Abstract
In the last few decades, life expectancy has been increasing. This has resulted in a higher proportion of older adults and increased prevalence of chronic conditions, posing challenges facing care needs. A possible solution is to foster both the prevention and health-related re-education, supporting healthier lifestyle and facilitating independent living. To facilitate this, it is crucial to measure individual’s key health metrics. For instance, human activity recognition through sensors provides valuable information about an individual’s lifestyle. Some crucial decisions, among which the quality of data collection, strengthen the methodological approach. This chapter addresses how the quality of data may affect the recognition performance. Two datasets of daily activities were collected through a triaxial accelerometer placed on the subject’s dominant wrist. The first dataset was collected by 141 users, whereas the second one comprised semi-realistic activities executed by three individuals. Specifically, outcomes were based on a comparison of activity recognition performance of six machine learning classifiers. Results show that, firstly, a higher number of features may not improve the recognition rate. Secondly, one approach may be robust in a laboratory setting but not generalizable to real-world applications. Finally, a great variability may increase the generalization of classifiers for successful activity recognition.
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References
Anguita D, Ghio A, Oneto L et al (2013) A public domain dataset for human activity recognition using smartphones, pp 24–26
Ariani A, Koesoema AP, Soegijoko S (2017) Innovative healthcare systems for the 21st century
Banos O, Galvez JM, Damas M et al (2014) Window size impact in human activity recognition. Sensors (Switzerland) 14:6474–6499. https://doi.org/10.3390/s140406474
Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data, pp 1–17. https://doi.org/10.1007/978-3-540-24646-6_1
Brox Ó (2013) The value of health care information exchange and interoperability. Atalante 74–83. https://doi.org/10.1377/hlthaff.w5.10
Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv 1:1–33. https://doi.org/10.1145/2499621
Chen L, Khalil I (2010) Activity recognition: approaches, practices and trends (Chap. 3)
Cleland I, Kikhia B, Nugent C, Boytsov A, Josef H, Synnes K et al (2013) Optimal placement of accelerometers for the detection of everyday activities, pp 9183–9200
Cleland I, Donnelly MP, Nugent CD et al (2018) Collection of a diverse, naturalistic and annotated dataset for wearable activity recognition, pp 674–679. https://doi.org/10.1109/percomw.2018.8480322
Cook DJ, Krishnan NC (2015) Activity learning: discovering, recognizing, and predicting human behavior from sensor data
Guiry JJ, P Van De Ven, Nelson J (2014) Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices. 5687–5701. https://doi.org/10.3390/s140305687
Gupta P, Dallas T (2014) Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans Biomed Eng 61:1780–1786. https://doi.org/10.1109/TBME.2014.2307069
Hall AM (1999) Correlation-based feature selection for machine learning. University of Waikato
John G, Kohavi R, Pfleger K (1994) Irrelevant features and the subset selection problem. Icml 121–129. https://doi.org/10.1126/science.333.6044.823-c
Khusainov R, Azzi D, Achumba IE, Bersch SD (2013) Real-time human ambulation, activity, and physiological monitoring: taxonomy of issues, techniques, applications, challenges and limitations. Sensors (Switzerland) 13:12852–12902. https://doi.org/10.3390/s131012852
Kim E, Helal S, Cook D (2010) Human activity recognition and pattern discovery. IEEE Pervasive Comput 9
Kong Y, Fu Y (2018) Action recognition and human interaction. Hum Act Recognit Predict A Surv 13. https://doi.org/10.1007/978-3-319-27004-3_2
Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. Mach Learn ECML-94 784:171–182. https://doi.org/10.1007/3-540-57868-4
Kou Z, Wu C (2018) Smartphone based operating behaviour modelling of agricultural machinery. IFAC-PapersOnLine 51:521–525. https://doi.org/10.1016/j.ifacol.2018.08.156
Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15:1192–1209. https://doi.org/10.1109/SURV.2012.110112.00192
Lester J, Choudhury T, Borriello G (2006) A practical approach to recognizing physical activities, pp 1–16
Mannini A, Intille SS, Rosenberger M et al (2013) Activity recognition using a single accelerometer placed at the wrist or ankle. 2193–2203. https://doi.org/10.1249/mss.0b013e31829736d6
Morales J, Akopian D (2017) Physical activity recognition by smartphones, a survey. Biocybern Biomed Eng 37:388–400. https://doi.org/10.1016/j.bbe.2017.04.004
Morales J, Akopian D, Agaian S (2014) Human activity recognition by smartphones regardless of device orientation human activity recognition by smartphones regardless of device orientation. https://doi.org/10.1117/12.2043180
Nakamoto K, Konishi Y, Kondo K, Ishigaki H (1999) Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. IDC 1999—1999 Information, Decis Control Data Inf Fusion Symp Signal Process Commun Symp Decis Control Symp Proc 15:283–288. https://doi.org/10.1109/idc.1999.754171
Nugent C et al (2016) Improving the quality of user generated data sets for activity recognition. In: García C, Caballero-Gil P, Burmester M, Quesada-Arencibia A (eds) Ubiquitous computing and ambient intelligence. IWAAL 2016, AmIHEALTH 2016, UCAmI 2016. Lecture Notes in Computer Science, vol 10070. Springer, Cham
Preece SJ, Goulermas JY, Kenney LPJ et al (2009) Activity identification using body-mounted sensors—a review of classification techniques. Physiol Meas 30. https://doi.org/10.1088/0967-3334/30/4/r01
Randell C, Muller H (2000) Context awareness by analysing accelerometer data, pp 175–176
Twomey N, Diethe T, Fafoutis X et al (2018) A comprehensive study of activity recognition using accelerometers. Informatics 5:27. https://doi.org/10.3390/informatics5020027
Vaizman Y, Weibel NG, Lanckriet N (2017) Context recognition in-the-wild: unified model for multi-modal sensors and multi-label classification. PACM Interact Mob Wearable Ubiquitous Technol 1:1–22. https://doi.org/10.1145/3161192
Vavilis S, Petković M, Zannone N (2012) Impact of ICT on home healthcare. IFIP Adv Inf Commun Technol 386 AICT:111–122. https://doi.org/10.1007/978-3-642-33332-3_11
Viet VQ, Thang HM, Choi D (2012) Balancing precision and battery drain in activity recognition on mobile phone. 1–2. https://doi.org/10.1109/icpads.2012.108
Vrigkas M, Nikou C, Kakadiaris IA (2015) A review of human activity recognition methods. Front Robot AI 2. https://doi.org/10.3389/frobt.2015.00028
Weber DM, Kauffman RJ (2011) What drives global ICT adoption? Analysis and research directions. Electron Commer Res Appl 10:683–701. https://doi.org/10.1016/j.elerap.2011.01.001
Yang JY, Wang JS, Chen YP (2008) Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recognit Lett 29(16):2213–2220. http://linkinghub.elsevier.com/retrieve/pii/S0167865508002560
Yiyan L, Fang Z, Wenhua S (2016) An hidden markov model based complex walking pattern recognition algorithm. In: 2016 fourth international conference on ubiquitous positioning, indoor navigation and location based services (UPINLBS), pp 223–229. https://doi.org/10.1109/upinlbs.2016.7809976
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Poli, A., Spinsante, S., Nugent, C., Cleland, I. (2020). Improving the Collection and Understanding the Quality of Datasets for the Aim of Human Activity Recognition. In: Chen, F., García-Betances, R., Chen, L., Cabrera-Umpiérrez, M., Nugent, C. (eds) Smart Assisted Living. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-25590-9_7
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