Abordagem Atividade-Intensidade para o Reconhecimento de Atividades
Resumo
No monitoramento em saúde pervasivo, uma aplicação essencial no acompanhamento do dia a dia da pessoa é o reconhecimento de atividades. Apesar dos diversos estudos sobre esse tema, um parâmetro pouco considerado é o reconhecimento da intensidade. Neste trabalho, propomos o acoplamento da atividade com a intensidade, a qual denominamos Atividade-Intensidade, em dados obtidos de acelerômetros, para melhor descrever as atividades diárias de um paciente. Adicionalmente, investigamos iniciativas de Lógica Fuzzy no reconhecimento de atividades. Os resultados mostraram a viabilidade da classificação e o bom desempenho do reconhecimento Atividade-Intensidade.
Referências
Ainsworth, B. E. et al. (2000). “Compendium of physical activities: an update of activity codes and MET intensities”. Medicine and science in sports and exercise, 32(9; SUPP/1), S498-S504.
Albinali, F., Goodwin, M. S. and Intille, S. S. (2009) “Recognizing stereotypical motor movements in the laboratory and classroom: a case study with children on the autism spectrum”, Ubiquitous Computing, pp. 71-80.
Bulling, A., Blanke, U., & Schiele, B. (2014). “A tutorial on human activity recognition using body-worn inertial sensors”. ACM Computing Surveys (CSUR), 46(3), 33.
bin Abdullah, M. F. A., Negara, A. F. P., Sayeed, M. S., Choi, D. J., & Muthu, K. S. (2012). Classification algorithms in human activity recognition using smartphones. International Journal of Computer and Information Engineering, 6, 77-84.
Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public health reports, 100(2), 126.
Chiang, S. Y., Kan, Y. C., Tu, Y. C. and Lin, H. C. (2012) “Activity Recognition by Fuzzy Logic System in Wireless Sensor Network for Physical Therapy”, Intelligent Decision Technologies, vol. 2, no. 16, pp. 191-200.
Copetti, A., Leite, J. C. B., Loques, O., da Nóbrega, A. C. L. and Barbosa, T. P. (2009) “Chequer. Intelligent Context-Aware Monitoring of Hypertensive Patients.” In: Situation Recognition and Medical Data Analysis in Pervasive Health Environments. Pervasive Healthcare Conference, Londres, Reino Unido.
Copetti, A., Leite, J. C. B. and Loques, O. (2013) “A Decision-making Mechanism for Context Inference in Pervasive Healthcare Environments”, Decision Support Systems, 55(2): 528-537.
Gjoreski, H. et al. (2015). Competitive Live Evaluations of Activity-Recognition Systems. Pervasive Computing, IEEE, 14(1), 70-77.
Helmi, M. and AlModarresi, S.M.T. (2009) “Human Activity Recognition Using a Fuzzy Inference System”. FUZZ-IEEE. Korea.
Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., Lach, J. and Zhou, G. (2009) “Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information”, Wearable and Implantable Body Sensor Networks, pp. 138-143.
Liu, S.H. and Chang, Y.J. (2009) “Using accelerometers for physical actions recognition by a neural fuzzy network”, Telemedicine and e-Health, 15(9): 867-876.
Melanson Jr, E. L., Freedson, P. S., & Blair, S. (1996). “Physical activity assessment: a review of methods”. Critical Reviews in Food Science & Nutrition, 36(5), 385-396.
Palmerini, L., Rocchi, L., Mellone, S., Valzania, F., & Chiari, L. (2011). “Feature selection for accelerometer-based posture analysis in Parkinson's disease”. Information Technology in Biomedicine, IEEE Transactions on, 15(3), 481-490.
Pärkkä, J. et al. (2009) “Relationship of psychological and physiological variables in longterm self-monitored data during work ability rehabilitation program”, Information Technology in Biomedicine, 13(2): 141-151.
Reiss, A., & Stricker, D. (2012). “Introducing a new benchmarked dataset for activity monitoring”. In Symposium on Wearable Computers (ISWC), (pp. 108-109). IEEE.
Reiss, A. (2014). “Personalized mobile physical activity monitoring for everyday life” (Tese de Doutorado, Technical University of Kaiserslautern).
Ribeiro Filho, J. D. P., Silva, F. J. S., Coutinho, L. R. and Gomes, B. T. P. (2015) “MHARS: Sistema Móvel de Reconhecimento de Atividades em Ambient Assisted Living”, In: Simpósio Brasileiro de Computação Ubíqua e Pervasiva- SBCUP, Recife.
Ribeiro Filho, J. D. P., Silva, F. J. S., Coutinho, L. R., Gomes, B. T. P. and Endler, M. (2016) “A Movement Activity Recognition Pervasive System for Patient Monitoring in Ambient Assisted Living”, 31st ACM Symposium On Applied Computing (SAC), Health Track.
Yang, S. I. and Cho, S. B. (2008) “Recognizing human activities from accelerometer and physiological sensors” IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Seoul, Korea.
Albinali, F., Goodwin, M. S. and Intille, S. S. (2009) “Recognizing stereotypical motor movements in the laboratory and classroom: a case study with children on the autism spectrum”, Ubiquitous Computing, pp. 71-80.
Bulling, A., Blanke, U., & Schiele, B. (2014). “A tutorial on human activity recognition using body-worn inertial sensors”. ACM Computing Surveys (CSUR), 46(3), 33.
bin Abdullah, M. F. A., Negara, A. F. P., Sayeed, M. S., Choi, D. J., & Muthu, K. S. (2012). Classification algorithms in human activity recognition using smartphones. International Journal of Computer and Information Engineering, 6, 77-84.
Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public health reports, 100(2), 126.
Chiang, S. Y., Kan, Y. C., Tu, Y. C. and Lin, H. C. (2012) “Activity Recognition by Fuzzy Logic System in Wireless Sensor Network for Physical Therapy”, Intelligent Decision Technologies, vol. 2, no. 16, pp. 191-200.
Copetti, A., Leite, J. C. B., Loques, O., da Nóbrega, A. C. L. and Barbosa, T. P. (2009) “Chequer. Intelligent Context-Aware Monitoring of Hypertensive Patients.” In: Situation Recognition and Medical Data Analysis in Pervasive Health Environments. Pervasive Healthcare Conference, Londres, Reino Unido.
Copetti, A., Leite, J. C. B. and Loques, O. (2013) “A Decision-making Mechanism for Context Inference in Pervasive Healthcare Environments”, Decision Support Systems, 55(2): 528-537.
Gjoreski, H. et al. (2015). Competitive Live Evaluations of Activity-Recognition Systems. Pervasive Computing, IEEE, 14(1), 70-77.
Helmi, M. and AlModarresi, S.M.T. (2009) “Human Activity Recognition Using a Fuzzy Inference System”. FUZZ-IEEE. Korea.
Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., Lach, J. and Zhou, G. (2009) “Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information”, Wearable and Implantable Body Sensor Networks, pp. 138-143.
Liu, S.H. and Chang, Y.J. (2009) “Using accelerometers for physical actions recognition by a neural fuzzy network”, Telemedicine and e-Health, 15(9): 867-876.
Melanson Jr, E. L., Freedson, P. S., & Blair, S. (1996). “Physical activity assessment: a review of methods”. Critical Reviews in Food Science & Nutrition, 36(5), 385-396.
Palmerini, L., Rocchi, L., Mellone, S., Valzania, F., & Chiari, L. (2011). “Feature selection for accelerometer-based posture analysis in Parkinson's disease”. Information Technology in Biomedicine, IEEE Transactions on, 15(3), 481-490.
Pärkkä, J. et al. (2009) “Relationship of psychological and physiological variables in longterm self-monitored data during work ability rehabilitation program”, Information Technology in Biomedicine, 13(2): 141-151.
Reiss, A., & Stricker, D. (2012). “Introducing a new benchmarked dataset for activity monitoring”. In Symposium on Wearable Computers (ISWC), (pp. 108-109). IEEE.
Reiss, A. (2014). “Personalized mobile physical activity monitoring for everyday life” (Tese de Doutorado, Technical University of Kaiserslautern).
Ribeiro Filho, J. D. P., Silva, F. J. S., Coutinho, L. R. and Gomes, B. T. P. (2015) “MHARS: Sistema Móvel de Reconhecimento de Atividades em Ambient Assisted Living”, In: Simpósio Brasileiro de Computação Ubíqua e Pervasiva- SBCUP, Recife.
Ribeiro Filho, J. D. P., Silva, F. J. S., Coutinho, L. R., Gomes, B. T. P. and Endler, M. (2016) “A Movement Activity Recognition Pervasive System for Patient Monitoring in Ambient Assisted Living”, 31st ACM Symposium On Applied Computing (SAC), Health Track.
Yang, S. I. and Cho, S. B. (2008) “Recognizing human activities from accelerometer and physiological sensors” IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Seoul, Korea.
Publicado
04/07/2016
Como Citar
GOMES, Eduardo; TORRES, Natália Melgaço Pereira; MOCAIBER, Izabela; ARTIGAS, Danilo; COPETTI, Alessandro.
Abordagem Atividade-Intensidade para o Reconhecimento de Atividades. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 8. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 927-936.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2016.9441.
