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A Wrist Worn Acceleration Based Human Motion Analysis and Classification for Ambient Smart Home System

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Abstract

In recent years, health-care industry has received a major boost due to sensors i.e., accelerometers, magnetometers etc., which allow its user to get instant updates about their current health status in indoor/outdoor environments. The real driving force behind the usage of accelerometer has been the fitness industry but it also holds a prominent place in ambient smart home to monitor resident’s life-style. In this paper, we proposed a novel triaxial accelerometer-based human motion detection and recognition system using multiple features and random forest. Triaxial signals have been statistically processed to produce worthy features like variance, positive and negative peaks, and signal magnitude features. The proposed model was evaluated over HMP recognition data sets and achieved satisfactory recognition accuracy of 85.17%. The proposed system is directly applicable to any elderly/children health monitoring system, 3D animated games/movies and examining the indoor behaviors of people at home, malls and offices.

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References

  1. Saghafi B, Rajan D, Li W (2016) Efficient 2D viewpoint combination for human action recognition. Proc Pattern Anal Appl 19(2):563–577

    Article  MathSciNet  Google Scholar 

  2. Keceli A, Can A (2014) A multimodal approach for recognizing human actions using depth information. Proceedings of IEEE Conf. Pattern recognition workshops, pp 421–426

  3. Kazama T (2018) Thermohydrodynamic lubrication model of a slipper in swashplate type axial piston machines-validation through experimental data. Int J Hydromechatron 1(3):259–271

    Article  Google Scholar 

  4. Kamal S, Jalal A, Kim D (2016) Depth images-based human detection, tracking and activity recognition using spatiotemporal features and modified HMM. JEET 11:1921–1926

    Google Scholar 

  5. Lin X (2017) Magnetic orientation system based on magnetometer, accelerometer and gyroscope. CAAI Trans Intell Technol. https://doi.org/10.1049/trit.2017.0024

    Google Scholar 

  6. Mahmood M, Jalal A, Evans HA (2018) Facial expression recognition in image sequences using 1D transform and gabor wavelet transform. IEEE conf. on Applied and Engi. Mathematics, pp 21–26

  7. Jalal A, Kamal S, Kim D (2014) A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors 14(7):11735–11759

    Article  Google Scholar 

  8. Jalal A, Kim JT, Kim TS (2012) Development of a life logging system via depth imaging-based human activity recognition for smart homes. Proc Inter Symp SHB, pp 91–95

  9. Jalal A, Kamal S, Kim D (2015) Depth silhouettes context: a new robust feature for human tracking and activity recognition based on embedded HMMs. Proceedings of URAI, Korea, pp 294–299

  10. Stetsyuk V, Kiong JCC (2018) Reynolds stress statistics in the near nozzle region of coaxial swirling jets. Int J Hydromechatron 1(3):332–349

    Article  Google Scholar 

  11. Kim Y-N, Park J-H, Kim M-H (2018) Spatio-temporal analysis of trajectory for pedestrian activity recognition. JEET 13(2):961–968

    Google Scholar 

  12. Nam Y, Kim Y (2015) Individualized exercise and diet recommendations: an expert system for monitoring physical activity and lifestyle interventions in obesity. JEET 10(6):2434–2441

    Google Scholar 

  13. Thanh VP, Tran DT, Nguyen DC, Anh ND, Dinh DN, El-Rabaie S, Sandrasegaran K (2018) Development of a real-time, simple and high-accuracy fall detection system for elderly using 3-DOF accelerometers. Arab J Sci Eng 44:1–14

    Google Scholar 

  14. Moussa MM, Hemayed EE, El Nemr HA, Fayek MB (2018) Human action recognition utilizing variations in skeleton dimensions. Arab J Sci Eng 43(2):597–610

    Article  Google Scholar 

  15. Yoon J-W, Noh Y-S, Kwon Y-S, Park S-B, Kim W-K, Yoon H-R (2014) Improvement of dynamic respiration monitoring through sensor fusion of accelerometer and gyro-sensor. JEET 9(1):334–343

    Google Scholar 

  16. Jalal A, Shahzad A (2007) Multiple facial feature detection using vertex-modeling structure. Proc ICL, pp 1–7

  17. Zhao X (2017) Second order differential evolution algorithm. CAAI Trans Intell Technol 1:1. https://doi.org/10.1049/trit.2017.0006

    Google Scholar 

  18. Jalal A, Kim Y, Kamal S, Farooq A, Kim D (2015) Human daily activity recognition with joints plus body features representation using kinect sensor. Proceedings of ICIEV, Japan, pp 1–6

  19. Jalal A, Kamal S, Kim D, Kim D (2015) Shape and motion features approach for activity tracking and recognition from Kinect video camera. Proceedings of WAINA Conference, Korea, pp 445–450

  20. Johnson JL (2018) Design of experiments and progressively sequenced regression are combined to achieve minimum data sample size. Int J Hydromechatron 1(3):308–331

    Article  Google Scholar 

  21. Jalal A, Kim S (2005) A complexity removal in the floating point and rate control phenomenon. Proc. of KMS, pp 48–51

  22. Jalal A, Kamal S, Kim D (2016) Human depth sensors-based activity recognition using spatiotemporal features and hidden markov model for smart environments. JCNC 2016:1–11

    Google Scholar 

  23. Jalal A, Kamal S, Kim D (2015) Individual detection-tracking-recognition using depth activity images. Proc. of URAI, pp 450–455

  24. Wei Z, Lee D, Nelson B, Archibald J (2008) Real-time accurate optical flow-based motion sensor. Proceedings of the IEEE Conference on PR

  25. Jalal A, Kim S, Yun B (2005) Assembled algorithm in the real-time H.263 codec for advanced performance. Proc. of Healthcom, pp 295–298

  26. Jalal A, Lee S, Kim J, Kim T (2012) Human activity recognition via the features of labeled depth body parts. Proceedings of ICOST, Italy, pp 246–249

  27. Scornet E (2016) Random forests and Kernal methods. IEEE Trans Inf Theory 62(3):1485–1500

    Article  MathSciNet  MATH  Google Scholar 

  28. Jalal A, Mahmood M (2019) Students’ behavior mining in e-learning environment using cognitive processes with information technologies, education and information technologies. Springer, New York

    Google Scholar 

  29. Jalal A, Kim Y-H, Kim Y-J, Kamal S, Kim D (2017) Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern Recogn 61:295–308

    Article  Google Scholar 

  30. Ivannikova E, Hamalainen T, Luostarinen K (2014) Variable group selection based on regression trees: paper machine case study. Proceedings of EAIS, Austria

  31. Bruno B, Mastrogiovanni F, Sgorbissa A, Vernazza T, Zaccaria R (2013) Analysis of human behavior recognition algorithms based on acceleration data. Proceedings of IEEE International Conference on Robotics and automation, pp 1602–1607

  32. De P, Chatterjee A, Rakshit A (2018) Recognition of human behavior for assisted living using dictionary learning approach. IEEE Sens J 18(6):2434–2441

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1D1A1A02085645).

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Correspondence to Ahmad Jalal.

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Jalal, A., Quaid, M.A.K. & Kim, K. A Wrist Worn Acceleration Based Human Motion Analysis and Classification for Ambient Smart Home System. J. Electr. Eng. Technol. 14, 1733–1739 (2019). https://doi.org/10.1007/s42835-019-00187-w

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  • DOI: https://doi.org/10.1007/s42835-019-00187-w

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