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Novel features for intensive human activity recognition based on wearable and smartphone sensors

  • Asmita Nandy
  • Jayita Saha
  • Chandreyee ChowdhuryEmail author
Technical Paper
  • 32 Downloads

Abstract

On the lap of this modern era, human activity recognition (HAR) has been of great help in case of health monitoring and rehabilitation. Existing works mostly use one or more specific devices (with embedded sensors) including smartphones for activity recognition and most of the time the detected activities are coarse grained like sit or walk rather than detailed and intensive like sit carrying weight or walk carrying weight. But, intensity of activities reflects valuable insight about a person’s health and more importantly, physical exertion for performing those activities. Consequently, in this paper, we propose an intense activity recognition framework that combines features from smartphone accelerometer (available in almost every smartphone) and that from wearable heartrate sensor. We introduce a set of novel heartrate features that takes into consideration finer variation of heartrate as compared to the resting heartrate of an individual. The proposed framework forms an ensemble model based on different classifiers to address the challenge of usage behavior in terms of how the smartphone is carried. The stack generalization based ensemble model predicts the intensity of activity. We have implemented the framework and tested for a real dataset collected from four users. We have observed that our work is able to identify both static and dynamic intense activities with 96% accuracy, and even found to be better than state of the art techniques.

Notes

References

  1. Acharjee D, Mukherjee A, Mandal JK, Mukherjee N (2016) Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors. Microsyst Technol 22(11):2715–2722CrossRefGoogle Scholar
  2. Adi E, Yeh CI, Chou N, Lee MW, Lin YH (2016) Integrated wearable system for monitoring heart rate and step during physical activity. Mob Inf Syst 2016:1–10Google Scholar
  3. Altin C, Er O (2016) Comparison of different time and frequency domain feature extraction methods on elbow gesture’s emg. Eur J Interdiscip Stud 5:35CrossRefGoogle Scholar
  4. Banerjee T, Keller JM, Skubic M, Stone E (2014) Day or night activity recognition from video using fuzzy clustering techniques. IEEE Trans Fuzzy Syst 22(3):483–493CrossRefGoogle Scholar
  5. Choudhury T, Borriello G, Consolvo S, Haehnel D, Harrison B, Hemingway B, Hightower J, Klasnja P, Koscher K, LaMarca A, Landay JA, LeGrand L, Lester J, Rahimi A, Rea A, Wyatt D (2008) The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput 7(2):32–41CrossRefGoogle Scholar
  6. Coskun D, Incel OD, Ozgovde A (2015) Phone position/placement detection using accelerometer: impact on activity recognition. In: 2015 IEEE 10th international conference on intelligent sensors, sensor networks and information processing (ISSNIP), pp 1–6Google Scholar
  7. Curone D, Tognetti A, Secco EL, Anania G, Carbonaro N, De Rossi D, Magenes G (2010) Heart rate and accelerometer data fusion for activity assessment of rescuers during emergency interventions. IEEE Trans Inf Technol Biomed 14(3):702–710CrossRefGoogle Scholar
  8. Goldberger A, Amaral L, Glass L, Havlin S, M Hausdorg J, Ivanov P, G Mark R, E Mietus J, B Moody G, Peng CK, Stanley H, Physiobank P (2000) Components of a new research resource for complex physiologic signals. PhysioNet 101Google Scholar
  9. Gupta P, Dallas T (2014) Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans Biomed Eng 61(6):1780–1786CrossRefGoogle Scholar
  10. Janidarmian M, Roshan Fekr A, Radecka K, Zilic Z (2017) A comprehensive analysis on wearable acceleration sensors in human activity recognition. Sensors 17:529CrossRefGoogle Scholar
  11. Lau SL, König I, David K, Parandian B, Carius-Düssel C, Schultz M (2010) Supporting patient monitoring using activity recognition with a smartphone. In: 2010 7th International symposium on wireless communication systems, pp 810–814Google Scholar
  12. LeeHeyone J, Oh KH, Do JC, Nam CW, Hwang DH, Lee SJ (2019) Angular velocity estimation of rotating plate using extended kalman filter with accelerometer bias model. Microsyst Technol 25:2855–2867CrossRefGoogle Scholar
  13. Mehrang S, Pietila J, Korhonen I (2018) An activity recognition framework deploying the random forest classifier and a single optical heart rate monitoring and triaxial accelerometer wrist-band. Sensors (Basel) 18(2):613CrossRefGoogle Scholar
  14. Nath S, Dey A, Pachal P, Sing JK, Sarkar SK (2019) Performance analysis of gas sensing device and corresponding iot framework in mines. Microsyst Technol.  https://doi.org/10.1007/s00542-019-04621-x
  15. Park H, Dong S, Lee M, Youn I (2017) The role of heart-rate variability parameters in activity recognition and energy-expenditure estimation using wearable sensors. Sensors (Basel) 17(7):1698CrossRefGoogle Scholar
  16. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, VanderPlas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2012) Scikit-learn: machine learning in python. arXiv:abs/1201.0490
  17. Powers D (2008) Evaluation: from precision, recall and f-factor to roc, informedness, markedness and correlation. Mach Learn Technol 2Google Scholar
  18. RoyChowdhury I, Saha J, Chowdhury C (2018) Detailed activity recognition with smartphones. In: 2018 5th International conference on emerging applications of information technology (EAIT), pp 1–4Google Scholar
  19. Saha J, Chowdhury C, Biswas S (2018a) Two phase ensemble classifier for smartphone based human activity recognition independent of hardware configuration and usage behaviour. Microsyst Technol 24(6):2737–2752CrossRefGoogle Scholar
  20. Saha J, Chowdhury C, Chowdhury IR, Biswas S, Aslam N (2018b) An ensemble of condition based classifiers for device independent detailed human activity recognition using smartphones. Information 9:94CrossRefGoogle Scholar
  21. Shany T, Redmond SJ, Narayanan MR, Lovell NH (2012) Sensors-based wearable systems for monitoring of human movement and falls. IEEE Sens J 12(3):658–670CrossRefGoogle Scholar
  22. Tapia EM, Intille SS, Haskell W, Larson K, Wright J, King A, Friedman R (2007) Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: 2007 11th IEEE international symposium on wearable computers, pp 37–40Google Scholar
  23. Zhou ZH (2015) Ensemble learning. Springer, Boston, pp 411–416Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of CSEJadavpur UniversityKolkataIndia

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