Human activity recognition in smart environments employing margin setting algorithm

Abstract

Human activity recognition is gaining promising attention in the research community with the recent revolution in artificial intelligence and machine learning to infer activities from the time-series sensor data. Significant progress has been made in the application of effective machine learning algorithms for pattern recognition and prediction of human activities in smart environments, such as ambient assisted living, healthcare monitoring, surveillance-based security and fitness tracking. In this paper, we propose to apply a supervised learning algorithm called margin setting algorithm (MSA) to predict the human activities. To validate the performance of MSA, we compare it with the support vector machine (SVM) and artificial neural network (ANN) and understand the activities of daily living of two residents in a smart home. The experimental results show that our proposed algorithm outperforms other state-of-the-art machine learning algorithms.

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References

  1. Alemdar H, Ertan H, Incel OD, Ersoy C (2013) ARAS human activity datasets in multiple homes with multiple residents. In: Proceedings of the 7th international conference on pervasive computing technologies for healthcare, 2013. ICST (Institute for Computer Sciences, Social-Informatics and Technology), pp 232–235

  2. Batchuluun G, Kim JH, Hong HG, Kang JK, Park KR (2017) Fuzzy system based human behavior recognition by combining behavior prediction and recognition. Expert Syst Appl 81:108–133

    Article  Google Scholar 

  3. Blank M, Gorelick L, Shechtman E, Irani M, Basri R (2005) Actions as space-time shapes. In: Tenth IEEE international conference on computer vision (ICCV'05) Volume 1, vol. 2, pp 1395–1402. IEEE

  4. Brendel W, Fern A, Todorovic S (2011) Probabilistic event logic for interval-based event recognition. In: CVPR 2011, 2011. IEEE, pp 3329–3336

  5. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:1–27

    Article  Google Scholar 

  6. Chen L, Nugent CD (2019) Sensor-based activity recognition review. Human activity recognition and behaviour analysis. Springer, New York, pp 23–47

    Google Scholar 

  7. Chen Y-P, Yang J-Y, Liou S-N, Lee G-Y, Wang J-S (2008) Online classifier construction algorithm for human activity detection using a tri-axial accelerometer. Appl Math Comput 205:849–860

    MathSciNet  Google Scholar 

  8. Cook DJ, Schmitter-Edgecombe M (2009) Assessing the quality of activities in a smart environment. Methods Inf Med 48:480–485

    Article  Google Scholar 

  9. Cook DJ, Crandall AS, Thomas BL, Krishnan NC (2012) CASAS: a smart home in a box. Computer 46:62–69

    Article  Google Scholar 

  10. Cook DJ, Krishnan NC, Rashidi P (2013) Activity discovery and activity recognition: a new partnership. IEEE Trans Cybern 43:820–828

    Article  Google Scholar 

  11. Cornacchia M, Ozcan K, Zheng Y, Velipasalar S (2016) A survey on activity detection and classification using wearable sensors. IEEE Sens J 17:386–403

    Article  Google Scholar 

  12. Dahmen J, Thomas BL, Cook DJ, Wang X (2017) Activity learning as a foundation for security monitoring in smart homes. Sensors 17:737

    Article  Google Scholar 

  13. Emphasis Telematics, https://www.emphasisnet.gr/e-glove/. Accessed Nov 2019

  14. Espinilla M, Medina J, Hallberg J, Nugent C (2018) A new approach based on temporal sub-windows for online sensor-based activity recognition. J Ambient Intell Hum Comput 1–13

  15. Fu J, Caulfield HJ, Wu D, Tadesse W (2010) Hyperspectral image analysis using artificial color. J Appl Remote Sens 4:043514

    Article  Google Scholar 

  16. Hassan MM, Huda S, Uddin MZ, Almogren A, Alrubaian M (2018) Human activity recognition from body sensor data using deep learning. J Med Syst 42:99

    Article  Google Scholar 

  17. Holzinger A, Röcker C, Ziefle M (2015) From smart health to smart hospitals. Smart health. Springer, New York, pp 1–20

    Google Scholar 

  18. Hsu Y-L, Yang S-C, Chang H-C, Lai H-C (2018) Human daily and sport activity recognition using a wearable inertial sensor network. IEEE Access 6:31715–31728

    Article  Google Scholar 

  19. Igwe OM, Wang Y, Giakos GC (2018) Activity learning and recognition using margin setting algorithm in smart homes. In: 2018 IEEE ubiquitous computing, electronics and mobile communication conference (UEMCON), New York, Nov 8–10, 2018. IEEE, pp 294–299

  20. Lillo I, Soto A, Carlos Niebles J (2014) Discriminative hierarchical modeling of spatio-temporally composable human activities. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014. pp 812–819

  21. Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2018) An improved extreme learning machine model for the prediction of human scenarios in smart homes. Appl Intell 48:2017–2030

    Article  Google Scholar 

  22. Liu L, Wang S, Su G, Huang Z-G, Liu M (2017) Towards complex activity recognition using a Bayesian network-based probabilistic generative framework. Pattern Recogn 68:295–309

    Article  Google Scholar 

  23. Malaisé A, Maurice P, Colas F, Charpillet F, Ivaldi S (2018) Activity recognition with multiple wearable sensors for industrial applications. In: ACHI 2018-eleventh international conference on advances in computer–human interactions, 2018

  24. Medina-Quero J, Zhang S, Nugent C, Espinilla M (2018) Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition. Expert Syst Appl 114:441–453

    Article  Google Scholar 

  25. Mohamed R, Perumal T, Sulaiman MN, Mustapha N (2017) Multi resident complex activity recognition in smart home: a literature review. Int J Smart Home 11:21–32

    Article  Google Scholar 

  26. Ordóñez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16:115

    Article  Google Scholar 

  27. Palumbo F, Gallicchio C, Pucci R, Micheli A (2016) Human activity recognition using multisensor data fusion based on reservoir computing. J Ambient Intell Smart Environ 8:87–107

    Article  Google Scholar 

  28. Qian H, Mao Y, Xiang W, Wang Z (2010) Recognition of human activities using SVM multi-class classifier. Pattern Recogn Lett 31:100–111

    Article  Google Scholar 

  29. Read J, Pfahringer B, Holmes G (2008) Multi-label classification using ensembles of pruned sets. In: 8th IEEE international conference on data mining, 2008. IEEE, pp 995–1000

  30. Read J, Martino L, Olmos PM, Luengo D (2015) Scalable multi-output label prediction: from classifier chains to classifier trellises. Pattern Recognit 48:2096–2109

    Article  Google Scholar 

  31. Reyes-Ortiz J-L, Oneto L, Samà A, Parra X, Anguita D (2016) Transition-aware human activity recognition using smartphones. Neurocomputing 171:754–767

    Article  Google Scholar 

  32. Roetenberg D, Luinge H, Slycke P (2009) Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors Xsens Motion Technologies BV, Tech Rep 1

  33. Roggen D et al (2010) Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh international conference on networked sensing systems (INSS), 2010. IEEE, pp 233–240

  34. Ronao CA, Cho SB (2016) Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst Appl 59:235–244

    Article  Google Scholar 

  35. Saha HN, Mandal A, Sinha A (2017) Recent trends in the Internet of Things. In: 2017 IEEE 7th annual computing and communication workshop and conference (CCWC), 2017. IEEE, pp 1–4

  36. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, 2004. IEEE, pp 32–36

  37. Sezer OB, Dogdu E, Ozbayoglu AM (2017) Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet Things J 5:1–27

    Article  Google Scholar 

  38. Van Laerhoven K, Aidoo KA, Lowette S (2001) Real-time analysis of data from many sensors with neural networks. In: Proceedings fifth international symposium on wearable computers, 2001. IEEE, pp 115–122

  39. Wang Y, Adhami R, Fu J (2015) A new machine learning algorithm for removal of salt and pepper noise. In: Seventh international conference on digital image processing (ICDIP 2015), 2015a. International society for optics and photonics, p 96311R

  40. Wang Y, Adhmai R, Fu J, Al-Ghaib H (2015b) A novel supervised learning algorithm for salt-and-pepper noise detection. Int J Mach Learn Cybern 6:687–697

    Article  Google Scholar 

  41. Wang Y, Fu J, Adhami R, Dihn H (2016) A novel learning-based switching median filter for suppression of impulse noise in highly corrupted colour images. Imaging Sci J 64:15–25

    Article  Google Scholar 

  42. Wang Y, Amin M, Fu J, Moussa H (2017) A novel data analytical approach for false data injection cyber-physical attack mitigation in smart grids. IEEE Access 5:26022

    Article  Google Scholar 

  43. Wang Y, Fu J, David Pan W (2018) Impact of setting margin on margin setting algorithm and support vector machine. J Imaging Sci Technol 62:30501–30511

    Article  Google Scholar 

  44. Wang Y, Fu J, Wei B (2019) A novel parallel learning algorithm for pattern classification SN. Appl Sci 1:1647

    Google Scholar 

  45. Yang J-Y, Wang J-S, Chen Y-P (2008) Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recognit Lett 29:2213–2220

    Article  Google Scholar 

  46. Yin J, Yang Q, Pan JJ (2008) Sensor-based abnormal human-activity detection. IEEE Trans Knowl Data Eng 20:1082–1090

    Article  Google Scholar 

  47. Zhang S, Wei Z, Nie J, Huang L, Wang S, Li Z (2017) A review on human activity recognition using vision-based method. J Healthc Eng 2017:1–31. https://doi.org/10.1155/2017/3090343

    Article  Google Scholar 

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Correspondence to Ogbonna Michael Igwe or Yi Wang.

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Igwe, O.M., Wang, Y., Giakos, G.C. et al. Human activity recognition in smart environments employing margin setting algorithm. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02229-y

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Keywords

  • Margin setting algorithm
  • Machine learning
  • Activity recognition
  • Supervised learning
  • Smart environment