Home sleep monitoring based on wrist movement data processing

Abstract In this paper, two original sleep monitoring algorithms, including threshold and k-means clustering algorithms are presented. All the proposed algorithms use only acceleration data acquired from the non-dominant wrist with a 3-axis accelerometer, allowing the detection of falling asleep and waking up and a classification into 4-sleep stages (“awake”, “light sleep”, “deep sleep” and “REM”). We validate the proposed methods by comparing them to the results of “Fitbit Charge 2” and “Withings Sleep Analyzer”. Based on wrist movement data collected during 10 nights of sleep of a volunteer, we can show that the algorithms obtain promising results that allow us to consider a new non-intrusive method for users and medical staff to follow the trend of sleep quality through long term monitoring. This longitudinal monitoring can help to detect abnormal changes in sleep that are usually a sign of a change in health status.

[1]  U. Ekelund,et al.  Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults , 2019, Scientific Reports.

[2]  N. Lazar,et al.  The ASA Statement on p-Values: Context, Process, and Purpose , 2016 .

[3]  Stanislas Chambon,et al.  A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Yunhao Liu,et al.  Sleep Hunter: Towards Fine Grained Sleep Stage Tracking with Smartphones , 2016, IEEE Transactions on Mobile Computing.

[5]  A. Dregan,et al.  Cross-country variation in sleep disturbance among working and older age groups: an analysis based on the European Social Survey , 2011, International Psychogeriatrics.

[6]  M. Carskadon,et al.  Chapter 2 - Normal Human Sleep : An Overview , 2005 .

[7]  W. Rottbauer,et al.  Automated sleep stage classification based on tracheal body sound and actigraphy , 2019, German medical science : GMS e-journal.

[8]  H. Montgomery-Downs,et al.  Actigraphy , 2018, Pediatric Sleep Medicine.

[9]  E. Campo,et al.  Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review , 2020 .

[10]  K Matsumoto,et al.  Evaluation of human activities and sleep‐wake identification using wrist actigraphy , 1998, Psychiatry and clinical neurosciences.

[11]  M. Ohayon.  [Prevalence and comorbidity of sleep disorders in general population]. , 2007, La Revue du praticien.

[12]  Jerome Boudy,et al.  Design and First Evaluation of a Sleep Characterization Monitoring System Using a Remote Contactless Sensor , 2017, IEEE Journal of Biomedical and Health Informatics.

[13]  Harald R. Gruber-Vodicka,et al.  Oxygen minimum zone cryptic sulfur cycling sustained by offshore transport of key sulfur oxidizing bacteria , 2018, Nature Communications.

[14]  Kemal Polat,et al.  Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting , 2010, Expert Syst. Appl..

[15]  Bongshin Lee,et al.  Investigating the Effectiveness of Cohort-Based Sleep Recommendations , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[16]  Yingjie Tian,et al.  A Comprehensive Survey of Clustering Algorithms , 2015, Annals of Data Science.

[17]  Francesco Sardanelli,et al.  Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach , 2020, European Radiology Experimental.

[18]  A. Rechtschaffen,et al.  A manual of standardized terminology, technique and scoring system for sleep stages of human subjects , 1968 .

[19]  Poul Jennum,et al.  Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy , 2017, Nature Communications.

[20]  A. Goldstone,et al.  A validation study of Fitbit Charge 2™ compared with polysomnography in adults , 2018, Chronobiology international.

[21]  Yan Li,et al.  EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  H. Nagaraja,et al.  How accurately does wrist actigraphy identify the states of sleep and wakefulness? , 2001, Sleep.