work to evaluate movement detection with a clinical dataset and replicate the clinical environment in a laboratory setup. The laboratory setup allowed for the characterization of detectable movements through the determination of speed and amplitude limits. We used the Kinect One time-of-flight sensor to record 3D videos. Movements were quantified based on the temporal depth change in these 3D videos. A computer-controlled lifting table allowed for the controlled simulation of movements. Our algorithm detected movements with amplitude values >3.0 mm and velocity values >3.5 mm/s with an F1 score ≥95%. The shortest reliably detected duration of movement was 350 ms. In an ethically approved clinical study including 44 patients, 93.1% of electromyography-detected leg movements were also found in 3D. A significant correlation ( $\rho = 0.86$ ) was found between movements detected by the 3D system and polysomnography. The 3D system detected 31.2% more movements than electromyography. In addition to obtaining a broader spectrum of movements not limited to local and muscle/limb-specific movements, the usage of a contactless 3D camera simplifies the recording setup and preserves natural sleeping behavior. The presented 3D system may become useful for diagnostic purposes during sleep studies.

[1]  Heinrich Garn,et al.  0678 Contactless 3D Detection Of Leg Movements In Sleep , 2018 .

[2]  Ngianga-Bakwin Kandala,et al.  Sleep problems: an emerging global epidemic? Findings from the INDEPTH WHO-SAGE study among more than 40,000 older adults from 8 countries across Africa and Asia. , 2012, Sleep.

[3]  Bernard Widrow,et al.  Design and Validation of a Periodic Leg Movement Detector , 2014, PloS one.

[4]  Linda Denehy,et al.  Validity of the Microsoft Kinect for assessment of postural control. , 2012, Gait & posture.

[5]  Giuseppe Plazzi,et al.  Computer-assisted detection of nocturnal leg motor activity in patients with restless legs syndrome and periodic leg movements during sleep. , 2005, Sleep.

[6]  David Rand,et al.  Home monitoring of sleep with a temporary-tattoo EEG, EOG and EMG electrode array: a feasibility study , 2019, Journal of neural engineering.

[7]  Lena Maier-Hein,et al.  Real-Time Range Imaging in Health Care: A Survey , 2013, Time-of-Flight and Depth Imaging.

[8]  Diego A. Golombek,et al.  Comparative analysis of actigraphy performance in healthy young subjects , 2016, Sleep science.

[9]  E. Tolosa,et al.  Quantification of electromyographic activity during REM sleep in multiple muscles in REM sleep behavior disorder. , 2008, Sleep.

[10]  小林 美奈,et al.  The validity of the PAM-RL device for evaluating periodic limb movements in sleep and an investigation on night-to-night variability of periodic limb movements during sleep in patients with restless legs syndrome or periodic limb movement disorder using this system , 2014 .

[11]  R Ferri,et al.  World Association of Sleep Medicine (WASM) 2016 standards for recording and scoring leg movements in polysomnograms developed by a joint task force from the International and the European Restless Legs Syndrome Study Groups (IRLSSG and EURLSSG). , 2016, Sleep medicine.

[12]  D. Greenblatt,et al.  The International Classification of Sleep Disorders , 1992 .

[13]  Susan M. Astley,et al.  Evaluation of Kinect 3D Sensor for Healthcare Imaging , 2016, Journal of medical and biological engineering.

[14]  Omar Cauli,et al.  A survey on sleep assessment methods , 2018, PeerJ.

[15]  Werner Poewe,et al.  PLM detection by actigraphy compared to polysomnography: a validation and comparison of two actigraphs. , 2009, Sleep Medicine.

[16]  T. Loddenkemper,et al.  Automated seizure detection systems and their effectiveness for each type of seizure , 2016, Seizure.

[17]  Ming-Sui Lee,et al.  Multiparameter Sleep Monitoring Using a Depth Camera , 2012, BIOSTEC.

[18]  Gerard de Haan,et al.  Robust and Sensitive Video Motion Detection for Sleep Analysis , 2014, IEEE Journal of Biomedical and Health Informatics.

[19]  Joan Santamaria,et al.  Screening for idiopathic REM sleep behavior disorder: usefulness of actigraphy , 2018, Sleep.

[20]  Winson C.C. Lee,et al.  Evaluation of the Microsoft Kinect as a clinical assessment tool of body sway. , 2014, Gait & posture.

[21]  Andrew D. Payne,et al.  A 0.13 μm CMOS System-on-Chip for a 512 × 424 Time-of-Flight Image Sensor With Multi-Frequency Photo-Demodulation up to 130 MHz and 2 GS/s ADC , 2015, IEEE Journal of Solid-State Circuits.

[22]  Rainer Stiefelhagen,et al.  Sleep position classification from a depth camera using Bed Aligned Maps , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[23]  Heinrich Garn,et al.  0460 Detecting Respiratory Events By Respiratory Effort Derived From 3D Time-of-Flight Camera And SpO2 , 2019 .

[24]  Min Hong,et al.  Sleep Monitoring System Using Kinect Sensor , 2015, Int. J. Distributed Sens. Networks.

[25]  Gene Cheung,et al.  Sleep monitoring via depth video compression & analysis , 2014, 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[26]  Thomas Penzel,et al.  Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing , 2019, Scientific Reports.

[27]  Kyoungwoo Lee,et al.  Detecting periodic limb movements in sleep using motion sensor embedded wearable band , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[28]  Heinrich Garn,et al.  3D detection of the central sleep apnoea syndrome , 2017 .

[29]  Marjorie Skubic,et al.  Fall Detection in Homes of Older Adults Using the Microsoft Kinect , 2015, IEEE Journal of Biomedical and Health Informatics.

[30]  Haiwei Dong,et al.  An Intelligent Sensing System for Sleep Motion and Stage Analysis , 2012 .

[31]  G. Plazzi,et al.  Motor pattern of periodic limb movements during sleep , 2001, Neurology.

[32]  E. Sforza,et al.  The PAM-RL ambulatory device for detection of periodic leg movements: a validation study. , 2005, Sleep medicine.

[33]  Serena Dittoni,et al.  An integrated video-analysis software system designed for movement detection and sleep analysis. Validation of a tool for the behavioural study of sleep , 2012, Clinical Neurophysiology.

[34]  Heinrich Garn,et al.  Measurement of respiratory effort in sleep by 3D camera and respiratory inductance plethysmography , 2019, Somnologie.

[35]  Azadeh Yadollahi,et al.  Automated Non-Contact Detection of Head and Body Positions During Sleep , 2019, IEEE Access.

[36]  Elise Lachat,et al.  Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling , 2015, Remote. Sens..