Seite - 35 - in Intelligent Environments 2019 - Workshop Proceedings of the 15th International Conference on Intelligent Environments

accuracy to predict sleep stages with time series machine learning models, where data has been transformed into data windows. The advantages of these techniques are that not only can sleep tracking be made with accelerometer and environment sensor but we can also estimate how active the person was during the day using trained models and 30 min sample data. In future work the team will continue this study and include different test subjects sleeping in the same environment to adjust machine learning models to different people in the same environment. The team is also interested in predicting the activity of test subjects from a daily routine such as sports and sedentarism from the analysis of our multisensory system. References [1] S. Herculano-Houzel, "Sleep it out," Science, 342(6156), pp. 316-317, 2013. [2] A. D. Krystal and J. D. Edinger, "Measuring sleep quality," Sleep medicine, 9, pp. S10-S17, 2008. [3] M. A. Carskadon and W. C. Dement, "Normal human sleep: an overview," Principles and practice of sleep medicine, 4, pp. 13-23, 2005. [4] D. J. Buysse, "Sleep health: can we define it? Does it matter?," Sleep, 37(1), pp. 9-17, 2014. [5] Z. Beattie, Y. Oyang, A. Statan, A. Ghoreyshi, A. Pantelopoulos, A. Russell and C. Heneghan, "Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals," Physiological Measurement, vol. 38(11), pp. 1968-1979, 2017. [6] S. Roomkham, D. Lovell, J. Cheung and D. Perrin, "Promises and challenges in the use of consumer- grade devices for sleep monitoring," IEEE reviews in biomedical engineering, 11, pp. 53-67, 2018. [7] K. Russo, B. Goparaju and M. T. Bianchi, "Consumer sleep monitors: is there a baby in the bathwater?," Nature and science of sleep, 7, pp. 147-157, 2015. [8] Z. Liang and M. Martell, "Validity of consumer activity wristbands and wearable EEG for measuring overall sleep parameters and sleep structure in free-living conditions," Journal of Healthcare Informatics Research, 2(1-2), pp. 152-178, 2018. [9] T. Åkerstedt, K. Hume, D. Minors and J. Waterhouse, "Good sleep - its timing and physiological sleep characteristics," Journal of Sleep Research, vol. 6(4), pp. 221-229, 1997. [10] M. Kay, E. K. Choe, J. Shepherd, B. Greenstein, N. Watson, S. Consolvo and J. A. Kientz, "Lullaby: a capture & access system for understanding the sleep environment," in Proceedings of the 2012 ACM conference on ubiquitous computing, 2012. [11] Z. Liang, B. Ploderer, W. Liu, Y. Nagata, J. Bailey, L. Kulik and Y. Li, "SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors," Personal and Ubiquitous Computing, 20(6), pp. 985-1000, 2016. [12] A. Sena, Cérebro, Saúde e Sociedade, Lisboa: Lidel - Edições Técnicas, Lda., 2016. [13] M. Borazio and K. Van Laerhoven, "Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies," in In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 2012. [14] C. Clifton and B. Thuraisingham, "Emerging standards for data mining," Computer Standards & Interfaces, vol. 23(3), pp. 187-193, 2001. [15] M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Kotter, T. Meinl, P. Ohl, K. Thiel and B. Wiswedel, "KNIME-the Konstanz information miner: version 2.0 and beyond," ACM SIGKDD Explorations Newsletter, vol. 11(1), pp. 26-31, 2009. C.Gonçalvesetal. /MultisensorMonitoringSystem toEstablishCorrelations 35