Abstract
During the last 5 years, research on Human Activity Recognition (HAR) has reported on systems showing good overall recognition performance. As a consequence, HAR has been considered as a potential technology for e-health systems. Here, we propose a machine learning based HAR classifier. We also provide a full experimental description that contains the HAR wearable devices setup and a public domain dataset comprising 165,633 samples. We consider 5 activity classes, gathered from 4 subjects wearing accelerometers mounted on their waist, left thigh, right arm, and right ankle. As basic input features to our classifier we use 12 attributes derived from a time window of 150ms. Finally, the classifier uses a committee AdaBoost that combines ten Decision Trees. The observed classifier accuracy is 99.4%.
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References
Yu-Jin, H., Ig-Jae, K., Sang Chul, A., Hyoung-Gon, K.: Activity Recognition Using Wearable Sensors for Elder Care. In: Proceedings of Second International Conference on Future Generation Communication and Networking, FGCN 2008, vol. 2, pp. 302–305 (2008), doi:10.1109/FGCN.2008.165
Gjoreski, H., Lustrek, M., Gams, M.: Accelerometer Placement for Posture Recognition and Fall Detection. In: 7th International Conference on Intelligent Environments, IE (2011)
Lei, G., Bourke, A.K., Nelson, J.: A system for activity recognition using multi-sensor fusion. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC (2011)
Salzberg, S.L.: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc. (1993) Machine Learning 16(3), 235–240 (1994) ISSN: 0885-6125, doi: 10.1007/bf00993309
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Liu, S., Gao, R.X., John, D., Staudenmayer, J.W., Freedson, P.S.: Multisensor Data Fusion for Physical Activity Assessment. IEEE Transactions on Biomedical Engineering 59(3), 687–696 (2012) ISSN: 0018-9294
Yuting, Z., Markovic, S., Sapir, I., Wagenaar, R.C., Little, T.D.C.: Continuous functional activity monitoring based on wearable tri-axial accelerometer and gyroscope. In: 5th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth (2011)
Sazonov, E.S., et al.: Monitoring of Posture Allocations and Activities by a Shoe-Based Wearable Sensor. IEEE Transactions on Biomedical Engineering 58(4), 983–990 (2011)
Reiss, A., Stricker, D.: Introducing a modular activity monitoring system. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC (2011)
Min, X., Goldfain, A., Chowdhury, A.R., DelloStritto, J.: Towards accelerometry based static posture identification. In: IEEE Consumer Communications and Networking Conference, CCNC (2011)
Maekawa, T., Watanabe, S.: Unsupervised Activity Recognition with User’s Physical Characteristics Data. In: 15th Annual International Symposium on Wearable Computers, ISWC (2011)
Martin, H., Bernardos, A.M., Tarrio, P., Casar, J.R.: Enhancing activity recognition by fusing inertial and biometric information. In: Proceedings of the 14th International Conference on Information Fusion, FUSION (2011)
Alvarez-Alvarez, A., Trivino, G., Cordon, O.: Body posture recognition by means of a genetic fuzzy finite state machine. In: IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS (2011)
Jun-Ki, M., Sung-Bae, C.: Activity recognition based on wearable sensors using selection/fusion hybrid ensemble. In: IEEE International Conference on Systems, Man, and Cybernetics, SMC (2011)
Ioana-Iuliana, F., Rodica-Elena, D.: Detection of daily movements from data collected with two tri-axial accelerometers. In: 34th International Conference on Telecommunications and Signal Processing, TSP (2011)
Feng, W., Meiling, W., Nan, F.: Research on Classification of Human Daily Activities Based on a Single Tri-Axial Accelerometer. In: 1st International Workshop on Complexity and Data Mining, IWCDM (2011)
Czabke, A., Marsch, S., Lueth, T.C.: Accelerometer based real-time activity analysis on a microcontroller. In: 5th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth (2011)
Chernbumroong, S., Atkins, A.S., Hongnian, Y.: Activity classification using a single wrist-worn accelerometer. In: 5th International Conference on Software, Knowledge Information, Industrial Management and Applications, SKIMA (2011)
Bayati, H., Millan, J.d.R., Chavarriaga, R.: Unsupervised Adaptation to On-body Sensor Displacement in Acceleration-Based Activity Recognition. In: 15th Annual International Symposium on Wearable Computers, ISWC (2011)
Atallah, L., et al.: Sensor Positioning for Activity Recognition Using Wearable Accelerometers. IEEE Transactions on Biomedical Circuits and Systems 5(4), 320–329 (2011)
Andreu, J., Baruah, R.D., Angelov, P.: Real time recognition of human activities from wearable sensors by evolving classifiers. In: IEEE International Conference on Fuzzy Systems, FUZZ (2011)
Xu, S., Kashima, H., Tomioka, R., Ueda, N., Ping, L.: A New Multi-task Learning Method for Personalized Activity Recognition. In: IEEE 11th International Conference on Data Mining, ICDM (2011)
Yang, X., Lianwen, J.: A naturalistic 3D acceleration-based activity dataset & benchmark evaluations. In: IEEE International Conference on Systems Man and Cybernetics (SMC), pp. 4081–4085 (2010) ISSN: 1062-922X
Maziewski, P., Kupryjanow, A., Kaszuba, K., Czyzewski, A.: Accelerometer signal pre-processing influence on human activity recognition. In: Conference Proceedings of Signal Processing Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 95–99 (2009) ISSN:978-83-62065-06-6
Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand (April 1999)
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Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidiú, R., Fuks, H. (2012). Wearable Computing: Accelerometers’ Data Classification of Body Postures and Movements. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_6
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DOI: https://doi.org/10.1007/978-3-642-34459-6_6
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