Abstract
In this article we present assumptions for development of novel model of active system that can assist elder and disabled people. In the following sections we discuss literature and propose a structure of decision support and data processing on levels: voice and speech processing, image processing based on proposed descriptors, routing and positioning. For these aspects pros and cons that can be faced in the development process are described with potential preventive actions.
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References
Aggarwal, J., Ryoo, M.: Human activity analysis: a review. ACM Comput. Surv. 43, 1–43 (2011)
Aggarwal, J., Xia, L.: Human activity recognition from 3D data: a review. Pattern Recogn. Lett. 48, 70–80 (2014)
Ainsworth, B., Haskell, W., Herrmann, S., Meckeset, N.: Compendium of physical activities: a second update of codes and MET values. Med. Sci. Sports Exerc. 43(8), 1575–1581 (2011)
Arif, M., Kattan, A.: Physical activities monitoring using wearable acceleration sensors attached to the body. PLoS ONE 10(7), e0130851 (2015)
Atallah, L., Lo, B., King, R., Yang, G.: Sensor positioning for activity recognition using wearable accelerometers. IEEE Trans. Biomed. Circ. Syst. 5, 320–329 (2011)
Brociek, R., Słota, D.: Reconstruction of the boundary condition for the heat conduction equation of fractional order. Therm. Sci. 19, 35–42 (2015)
Brociek, R., Słota, D.: Application of intelligent algorithm to solve the fractional heat conduction inverse problem. Commun. Comput. Inf. Sci. 538, 356–365 (2015)
Budnikas, G.: A model for an aggression discovery through person online behavior. In: Saeed, K., Homenda, W. (eds.) CISIM 2015. LNCS, vol. 9339, pp. 305–315. Springer, Heidelberg (2015)
Capela, N., Lemaire, E., Baddour, N.: Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. PLoS ONE 10(4), e0124414 (2015)
Cheung, V., Gray, L., Karunanithi, M.: Review of accelerometry for determining daily activity among elderly patients. Arch. Phys. Med. Rehabil. 92, 998–1014 (2011)
Cpałka, K., Łapa, K., Przybył, A.: A new approach to design of control systems using genetic programming. Inf. Technol. Control 44(4), 433–442 (2015)
Damaševičius, R., Stuikys, V., Toldinas, J.: Domain ontology-based generative component design using feature diagrams and meta-programming technique. In: Proceedings of 2nd European Conference on Software Architecture ECSA 2008, pp. 338–341 (2008)
Drosou, A., Ioannidis, D., Moustakas, K., Tzovaras, D.: Spatiotemporal analysis of human activities for biometric authentication. Comput. Vis. Image Underst. 116(3), 411–421 (2012)
Ferdowsi, S., Voloshynovskiy, S., Kostadinov, D., Korytkowski, M., Scherer, R.: Secure representation of images using multi-layer compression. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 696–705. Springer, Heidelberg (2015)
Fleury, A., Noury, N., Vacher, M.: Improving supervised classification of activities of daily living using prior knowledge. Int. J. E-Health Med. Commun. 2(1), 17–34 (2011)
Govindaraju, V.: A generative framework to investigate the underlying patterns in human activities. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1472–1479 (2011)
Gupta, P., Dallas, T.: Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans. Biomed. Eng. 61(6), 1780–1786 (2014)
Hoque, E., Stankovic, J.: AALO: activity recognition in smart homes using Active Learning in the presence of Overlapped activities. In: PervasiveHealth, pp. 139–146 (2012)
Incel, O., Kose, M., Ersoy, C.: A review and taxonomy of activity recognition on mobile phones. BioNanoScience 3, 145–171 (2013)
Jain, A., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14, 4–20 (2004)
Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)
Lara, O., Labrador, M.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15, 1192–1209 (2013)
Lee, D., Yang, M., Oh, S.: Fast and accurate head pose estimation via random projection forests. In: Proceedings of International Conference on Computer Vision (ICCV 2015), pp. 1958–1966 (2015)
Martišius, I., Damaševičius, R.: A prototype SSVEP based real time BCI gaming system. Comput. Intell. Neurosci. 2016 (2016)
Napoli, C., Pappalardo, G., Tramontana, E.: A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent. Appl. Math. Comput. Sci. 26(1), 147–160 (2016)
Napoli, C., Pappalardo, G., Tramontana, E., Zappalà, G.: A cloud-distributed GPU architecture for pattern identification in segmented detectors big-data surveys. Comput. J. 59(3), 338–352 (2016)
Okulewicz, M., Mandziuk, J.: Two-phase multi-swarm PSO and the dynamic vehicle routing problem. In: Proceedings of the IEEE Symposium Series on Computational Intelligence, pp. 86–93 (2014)
Osmani, V., Balasubramaniam, S., Botvich, D.: Human activity recognition in pervasive health-care: supporting efficient remote collaboration. J. Netw. Comput. Appl. 31(4), 628–655 (2008)
Özdemir, A., Barshan, B.: Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6), 10691–10708 (2014)
Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. J. 28, 976–990 (2010)
Pei, L., Guinness, R., Chen, R., Liu, J.: Human behavior cognition using smartphone sensors. Sensors 13, 1402–1424 (2013)
Shoaib, M., Bosch, S., Incel, O., Scholten, H., Havinga, P.: A survey of online activity recognition using mobile phones. Sensors 15(1), 2059–2085 (2015)
Stateczny, A., Wlodarczyk-Sielicka, M.: Self-organizing artificial neural networks into hydrographic big data reduction process. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 335–342. Springer, Heidelberg (2014)
Suriani, N., Hussain, A., Zulkifley, M.: Sudden event recognition: a survey. Sensors 13(8), 9966–9998 (2013)
Turaga, P., Chellappa, R., Subrahmanian, V., Udrea, O.: Machine recognition of human activities: a survey. IEEE Trans. Circ. Syst. Video Technol. 18, 1473–1488 (2008)
Waledzik, K., Mandziuk, J.: An automatically generated evaluation function in general game playing. IEEE Trans. Comput. Intell. AI Games 6(3), 258–270 (2014)
Wlodarczyk-Sielicka, M., Stateczny, A.: Selection of SOM parameters for the needs of clusterisation of data obtained by interferometric methods. In: Proceedings of 16th International Radar Symposium, Dresden, pp. 1129–1134 (2015)
Ziaeefard, M., Bergevin, R.: Semantic human activity recognition: a literature review. Pattern Recogn. 48(8), 2329–2345 (2015)
Zhu, C., Sheng, W.: Motion- and location-based online human daily activity recognition. Pervasive Mob. Comput. 7(2), 256–269 (2011)
Yampolskiy, R., Govindaraju, V.: Behavioural biometrics: a survey and classification. Int. J. Biometrics 1(1), 81–113 (2008)
Yu, H., Spenko, M., Dubowsky, S.: An adaptive shared control system for an intelligent mobility aid for the elderly. Auton. Robots 15, 53–66 (2003)
Kwapisz, J.R., Weiss, G., Moore, S.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12(2), 74–82 (2011)
Mannini, A., Sabatini, A.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10, 1154–1175 (2010)
Mathie, M., Celler, B., Lovell, N., Coster, A.: Classification of basic daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. 42, 679–687 (2004)
Miluzzo, E., Lane, N., Fodor, K., Peterson, R.: Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys 2008, pp. 337–350 (2008)
Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J.: Activity classification using realistic data from wearable sensors. IEEE Trans. Inf. Technol. Biomed. 10, 119–128 (2006)
Siirtola, P., Roning, J.: Recognizing human activities user-independently on smartphones based on accelerometer data. Int. J. Interact. Multimedia Artif. Intell. 1(5), 38–45 (2012)
Sohn, T., Varshavsky, A., LaMarca, A., Chen, M.Y., Choudhury, T., Smith, I., Consolvo, S., Hightower, J., Griswold, W.G., de Lara, E.: Mobility detection using everyday GSM traces. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 212–224. Springer, Heidelberg (2006)
Tran, D., Sorokin, A.: Human activity recognition with metric learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 548–561. Springer, Heidelberg (2008)
Yang, J.: Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics, IMCE 2009, pp. 1–10 (2009)
Cpalka, K., Zalasinski, M., Rutkowski, L.: A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. Appl. Soft Comput. 43, 47–56 (2016)
Cpalka, K., Zalasinski, M.: On-line signature verification using vertical signature partitioning. Expert Syst. Appl. 41(9), 4170–4180 (2014)
Zhang, M., Sawchuk, A.: USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 1036–1043 (2012)
Acknowledgments
Authors acknowledge contribution to this project of Operational Programme: “Knowledge, Education, Development” financed by the European Social Fund under grant application POWR.03.03.00-00-P001/15, contract no. MNiSW/2016/DIR/208/NN.
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Połap, D., Woźniak, M. (2016). Introduction to the Model of the Active Assistance System for Elder and Disabled People. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-46254-7_31
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DOI: https://doi.org/10.1007/978-3-319-46254-7_31
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