Abstract
Objective measurements of physical behaviour are an interesting research field from the public health and computer science perspective. While for public health research, measurements with a high quality and feasible setup is important, the analysis of and reasoning about the data is what we will present in this work. Our focus in this work is the comprehensive representation of physical behaviour throughout consecutive days and allowing to find subgroups in the population with similar physical activity levels.
We have a unique data set of 4628 participants wearing tri-axial accelerometers for six days and will present a case-based reasoning (CBR) system that can find and compare similar activity profiles. In this work, we focus on creating a CBR model using myCBR and do initial experiments with the resulting system. We will introduce a data-driven approach for modelling local similarity measures. Eventually, in the experiments we will show that for the given data set, the CBR system outperforms a k-Nearest Neighbor regressor in finding most similar participants.
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Since the study is ongoing, we have used the data available by March, 12 2018.
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References
A, A., MH, F., MB, R.: Health effects of overweight and obesity in 195 countries over 25 years. New England Journal of Medicine 377(1), 13–27 (2017), pMID: 28604169
Abdel-Aziz, A., Strickert, M., Hüllermeier, E.: Learning solution similarity in preference-based CBR. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS (LNAI), vol. 8765, pp. 17–31. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11209-1_3
Aitchison, J., Egozcue, J.J.: Compositional data analysis: where are we and where should we be heading? Math. Geol. 37(7), 829–850 (2005)
Arif, M., Kattan, A.: Physical activities monitoring using wearable acceleration sensors attached to the body. PLOS ONE 10(7), 1–16 (2015)
Bach, K., Althoff, K.-D.: Developing case-based reasoning applications using myCBR 3. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS (LNAI), vol. 7466, pp. 17–31. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32986-9_4
Bergmann, R., Kolodner, J., Plaza, E.: Representation in case-based reasoning. Knowl. Eng. Rev. 20(03), 209 (2005)
Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46(3), 1–33 (2014)
Campillo-Gimenez, B., Jouini, W., Bayat, S., Cuggia, M.: Improving case-based reasoning systems by combining k-nearest neighbour algorithm with logistic regression in the prediction of patients’ registration on the renal transplant waiting list. PLoS ONE 8(9), e71991 (2013)
Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Multi-level interactive medical process mining. In: ten Teije, A., Popow, C., Holmes, J.H., Sacchi, L. (eds.) AIME 2017. LNCS (LNAI), vol. 10259, pp. 256–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59758-4_28
Dumuid, D., et al.: The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat. Methods Med. Res. (2017)
Gabel, T., Godehardt, E.: Top-down induction of similarity measures using similarity clouds. In: Hüllermeier, E., Minor, M. (eds.) ICCBR 2015. LNCS (LNAI), vol. 9343, pp. 149–164. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24586-7_11
El-Sappagh, H., El-Sappagh, S., Elmogy, M.: Case representation and indexing. In: Foundations of Soft Case-Based Reasoning, pp. 34–74 (2004)
Howie, E.K., Smith, A.L., Mcveigh, J.A., Straker, L.M.: Accelerometer-derived activity phenotypes in young adults: a latent class analysis. Int. J. Behav. Med. (2018)
Hüllermeier, E., Schlegel, P.: Preference-based CBR: first steps toward a methodological framework. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS (LNAI), vol. 6880, pp. 77–91. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23291-6_8
Khamparia, A., Pandey, B.: A novel method of case representation and retrieval in CBR for e-learning. Educ. Inf. Technol. 22(1), 337–354 (2017)
Kohl, H.W., et al.: The pandemic of physical inactivity: global action for public health. Lancet 380(9838), 294–305 (2012)
Lagersted-Olsen, J., et al.: Comparison of objectively measured and self-reported time spent sitting. Int. J. Sport. Med. 35(06), 534–540 (2013)
Lee, I.M., Shiroma, E.J.: Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br. J. Sport. Med. 48(3), 197–201 (2013)
Lee, I.M., Shiroma, E.J., Lobelo, F., Puska, P., Blair, S.N., Katzmarzyk, P.T.: Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380(9838), 219–229 (2012)
Li, X.: Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLOS Biol. 15(1), e2001402 (2017)
Marschollek, M.: A semi-quantitative method to denote generic physical activity phenotypes from long-term accelerometer data - the atlas index. PLoS ONE 8(5), e63522 (2013)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Plis, K., Bunescu, R.C., Marling, C.R., Shubrook, J., Schwartz, F.: A machine learning approach to predicting blood glucose levels for diabetes management. In: AAAI Workshop: Modern Artificial Intelligence for Health Analytics (2014)
Prince, S.A., Adamo, K.B., Hamel, M., Hardt, J., Gorber, S.C., Tremblay, M.: A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int. J. Behav. Nutr. Phys. Act. 5(1), 56 (2008)
Raitakan, O.T., Porkka, K.V.K., Taimela, S., Telama, R., Räsänen, L., Vllkari, J.S.: Effects of persistent physical activity and inactivity on coronary risk factors in children and young adults the cardiovascular risk in young finns study. Am. J. Epidemiol. 140(3), 195–205 (1994)
Richter, M.M.: The knowledge contained in similarity measures. In: Veloso, M.M., Aamodt, A. (eds.) Case-Based Reasoning Research and Development, Proceedings of the First International Conference, ICCBR 1995. LNCS, vol. 1010. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60598-3
Sani, S., Wiratunga, N., Massie, S., Cooper, K.: kNN sampling for personalised human activity recognition. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 330–344. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61030-6_23
Smyth, B., Cunningham, P.: Running with cases: a CBR approach to running your best marathon. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 360–374. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61030-6_25
Stahl, A., Roth-Berghofer, T.R.: Rapid prototyping of CBR applications with the open source tool myCBR. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 615–629. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85502-6_42
Uddin, M., Loutfi, A.: Physical activity identification using supervised machine learning and based on pulse rate. Int. J. Adv. Comput. Sci. Appl. 4(7), 210–217 (2013)
Wen, C.P., Wu, X.: Stressing harms of physical inactivity to promote exercise. Lancet 380(9838), 192–193 (2012)
Willetts, M., Hollowell, S., Aslett, L., Holmes, C., Doherty, A.: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 uk biobank participants. BioRxiv (2018)
Yao, B., Li, S.: Anmm4cbr: a case-based reasoning method for gene expression data classification. Algorithms Mol. Biol. 5(1), 14 (2010)
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Verma, D., Bach, K., Mork, P.J. (2018). Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_28
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