ABSTRACT
Long-term sleep monitoring of patients has been identified as a useful tool to observe sleep trends manifest themselves over weeks or months for use in behavioral studies. In practice, this has been limited to coarse-grained methods such as actigraphy, for which the levels of activity are logged, and which provide some insight but have simultaneously been found to lack accuracy to be used for studying sleeping disorders. This paper presents a method to automatically detect the user's sleep at home on a long-term basis. Inertial, ambient light, and time data tracked from a wrist-worn sensor, and additional night vision footage is used for later expert inspection. An evaluation on over 4400 hours of data from a focus group of test subjects demonstrates a high re-call night segment detection, obtaining an average of 94%. Further, a clustering to visualize reoccurring sleep patterns is presented, and a myoclonic twitch detection is introduced, which exhibits a precision of 74%. The results indicate that long-term sleep pattern detections are feasible.
- M. A. Bain D, Ferguson-Pell M. Evaluation of Mattresses Using Interface Pressure Mapping. J Wound Care, 12(6):231--5, 2003.Google ScholarCross Ref
- H. Becker, M. Borazio, and K. Van Laerhoven. How to Log Sleeping Trends? A Case Study on the Long-Term Capturing of User Data. Smart Sensing and Context, pages 15--27, 2010. Google ScholarDigital Library
- M. H. Bonnet and D. L. Arand. Insomnia, Metabolic Rate and Sleep Restoration. Internal Medicine, 254:23--31, 2003.Google Scholar
- J. Caviness. Myoclonus. In Mayo Clinic Proceedings, volume 71, page 679. Mayo Clinic, 1996.Google Scholar
- C. Cortes and V. Vapnik. Support-Vector Networks. Machine Learning, 20(3):273--297, 1995. Google ScholarDigital Library
- J. De Koninck, P. Gagnon, and S. Lallier. Sleep Positions in the Young Adult and their Relationship with the Subjective Quality of Sleep. Sleep, 6(1):52, 1983.Google ScholarCross Ref
- C. A. Everson, M. Bergmann, and A. Rechtschaffen. Sleep Deprivation in the Rat: III. Total Sleep Deprivation. Sleep, 12(1):13--21, 1989.Google Scholar
- G.J. Welk and J.A. Schaben and J.A.Jr. Morrow. Reliability of Accelerometry-Based Activity Monitors: A Generalizability Study. In Medicine & Science in Sports & Exercise Vol.36 No.9, pages 1637--1645, 2004.Google Scholar
- S. Gordon, K. Grimmer, and P. Trott. Self Reported Versus Recorded Sleep Position: An Observational Study. In Internet Journal of Allied Health Sciences and Practice Vol2No1, 2004.Google Scholar
- S. Gyulay, L. Olson, M. Hensley, M. King, K. Allen, and N. Saunders. A Comparison of Clinical Assessment and Home Oximetry in the Diagnosis of Obstructive Sleep Apnea. The American review of respiratory disease, 147(1):50, 1993.Google Scholar
- E. Hoque, R. F. Dickerson, and J. A. Stankovic. Monitoring Body Positions and Movements During Sleep Using WISPs. In Wireless Health 2010, WH '10, pages 44--53, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- G. Jean-Louis, F. Zizi, H. Gyzicki, and P. Hauri. Actigraphic Assessment of Sleep in Insomnia: Application of the Actigraph Data Analysis Software (ADAS). In Physiology & Behavior Vol.65 No.4--5, pages 659--663, 1999.Google Scholar
- T. Kohonen. The Self-Organizing Map. Proceedings of the IEEE, 78(9):1464--1480, 1990.Google ScholarCross Ref
- W. Liao and C. Yang. Video-Based Activity and Movement Pattern Analysis in Overnight Sleep Studies. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pages 1--4. IEEE, 2008.Google ScholarCross Ref
- M. Michael Littner, C. Kushida, D. Dennis Bailey, R. Berry, D. Davila, and M. Hirshkowitz. Practice Parameters for the Role of Actigraphy in the Study of Sleep and Circadian Rhythms: An Update for 2002. Sleep, 26(3):337, 2003.Google ScholarCross Ref
- K. Murphy. Hidden Markov Model (HMM) for Matlab. http://www.cs.ubc.ca/ murphyk/Software/HMM/hmm.html.Google Scholar
- K. Murphy. Dynamic Bayesian Networks. Probabilistic Graphical Models, 2003.Google Scholar
- A. Oksenberg and D. Silverberg. The Effect of Body Posture on Sleep-Related Breathing Disorders: Facts and Therapeutic Implications. Sleep Medicine Reviews, 2(3):139--162, 1998.Google ScholarCross Ref
- T. Partonen and A. Magnusson. Seasonal Affective Disorder: Practice and Research, volume 66. Oxford University Press Oxford, 2001.Google Scholar
- K. Partridge and P. Golle. On Using Existing Time-Use Study Data for Ubiquitous Computing Applications. In UbiComp'08, pages 144--153, 2008. Google ScholarDigital Library
- T. Roehrs and T. Roth. Sleep, Sleepiness, and Alcohol Use. Alcohol Research & Health, 25(2):101--9, 2001.Google Scholar
- E. Roze, P. Bounolleau, D. Ducreux, V. Cochen, S. Leu-Semenescu, Y. Beaugendre, M. Lavallard-Rousseau, A. Blancher, F. Bourdain, P. Dupont, et al. Propriospinal Myoclonus Revisited. Neurology, 72(15):1301, 2009.Google ScholarCross Ref
- K. Van Laerhoven, M. Borazio, D. Kilian, and B. Schiele. Sustained Logging and Discrimination of Sleep Postures with Low-Level, Wrist-Worn Sensors. In Wearable Computers, 2008. ISWC 2008. 12th IEEE International Symposium on, pages 69--76. IEEE, 2008. Google ScholarDigital Library
- E. Weitzman et al. Delayed Sleep Phase Syndrome: A Chronobiological Disorder with Sleep-Onset Insomnia. Archives of General Psychiatry, 38(7):737--46, 1981.Google Scholar
Index Terms
- Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies
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