Gesture Controlled Home Automation

Description

Gesture-Controlled Home Automation

Abstract:

A Human-Computer Interaction (HCI) between computers and human understands human language and develop a user-friendly interface. Gestures a non-verbal form of communication provide the HCI interface. The goal of gesture recognition is to create a system that can identify specific human gestures and use them to convey information or for device control. Hand gesture recognition is relatively complicated since different persons have different speeds and styles to perform gestures. Hand gesture recognition is suffering from the accuracy of hand detection. Many algorithms are proposed for gesture recognition accuracy. I proposed one approach; that also increases the accuracy of hand gesture detection. In the proposed approach OpenCV library is used to solve the problem of accuracy. In that approach background, subtraction is used for better recognition of the hand from the frame and increases the accuracy rate of hand recognition. This can be implemented in a PC and this can be connected to an Arduino board for device control, this can be done with help of IoT. Here we use the MQTT protocol for transferring data from the PC to the node MCU board. This way this system can be implemented cost-effectively.  Gesture Controlled Home Automation.


Gesture-Controlled Home Automation

Existing system:

  • Conventional switchboard 
  • Remote control 
  • Automatic control 

Gesture-Controlled Home Automation

Proposed system:

  • Gesture-based device controlling 
  • Camera-based gesture recognition 
  • OpenCV is used for gesture recognition 
  • Arduino platform is used for controlling devices 

Gesture-Controlled Home Automation

Block diagram:

 

Gesture Controlled Home Automation
Gesture-Controlled Home Automation


Hardware required :

  • NODEMCU
  • RELAY 

Software required:

  • Python IDLE
  • ARDUINO IDE

References:

[1] D. T. Wade, R. Langton-Hewer, V. A. Wood, C. E. Ski beck, and H. M. Ismail, “The hemiplegic arm after stroke: measurement and recovery,” J. Neurol. Neurosurg. Psychiatry, vol. 46, pp. 521–524, 1983. 

[2] J. P. Giuffrida, A. Lerner, R. Steiner, and J. Daly, “Upper-Extremity Stroke Therapy Task Discrimination Using Motion Sensors and Electromyography. Neural Systems,” Neural Syst. Rehabil. Eng. IEEE Trans., vol. 16, no. 1, pp. 82–90, 2008. 

[3] S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers, “A review of wearable sensors and systems with application in rehabilitation,” J. Neuroeng. Rehabil., vol. 9, no. 1, p. 21, 2012. 

[4] B. H. Dobkin and A. Dorsch, “The promise of mHealth: Daily activity monitoring and outcome assessments by wearable sensors,” Neurorehabil. Neural Repair, vol. 25, no. 9, pp. 788–798, 2011. 

[5] A. H. Al-Timmy, R. N. Khushaba, G. Bugmann, and J. Escudero, “Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 6, pp. 650–661, 2016. 

[6] D. Farina et al., “The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no

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