Abstract
The paper presents results from the SmartSleep project which aims at developing a smartphone app that gives users individual advice on how to change their behaviour to improve their sleep. The advice is generated by identifying correlations between behaviour during the day and sleep architecture. To this end, the project addressed two sub-tasks: detecting a user’s daytime behaviour and recognising sleep stages in an everyday setting. In the case of daytime activity detection the best results were achieved using an accelerometer at the wrist and another one at the ankle (87%). A subsequent smoothing step increased the accuracy to over 90%. For recognising sleep architecture we experimented with various consumer wearables that we used in addition to the usual PSG sensors in a sleep lab. Several sleep stage classifiers were learned from the resulting sensor data streams segmented into labelled sleep stages of 30 s each. Apart from handcrafted features we experimented with unsupervised feature learning based on the deep learning paradigm. Our best results for correctly classified sleep stages are between 86 and 90% for Wake, REM, N2 and N3, while the best recognition rate for N1 is 37%. Finally, we discuss a preliminary design of the algorithm for determining correlations between daytime behaviour and sleep architecture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The SmartSleep project is funded by the International Bodensee Hochschule. The consortium includes the Universities of Applied Sciences of St. Gallen, of Vorarlberg and of Constance, the Center for Sleep Research and Sleep Medicine at the Swiss Clinic Barmelweid and the two SMEs Biovotion and myVitali.
- 2.
- 3.
- 4.
- 5.
REM corresponds to rapid eye movement sleep, while N1 to N3 correspond to progressively deeper stages of sleep, N1 standing for light sleep, N3 for deep sleep.
- 6.
- 7.
- 8.
References
Prinz, P.N., Vitiello, M.V., Raskind, M.A., Thorpy, M.J.: Sleep disorders and aging. N. Engl. J. Med. 323, 520–526 (1990)
Tibbitts, G.M.: Sleep disorders: causes, effects, and solutions. Prim. Care: Clin. Off. Pract. 35, 817–837 (2008)
Hossain, J.L., Shapiro, C.M.: The prevalence, cost implications, and management of sleep disorders: an overview. Sleep Breath. 6, 085–102 (2002)
Panossian, L.A., Avidan, A.Y.: Review of sleep disorders. Med. Clin. North Am. 93, 407–425 (2009)
Tinguely, G., Landolt, H.P., Cajochen, C.: Schlafgewohnheiten, Schlafqualität und Schlafmittelkonsum der Schweizer Bevölkerung: Ergebnisse aus einer neuen Umfrage bei einer repräsentativen Stichprobe. Ther. Umsch. 71, 637–646 (2014)
Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., Lombardo, C., Riemann, D.: Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J. Affect. Disord. 135, 10–19 (2011)
Behar, J., Roebuck, A., Domingos, J.S., Gederi, E., Clifford, G.D.: A review of current sleep screening applications for smartphones. Physiol. Meas. 34, R29 (2013)
Reimer, U., Emmenegger, S., Maier, E., Zhang, Z., Khatami, R.: Recognizing sleep stages with wearable sensors in everyday settings. In: Proceedings 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE) (2017)
Reimer, U., Maier, E., Laurenzi, E., Ulmer, T.: Mobile stress recognition and relaxation support with SmartCoping: user adaptive interpretation of physiological stress parameters. In: Proceedings of the Hawaii International Conference on System Sciences (HICSS-50) (2017)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newslett. 12, 74–82 (2011)
Alsheikh, M.A., Selim, A., Niyato, D., Doyle, L., Lin, S., Tan, H.P.: Deep activity recognition models with triaxial accelerometers. arXiv:1511.04664 (2015)
Huỳnh, T., Blanke, U., Schiele, B.: Scalable recognition of daily activities with wearable sensors. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 50–67. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75160-1_4
Huỳnh, T., Fritz, M., Schiele, B.: Discovery of activity patterns using topic models. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 10–19. ACM (2008)
Blanke, U., Schiele, B.: Daily routine recognition through activity spotting. In: Choudhury, T., Quigley, A., Strang, T., Suginuma, K. (eds.) LoCA 2009. LNCS, vol. 5561, pp. 192–206. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01721-6_12
Yan, Z., Chakraborty, D., Misra, A., Jeung, H., Aberer, K.: Sammple: Detecting semantic indoor activities in practical settings using locomotive signatures. In: Proceedings 16th International Symposium on Wearable Computers, 37–40. IEEE (2012)
Garcia-Ceja, E., Brena, R.: Long-term activity recognition from accelerometer data. Procedia Technol. 7, 248–256 (2013)
Okeyo, G., Chen, L., Wang, H.: Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes. Future Gener. Comput. Syst. 39, 29–43 (2014)
Sohm, M.: Erkennung von komplexen Aktivitäten anhand von tragbaren Sensoren. Master thesis, University of Applied Sciences, Vorarlberg (2016)
Intille, S.S., Bao, L., Tapia, E.M., Rondoni, J.: Acquiring in situ training data for context-aware ubiquitous computing applications. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1–8. ACM (2004)
Längkvist, M., Karlsson, L., Loutfi, A.: Sleep stage classification using unsupervised feature learning. Adv. Artif. Neural Syst. 2012, 1–9 (2012)
Herrera, L.J., Fernandes, C.M., Mora, A.M., Migotina, D., Largo, R., Guillén, A., Rosa, A.C.: Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification. Int. J. Neural Syst. 23, 1350012 (2013)
Shi, J., Liu, X., Li, Y., Zhang, Q., Li, Y., Ying, S.: Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning. J. Neurosci. Methods 254, 94–101 (2015)
Gu, W., Yang, Z., Shangguan, L., Sun, W., Jin, K., Liu, Y.: Intelligent sleep stage mining service with smartphones. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 649–660 (2014)
Rahman, T., Adams, A.T., Ravichandran, R.V., Zhang, M., Patel, S.N., Kientz, J.A., Choudhury, T.: Dopplesleep: a contactless unobtrusive sleep sensing system using short-range doppler radar. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 39–50 (2015)
Kurihara, Y., Watanabe, K.: Sleep-stage decision algorithm by using heartbeat and body-movement signals. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Humans 42, 1450–1459 (2012)
O’Hare, E., Flanagan, D., Penzel, T., Garcia, C., Frohberg, D., Heneghan, C.: A comparison of radio-frequency biomotion sensors and actigraphy versus polysomnography for the assessment of sleep in normal subjects. Sleep Breath. 19, 91–98 (2015)
Kolla, B.P., Mansukhani, S., Mansukhani, M.P.: Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Rev. Med. Devices 13, 497–506 (2016)
Radha, M., Garcia-Molina, G., Poel, M., Tononi, G.: Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal. In: Proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1876–1880 (2014)
Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-Oriented Software Architecture: A System of Patterns. Wiley, Hoboken (2013)
Panagiotou, C., Samaras, I., Gialelis, J., Chondros, P., Karadimas, D.: A comparative study between SVM and fuzzy inference system for the automatic prediction of sleep stages and the assessment of sleep quality. In: Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare, pp. 293–296 (2015)
Bengio, Y., Courville, A.C., Vincent, P.: Unsupervised feature learning and deep learning: a review and new perspectives. CoRR abs/1206.5538 (2012)
Keyvanrad, M.A., Homayounpour, M.M.: A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet). Technical report, Laboratory for Intelligent Multimedia Processing, Computer Engineering and Information Technology Department, Amirkabir University of Technology, Tehran, Iran (2014)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14, 1771–1800 (2002)
Carreira-Perpinan, M.A., Hinton, G.E.: On contrastive divergence learning. In: Artificial Intelligence and Statistics 2005 (2005)
Hinton, G.E.: A practical guide to training Restricted Boltzmann Machines. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 599–619. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_32
Borazio, M., Berlin, E., Kücükyildiz, N., Scholl, P., Laerhoven, K.V.: Towards benchmarked sleep detection with wrist-worn sensing units. In: 2014 IEEE International Conference on Healthcare Informatics, pp. 125–134 (2014)
Biswal, S., Kulas, J., Sun, H., Goparaju, B., Westover, M.B., Bianchi, M.T., Sun, J.: Sleepnet: Automated sleep staging system via deep learning. arXiv:1707.08262 (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Ohayon, M.M., Carskadon, M.A., Guilleminault, C., Vitiello, M.V.: Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 27, 1255–1273 (2004)
Danker-Hopfe, H., Anderer, P., Zeitlhofer, J., Boeck, M., Dorn, H., Gruber, G., Heller, E., Loretz, E., Moser, D., Parapatics, S., et al.: Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. J. Sleep Res. 18, 74–84 (2009)
Reimer, U., Maier, E., Ulmer, T.: A Self-learning Application Framework for Behavioral Change Support. In: Röcker, C., O’Donoghue, J., Ziefle, M., Helfert, M., Molloy, W. (eds.) ICT4AWE 2016. CCIS, vol. 736, pp. 119–139. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62704-5_8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Reimer, U. et al. (2018). Laying the Foundation for Correlating Daytime Behaviour with Sleep Architecture Using Wearable Sensors. In: Röcker, C., O’Donoghue, J., Ziefle, M., Maciaszek, L., Molloy, W. (eds) Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE 2017. Communications in Computer and Information Science, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-319-93644-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-93644-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93643-7
Online ISBN: 978-3-319-93644-4
eBook Packages: Computer ScienceComputer Science (R0)