Fitness Device Based on MEMS Sensor

  • Fenglin Wei
  • Chengquan Hu
  • Lili He
  • Kai Wang
  • Yu JiangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)


Nowadays, motion detection technology is an important field of investigation especially for those researchers whose field is human-computer interaction. Visual algorithms are generally getting complicated when the scale of information is huge. Under most of the situations, calculations need to be done rapidity. Vision sensor may not that appropriate. MEMS provides low dimensional data with stronger adaptability for various occasions. This paper represents a fitness device in which an acceleration sensor can capture users’ movements. Experimental results confirm the feasibility of the fitness devices.


Motion detection MEMS sensor Acceleration decomposition Fitness device 


  1. 1.
    Reddy, K.K., Shah, M.: Recognizing 50 human action categories of web videos. Mach. Vis. Appl. 24(5), 971–981 (2013)CrossRefGoogle Scholar
  2. 2.
    Ellis, C., Masood, S.Z., Tappen, M.F., et al.: Exploring the trade-off between accuracy and observational latency in action recognition. Int. J. Comput. Vis. 101(3), 420–436 (2013)CrossRefGoogle Scholar
  3. 3.
    Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. (CSUR) 43(3), 16 (2011)CrossRefGoogle Scholar
  4. 4.
    Ji, R., Yao, H., Sun, X.: Actor-independent action search using spatiotemporal vocabulary with appearance hashing. Pattern Recognit. 44(3), 624–638 (2011)CrossRefGoogle Scholar
  5. 5.
    Niebles, J.C., Wang, H., Li, F.F.: Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 79(3), 299–318 (2008)CrossRefGoogle Scholar
  6. 6.
    Laptev, I., Lindeberg, T.: On space-time interest points. Int. J. Comput. Vis. 64(2), 107–123 (2005)CrossRefGoogle Scholar
  7. 7.
    Jiang, Z., Lin, Z., Davis, L.: Recognizing human actions by learning and matching shape-motion prototype trees. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 533–547 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Fenglin Wei
    • 1
  • Chengquan Hu
    • 1
  • Lili He
    • 1
  • Kai Wang
    • 1
  • Yu Jiang
    • 1
    Email author
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina

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