Princy Randhawa, Vijay C. Shanthagiri, and Ajay Kumar


  1. [1] E. Garcia, R.F. Brena, J.C. Carrasco-Jimenez, andL. Garrido, Long-term activity recognition from wristwatchaccelerometer data, Sensors, 14(12), 2014, 22500–22524.doi:10.3390/s141222500.
  2. [2] S.-W. Lee and K. Mase, Activity and location recognition usingwearable sensors, IEEE Pervasive Computing, 1, 2002, 24–32.
  3. [3] A. Mazzoldi, D. De Rossi, F. Lorussi, E.P. Scilingo, and R.Paradiso, Smart textiles for wearable motion capture systems,Autex Research Journal, 2, 2002, 199–203.
  4. [4] L. Atallah, B. Lo, R. King, and G. Yang, Sensor positioningfor activity recognition using wearable accelerometers, IEEETransactions on Biomedical Circuits and Systems, 5(4), 2011,320–329.
  5. [5] F. Attal, S. Mohammed, M. Dedabrishvili, F. Chamroukhi, L.Oukhellou, and Y. Amirat, Physical human activity recognitionusing wearable sensors, Sensors, 15(12), 2015, 31314–31338.
  6. [6] L.M. Castano and A.B. Flatau, Smart fabric sensors ande-textile technologies: A review, Smart Materials andStructures, 23(5), 2014, 53001.
  7. [7] Y. Cha, K. Nam, and D. Kim, Patient posture monitoringsystem based on flexible sensors, Sensors, 17(3), 2017, 584.
  8. [8] M. Cornacchia, K. Ozcan, Y. Zheng, and S. Velipasalar, Usingwearable sensors, 17(2), 2017, 386–403.
  9. [9] S.B. Gadhe, G. Chinchansure, A. Kumar, and M. Ojha, Womenanti-rape belt, Compusoft, 4(4), 2015, 1632–1636.
  10. [10] M. Jutila, H. Rivas, P. Karhula, and S. Pantsar-Syv¨aniemi,Implementation of a wearable sensor vest for the safety andwell-being of children, Procedia Computer Science, 32, 2014,888–893.
  11. [11] R. Madarshahian and J.M. Caicedo, Human activity recogni-tion using multinomial logistic regression, in H.S. Atamturktur,B. Moaveni, C. Papadimitriou, and T. Schoenherr (eds.), Modelvalidation and uncertainty quantification (Springer Interna-tional Publishing, 2015), Vol. 3.
  12. [12] Y. Menguc, Y.-L. Park, H. Pei, D. Vogt, P.M. Aubin, E.Winchell, and C.J. Walsh, Wearable soft sensing suit for hu-man gait measurement, The International Journal of RoboticsResearch, 33(14), 2014, 1748–1764.
  13. [13] T. Karthick and M. Manikandan, Fog assisted IoT based med-ical cyber system for cardiovascular diseases affected patients,Concurrency and Computatation Practice and Experience, 31,2019, e4861.
  14. [14] P. Randhawa, V. Shanthagiri, and A. Kumar, A review onapplied machine learning in wearable technology and its ap-plications, International Conference on Intelligent SustainableSystems (ICISS), 2017, 347–354.
  15. [15] E. Al Safadi, F. Mohammad, D. Iyer, B.J. Smiley, andN.K. Jain, Generalized activity recognition using accelerom-eter in wearable devices for IoT applications, 13th IEEE In-ternational Conference on Advanced Video and Signal BasedSurveillance (AVSS), Colorado Springs, CO, 2016, 73–79,doi:10.1109/AVSS.2016.7738020.
  16. [16] P. Randhawa, V. Shanthagiri, and A. Kumar, Design anddevelopment of a Smart-Jacket for posture detection andclassification using machine learning, International Conferenceon SmartComputing and Electronics Enterprise, 2018.
  17. [17] J. Wang, et al., Wearable sensor based human posture recog-nition, IEEE International Conference on Big Data (BigData), Washington, DC, 2016, 3432–3438, doi: 10.1109/BigData.2016.7841004.
  18. [18] N.D. Nguyen, D.T. Bui, P.H. Truong, and G.-M. Jeong,Position-based feature selection for body sensors regardingdaily living activity recognition, Journal of Sensors, 2018,9762098.
  19. [19] C.V. Bouten, K.T. Koekkoek, M. Verduin, R. Kodde, andJ.D. Janssen, A triaxial accelerometer and portable dataprocessing unit for the assessment of daily physical activity,IEEE Transactions on Biomedical Engineering, 44, 1997, 136–147.
  20. [20] L. Baoand S.S. Intille, Activity recognition from user-annotated acceleration data, in Pervasive Computing, SpringerBerlin/Heidelberg, Berlin, Germany, 2004, 1–17.
  21. [21] B. Wang, Y. Li, H. Lang, and Y. Wang, Hand gesturerecognition and motion estimation using the kinect sensor,Mechatronic Systems and Control, 48(1), 2020.
  22. [22] G. Sreenu and M.A. Durai, Intelligent video surveillance:A review through deep learning techniques for crowd analysis,Journal of Big Data, 6, 2019, 48. doi:10.1186/s40537-019-0212-5.
  23. [23] S. Pappu, P. Vudatha, A.V. Niharika, T. Karthick, and S.Sankaranarayanan, Intelligent IoT based water quality mon-itoring system, International Journal of Applied EngineeringResearch, 12(16), 2017, 5447–5454.
  24. [24] L. Cheng, Y. Guan, K. Zhu, and Y. Li, Recognition ofhuman activities using machine learning methods with wearablesensors, IEEE 7th Annual Computing and CommunicationWorkshop and Conference (CCWC), Las Vegas, NV, 2017,1–7, doi:10.1109/CCWC.2017.7868369.
  25. [25] M. Cornacchia, K. Ozcan, Y. Zheng, and S. Velipasalar,A survey on activity detection and classification using wear-able sensors, IEEE Sensors Journal, 17(2), 2017, 386–403,doi:10.1109/JSEN.2016.2628346.
  26. [26] X. Kui, W. Liu, K. Guo, J. Xia, and H. Du, Teaching methodreform of python language programming course based onminimum knowledge sets, Mechatronic Systems and Control,46(4), 2018, 181–186.
  27. [27] N. Noury, A. Galay, J. Pasquier, and M. Ballussaud, Prelim-inary investigation into the use of autonomous fall detectors,in Proc. International Conference of the IEEE Engineering inMedicine and Biology Society, 2008, 2828–2831.
  28. [28] J. Shin, S.J. Kim, D.-H. Kim, C.S. Hwang, K. Do, T. Hyeon,et al., Multifunctional wearable devices for diagnosis andtherapy of movement disorders, Nature Nanotechnology, 9(5),2014, 397–404.

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