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Multimodal information fusion based human movement recognition

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Abstract

Emotion recognition has wide application prospect in multiple fields including psychological research, security monitoring and control, distance education and clinical medicine, the information source of which involves multiple original data statuses of humans in the recognition process. This paper briefly introduces several kinds of emotion recognition methods based on different data sources and information integration technology thus to provide certain theoretical background for the engineering technicians. And then it conducts classified introduction to the current situation of emotion recognition in the multi-source information integration field, explains and analyzes the problems existed in emotion recognition based on multi-source information integration, briefly expounds the application prospect of it in the emotion recognition field, and finally makes summary.

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References

  1. Arunkumar N, Ramkumar K, Venkatraman V, Abdulhay E, Fernandes SL, Kadry S, Segal S (2017) Classification of focal and non focal EEG using entropies. Pattern Recogn Lett 94:112–117

    Article  Google Scholar 

  2. Chen Y, Tang F, Bao Y, Tang Y, Chen G (2016) A Fe-C coated long period fiber grating sensor for corrosion induced mass loss measurement. Opt Lett 41(2016):2306–2309

    Article  Google Scholar 

  3. Du X, Chen L, Huang D, Peng Z, Zhao C, Zhang Y, Zhu Y, Wang Z, Li X, Liu G (2017b) Elevated apoptosis in the liver of dairy cows with ketosis. Cell Physiol Biochem 43(2):568–578

    Article  Google Scholar 

  4. Fernandes SL, Gurupur VP, Sunder NR, Arunkumar N, Kadry S (2017) A novel nonintrusive decision support approach for heart rate measurement. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2017.07.002

  5. Ghebrebrhan M, Aranda F, Walsh G, Ziegler D, Giardini S, Carlson J, Kimball B, Steeves D, Xia Z, Yu S, Kingsley E, Nagarajan R, Torcedo J, Williams R, Gatesman A (2017) Textile frequency selective surface. IEEE Microwave Wireless Compon Lett 27:989–991. https://doi.org/10.1109/LMWC.2017.2750031

    Article  Google Scholar 

  6. Kurup P, Sullivan C, Hannagan R, Yu S, Azimi H, Robertson S, Ryan D, Nagarajan R, Ponrathnam T, Howe G (2017) A review of technologies for characterization of heavy metal contaminants. Indian Geotech J 47:421–436. https://doi.org/10.1007/s40098-016-0214-6

    Article  Google Scholar 

  7. Lv Z, Halawani A, Feng S, Li H, Réhman SU (2014) Multimodal hand and foot gesture interaction for handheld devices. ACM Trans Multimed Comput, Commun Appl (TOMM) 11(1s):10

    Google Scholar 

  8. Pan W, Chen S, Feng Z (2013) Automatic clustering of social tag using community detection. Appl Math Inf Sci 7(2):675–681

    Article  Google Scholar 

  9. Stephygraph LR, Arunkumar N (2016) Brain-actuated wireless mobile robot control through an adaptive human-machine interface. Adv Intell Syst Comput 397:537–549

    Google Scholar 

  10. Stephygraph LR, Arunkumar N, Venkatraman V (2015) Wireless mobile robot control through human machine interface using brain signals. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, art. no. 7225484, pp 596–603

  11. Zhang Y, Li Q, Welsh WJ, Moghe PV, Uhrich KE (2016) Micellar and structural stability of nanoscale amphiphilic polymers: implications for anti-atherosclerotic bioactivity. Biomaterials 84:230–240

    Article  Google Scholar 

  12. Zhao Y, Wang L, Wang H, Liu C (2015) Minimum rate sampling and spectrum blind reconstruction in random equivalent sampling. Circ Syst Signal Process 34(8):2667–2680

    Article  Google Scholar 

  13. Zhao Y, Hu YH, Liu J (2017) Random triggering-based sub-nyquist sampling system for sparse multiband signal. IEEE Trans Instrum Meas 66(7):1789–1797

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Yao Shu & Heng Zhang

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  1. Yao Shu
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  2. Heng Zhang
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Correspondence to Yao Shu.

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Shu, Y., Zhang, H. Multimodal information fusion based human movement recognition. Multimed Tools Appl 79, 5043–5052 (2020). https://doi.org/10.1007/s11042-018-6315-8

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  • DOI: https://doi.org/10.1007/s11042-018-6315-8

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