Real time emotion detection within a wireless sensor network and its impact on power consumption

Real time emotion detection within a wireless sensor network and its impact on power consumption

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Recent advances in portable and wearable electroencephalograph (EEG) devices has raised the need to detect emotions in real time for applications such as wellbeing monitoring, gaming and social networking. A number of researchers have reported real time emotion detection systems implemented on a computer. This study advances these efforts by implementing a real time emotion detection system on a wirelesses sensor node with minimal hardware resources (256 kb of flash memory and 16 MHz processing speed) suitable for integration in a wearable wireless sensor node. The experimental results demonstrate that detecting emotions within the sensor node using suitable algorithms prolong the battery life by 5 days (38%) and by 39 days at an emotion detection rate of 2 and 60 s, respectively, as compared with transmitting the raw EEG data wirelessly. This also reduces the length of packets transmitted which directly minimises the packet error rate and the power that would be consumed because of retransmission of these erroneous packets.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 7. Kok, B.E., Coffey, K.A., Cohn, M.A., et al: ‘How positive emotions build physical health: perceived positive social connections account for the upward spiral between positive emotions and vagal tone’, Psychol. Sci., 2013, pp. 124.
    8. 8)
      • 8. Jatupaiboon, N., Panngum, S., Israsena, P.: ‘Emotion classification using minimal EEG channels and frequency bands’. 10th Int. Joint Conf. on Computer Science and Software Engineering (JCSSE), 2013, pp. 2124.
    9. 9)
    10. 10)
    11. 11)
      • 11. Niemic, C.P.: ‘Studies of emotion’, J. Undergrad. Res., 2002, 1, (1), pp. 1518.
    12. 12)
      • 12. Trainor, L., Schmidt, L.: ‘Processing emotions induced by music’, in Peretz, I., Zatorre, R.J. (Eds.): ‘The cognitive neuroscience of music’ (Oxford University Press, USA, 2003).
    13. 13)
      • 13. Park, K.S., Choi, H., Lee, K.J., Lee, J.Y., An, K.O., Kim, E.J.: ‘Emotion recognition based on the asymmetric left and right activation’, Int. J. Med. Med. Sci., 2011, 3, (6), pp. 201209.
    14. 14)
    15. 15)
      • 15. Liu, Y., Sourina, O., Nguyen, M.K.: ‘Real-time EEG-based human emotion recognition and visualization’. Int. Conf. Cyberworlds, October 2010, pp. 262269.
    16. 16)
      • 16. Sanei, S., Chambers, J.A.: ‘EEG signal processing’ (John Wiley & Sons, Ltd, 2007), p. 313.
    17. 17)
      • 17. Anh, V.H., Van, M.N., Ha, B.B., Quyet, T.H.: ‘A real-time model based support vector machine for emotion recognition through EEG’. Int. Conf. Control. Autom. Inf. Sci., November 2012, pp. 191196.
    18. 18)
      • 18. Cheemalapati, S., Gubanov, M., Del Vale, M., Pyayt, A.: ‘A real-time classification algorithm for emotion detection using portable EEG’. IEEE 14th Int. Conf. Inf. Reuse Integr., August 2013, pp. 720723.
    19. 19)
      • 19. Abadi, D.J., Ahmad, Y., Balazinska, M., et al: ‘The design of the borealis stream processing engine’.
    20. 20)
    21. 21)
    22. 22)
      • 22. Matiko, J.W., Beeby, S., Tudor, J.: ‘Real time eye blink noise removal from EEG signals using morphological component analysis’. 35th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2013, vol. 2013, no. 2, pp. 1316.
    23. 23)
      • 23. Matiko, J.W., Beeby, S.P., Tudor, J.: ‘Fuzzy logic based emotion classification’. IEEE Int. Conf. on Acoustic, Speech and Signal Processing, 2014, pp. 44224426.
    24. 24)
      • 24. Queen Mary Univeristy of Landon: ‘DAEAP dataset’. Available at:, accessed 20 October 2013.
    25. 25)
    26. 26)
    27. 27)
      • 27. Smith, S.W.: ‘Digital signal processing: a practical guide for engineers and scientists’, 3rd revise. (Newnes, 2002), p. 668.
    28. 28)
      • 28. Atmel: ‘ATmega256RFR2 Xplained pro evaluation kit’. Available at:
    29. 29)
      • 29. ZigBee Alliance: ‘ZigBee standards’. Available at:, accessed 13 May 2014.
    30. 30)
      • 30. IEEE 802.15.4 Standards: ‘Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs)’, 2003. Available at:, accessed May 2014.
    31. 31)
      • 31. Atmel: ‘ATmega256RFR2 datasheet - 8-bit AVR microcontroller with low power 2.4 GHz transceiver for ZigBee and IEEE 802.15.4’. Available at:
    32. 32)
      • 32. Atmel: ‘Atmel lightweight mesh’.
    33. 33)
      • 33. Atmel: ‘Lightweight mesh developer guide’, 2014.

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