Alejandro Baldominos
ABSTRACT
Physical activity is recognized as one of the key factors for a healthy life due to its beneficial effects. The range of physical activities is very broad, and not all of them require the same effort to be performed nor have the same effects on health. For this reason, automatically recognizing the physical activity performed by a user (or patient) turns out to be an interesting research field, mainly because of two reasons: (1) it increases personal awareness about the activity being performed and its consequences on health, allowing to receive proper credit (e.g. social recognition) for the effort; and (2) it allows doctors to perform continuous remote patient monitoring.
This paper proposes a new approach for improving activity recognition by describing an activity recognition chain (ARC) that is optimized by means of genetic algorithms. This optimization process determines the most suitable and informative set of features that turns out into higher recognition accuracy while reducing the total number of sensors required to track the user activity. These improvements can be translated into lower costs in hardware and less intrusive devices for the patients. In this work, for the assessment of the proposed approach versus other techniques and for replication purposes, a publicly available dataset on physical activity (PAMAP2) has been used.
Experiments are designed and conducted to evaluate the proposed ARC by using leave-one-subject-out cross validation and results are encouraging, reaching an average classification accuracy of about 94%.
- H. Ali and D. Amalarethinam. Activity recognition with fuzzy finite automata. In Proc. WCCCT'14, pages 222--227, 2014. Google Scholar
Digital Library
- N. Alshurafa, W. Xu, J. Liu, M.-C. Huang, B. Mortazavi, C. Roberts, and M. Sarrafzadeh. Designing a robust activity recognition framework for health and exergaming using wearable sensors. IEEE Journal of Biomedical and Health Informatics, 18(5):1636--1646, 2013.Google ScholarCross Ref
- M. Altini, J. Penders, R. Vullers, and O. Amft. Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning. IEEE Journal of Biomedical and Health Informatics, 19(1):219--226, 2014.Google ScholarCross Ref
- A. Alvarez, J. Alonso, and G. Trivino. Human activity recognition in indoor environments by means of fusing information extracted from intensity of WiFi signal and accelerations. Information Sciences, 233:162--182, 2013. Google ScholarDigital Library
- D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In Ambient Assisted Living and Home Care, volume 7657 of Lecture Notes in Computer Science, pages 216--223. 2012. Google ScholarDigital Library
- D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz. A public domain dataset for human activity recognition using smartphones. In Proc. ESANN'13, pages 24--26, 2013.Google Scholar
- A. E. Bauman, R. S. Reis, J. F. Sallis, J. C. Wells, R. J. Loos, and B. W. Martin. Correlates of physical activity: why are some people physically active and others not? The Lancet, 380(9838):258--271, 2012.Google ScholarCross Ref
- S. J. H. Biddle and M. Asare. Physical activity and mental health in children and adolescents: a review of reviews. British Journal of Sports Medicine, 45(11):886--895, 2011.Google ScholarCross Ref
- M. F. A. bin Abdullah, A. F. P. Negara, S. Sayeed, D.-J. Choi, and K. S. Muthu. Classification algorithms in human activity recognition using smartphones. International Journal of Computer and Information Engineer, 6:77--84, 2012.Google Scholar
- A. Bulling, U. Blanke, and B. Schiele. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys (CSUR), 46(3):33:1--33:33, 2014. Google ScholarDigital Library
- N. M. Byrne, A. P. Hills, G. R. Hunter, R. L. Weinsier, and Y. Schutz. Metabolic equivalent: one size does not fit all. Journal of Applied Physiology, 99(3):1112--1119, 2005.Google ScholarCross Ref
- C. Catal, S. Tufekci, E. Pirmit, and G. Kocabag. On the use of ensemble of classifiers for accelerometer-based activity recognition. Applied Soft Computing, In Press, 2015.Google Scholar
- S. Chen, J. Lach, O., M. Altini, and J. Penders. Unsupervised activity clustering to estimate energy expenditure with a single body sensor. In Proc. BSN'13), 2013.Google ScholarCross Ref
- S. Chernbumroong, S. Cang, and H. Yu. GA-based classifiers fusion for multi-sensor activity recognition of elderly people. IEEE Journal of Biomedical and Health Informatics, 19(1):282--289, 2015.Google ScholarCross Ref
- W. Chodzko-Zajko. Exercise and physical activity for older adults. Universal Access in the Information Society, 3(1):101--106, 2014.Google Scholar
- R. Cilla, M. A. Patricio, J. García, A. Berlanga, and J. M. Molina. Recognizing human activities from sensors using hidden markov models constructed by feature selection techniques. Algorithms, 2(1):282--300, 2009.Google ScholarCross Ref
- S. Consolvo, K. Everitt, I. Smith, and J. A. Landay. Design requirements for technologies that encourage physical activity. In Proc. SIGCHI Conf. on Human Factors in Comput. Syst. (CHI'06), pages 457--466, 2006. Google ScholarDigital Library
- L. K. Ejsing, U. Becker, J. S. Tolstrup, and T. Flensborg-Madsen. Physical activity and risk of alcohol use disorders: results from a prospective cohort study. Alcohol and Alcoholism, 50(2):206--212, 2014.Google ScholarCross Ref
- I. Fatima, M. Fahim, Y.-K. Lee, and S. Lee. A genetic algorithm-based classifier ensemble optimization for activity recognition in smart homes. Applied Soft Computing, In Press, 2015.Google Scholar
- J. G. Godino, C. Watkinson, K. Corder, S. Sutton, S. J. Griffin, and E. M. van Sluijs. Awareness of physical activity in healthy middle-aged adults: a cross-sectional study of associations with sociodemographic, biological, behavioural, and psychological factors. BMC Public Health, 14(421), 2014.Google Scholar
- Y. Gu, L. Quan, and F. Ren. Wifi-assisted human activity recognition. In Proc. APWiMob'14, pages 60--65, 2014.Google Scholar
- K. B. Guntera, K. R. Ricea, D. S. Wardb, and S. G. Trosta. Factors associated with physical activity in children attending family child care homes. Preventive Medicine, 54(2):131--133, 2012.Google ScholarCross Ref
- P. Gupta and T. Dallas. Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Transactions on Biomedical Engineering, 61(6):1780--1786, 2014.Google ScholarCross Ref
- A. Haase, A. Steptoe, J. F. Sallis, and J. Wardle. Leisure-time physical activity in university students from 23 countries: associations with health beliefs, risk awareness, and national economic development. Preventive Medicine, 39(1):182--190, 2004.Google ScholarCross Ref
- P. C. Hallal, L. B. Andersen, F. C. Bull, R. Guthold, W. Haskell, and U. Ekelund. Global physical activity levels: surveillance progress, pitfalls, and prospects. The Lancet, 380(9838):247--257, 2012.Google ScholarCross Ref
- G. W. Heath, D. C. Parra, O. L. Sarmiento, L. B. Andersen, N. Owen, S. Goenka, F. Montes, and R. C. Brownson. Evidence-based intervention in physical activity: lessons from around the world. The Lancet, 380(9838):272--281, 2012.Google ScholarCross Ref
- I. L. IM, H. Sesso, J. Chen, and R. Paffenbarger. Does physical activity play a role in the prevention of prostate cancer? Epidemiologic Reviews, 23(1):132--137, 2001.Google ScholarCross Ref
- Janssen and LeBlanc. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 7(40), 2010.Google Scholar
- P. R. Kahn, A. Kinsolving, M. A. Christensen, B. Y. Lee, and D. Vogel. Human activity monitoring device with activity identification, 2015.Google Scholar
- J. A. Knight. Physical inactivity: associated diseases and disorders. Annals of Clinical & Laboratory Science, 42(3):320--337, 2012.Google Scholar
- A. Merglen, A. Flatz, R. E. Bélanger, P.-A. Michaud, and J.-C. Suris. Weekly sport practice and adolescent well-being. Archives of Disease in Childhood, 99:208--210, 2014.Google ScholarCross Ref
- D. Morris, I. Kelner, F. Shariff, D. Tom, T. S. Saponas, and A. Guillory. Personal training with physical activity monitoring device, 2015.Google Scholar
- A. Reiss. Personalized Mobile Physical Activity Monitoring for Everyday Life. PhD thesis, Technical University of Kaiserslautern, 2013.Google Scholar
- A. Reiss, G. Hendeby, and D. Stricker. A competitive approach for human activity recognition on smartphones. In Proc. ESANN'13, 2013.Google Scholar
- A. Reiss, G. Hendeby, and D. Stricker. A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring. Personal and Ubiquitous Computing, 19(1):105--121, 2014. Google ScholarDigital Library
- A. Reiss and D. Stricker. Towards global aerobic activity monitoring. In Proc. PETRA'11, 2011. Google ScholarDigital Library
- A. Reiss and D. Stricker. Creating and benchmarking a new dataset for physical activity monitoring. In Proc. 5th Intl. Conf. Pervasive Tech. related to Assistive Environments (PETRA'12), pages 108--109, 2012. Google ScholarDigital Library
- A. Reiss and D. Stricker. Introducing a new benchmarked dataset for activity monitoring. In Proc. Intl. Symp. Wearable Computers (ISWC'12), pages 108--109, 2012. Google ScholarDigital Library
- A. Reiss and D. Stricker. Aerobic activity monitoring: towards a long-term approach. Universal Access in the Information Society, 13:101--114, 2013. Google ScholarDigital Library
- A. Reiss, D. Stricker, and I. Lamprinos. An integrated mobile system for long-term aerobic activity monitoring and support in daily life. In Proc. TrustCom'12, pages 2021--2028, 2012. Google ScholarDigital Library
- A. Reiss, M. Weber, and D. Stricker. Exploring and extending the boundaries of physical activity recognition. In Proc. ICSMC'11, pages 46--50, 2011.Google ScholarCross Ref
- Y. Saeys, I. Inza, and P. Larrañaga. A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19):2507--2517, 2007. Google ScholarDigital Library
- T. R. D. Saputri, A. M. Khan, and S.-W. Lee. User-independent activity recognition via three-stage GA-based feature selection. International Journal of Distributed Sensor Networks, 2014, 2014.Google Scholar
- P. Schnohr, M. Grønbæk, L. Petersen, H. O. Hein, and T. I. Sørensen. Physical activity in leisure-time and risk of cancer: 14-year follow-up of 28,000 Danish men and women. Scandinavian Journal of Public Health, 33(4):244--249, 2005.Google ScholarCross Ref
- J. Seiter, A. Derungs, C. Schuster-Amft, O. Amft, , and G. Trster. Activity routine discovery in stroke rehabilitation patients without data annotation. In Proc. Rehab'14, 2014. Google ScholarDigital Library
- M. Shoaib, S. Bosch, O. D. Incel, H. Scholten, and P. J. Havinga. A survey of online activity recognition using mobile phones. Sensors, 15(1):2059--2085, 2015.Google ScholarCross Ref
- L. M. Soria, L. Gonzalez-Abril, J. A. Ortega, and M. A. Alvarez. Low energy physical activity recognition system on smartphones. Sensors, 15(3):5163--5196, 2015.Google ScholarCross Ref
- X. Su, H. Tong, and P. Ji. Activity recognition with smartphone sensors. Tsinghua Science and Technology, 19(3):235--249, 2014.Google ScholarCross Ref
- X. Sun, H. Kashima, and N. Ueda. Large-scale personalized human activity recognition using online multitask learning. IEEE Transactions on Knowledge and Data Engineering, 25(11):2551--2563, 2013. Google ScholarDigital Library
- W. Ugulino, D. Cardador, K. Vega, E. Velloso, R. Milidiú, , and H. Fuks. Wearable computing: accelerometers' data classification of body postures and movements. In Advances in Artificial Intelligence - SBIA 2012, pages 52--61. 2012. Google ScholarDigital Library
- A. W. Vaes, A. Cheung, M. Atakhorrami, M. T. J. Groenen, O. Amft, F. M. E. Franssen, E. F. M. Wouters, and M. A. Spruit. Effect of 'activity monitor-based' counseling on physical activity and health-related outcomes in patients with chronic diseases: A systematic review and meta-analysis. Annals of Medicine, 45(5-6):397--412, 2013.Google ScholarCross Ref
- C. A. Vock, P. Flentov, and D. M. Darcy. Activity monitoring systems and methods, 2013.Google Scholar
- K. L. White, M. L. Orenstein, J. Campbell, C. S. Self, E. Walker, M. Micheletti, G. McKeag, J. Zipperer, and M. Lapinsky. Activity recognition with activity reminders, 2015.Google Scholar
- Q. Xiao, H. P. Yang, N. Wentzensen, A. Hollenbeck, and C. E. Matthews. Physical activity in different periods of life, sedentary behavior, and the risk of ovarian cancer in the NIH-AARP diet and health study. Cancer Epidemiology, Biomarkers & Prevention, 22(11):2000--2008, 2013.Google ScholarCross Ref
- G. Yu, J. Yuan, and Z. Liu. Propagative Hough voting for human activity detection and recognition. IEEE Transactions on Circuits and Systems for Video Technology, 25(1):87--98, 2015.Google ScholarDigital Library
- S. G. J. Yuen, J. Park, and E. N. Friedman. Activity monitoring systems and methods of operating same, 2013.Google Scholar
- Y. Zheng, W.-K. Wong, X. Guan, and S. Trost. Physical activity recognition from accelerometer data using a multi-scale ensemble method. In Proc. IAAI'10, pages 1575--1581, 2010.Google ScholarDigital Library
- Y. Zhu, C. Wang, J. Zhang, and J. Xu. Human activity recognition based on similarity. In Proc. CSE'14, pages 1382--1387, 2014. Google ScholarDigital Library
Index Terms
- Feature Set Optimization for Physical Activity Recognition Using Genetic Algorithms
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