Smart Blind Stick using Raspberry Pi

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Smart Blind Stick using Raspberry Pi

A brain-computer interface (BCI) is a new communication channel between the human brain and a digital computer. The ambitious goal of a BCI is finally the restoration of movements, communication, and environmental control for handicapped people. An electroencephalogram (EEG) based brain-computer interface was connected with a Virtual Reality system in order to control a smart home application. It offers an alternative to natural communication and control. It is an artificial system that bypasses the body’s normal efficient pathways, which are the neuromuscular output channels.

Different brain states are the result of different patterns of neural interaction. These patterns lead to waves characterized by different amplitudes and frequencies. This neural interaction is done with multiple neurons. Every interaction between neurons creates a minuscule electrical discharge. This project deals with the signals from the brain. Different brain states are the result of different patterns of neural interaction. These patterns lead to waves characterized by different amplitudes and frequencies. The signal generated by the brain was received by the brain sensor and it will divide into packets and the packet data is transmitted to a wireless medium (blue tooth). the wave measuring unit will receive the brain wave raw data and it will convert it into a signal using the MATLAB GUI platform. Then the instructions will be sent to the home section to operate the modules (bulb, fan). The project operated with human brain assumption and the ON/OFF condition of the home appliance is based on changing the muscle movement with blinking. Smart Blind Stick using Raspberry Pi

Smart Blind Stick using Raspberry Pi


A Brain-Computer Interface (BCI) is a new communication channel to capture human thoughts. By using BCI it has become possible to link brain activity to the operation of computers and devices and hence creating a direct communication channel between mind and machine. The BCI technology can be used by disabled people to improve their independence and maximize their capabilities at home. This project is designed to help disabled people to control the appliance with an increased inaccuracy.

This paper presents a new architecture for home device control systems via thoughts. Such a system will be useful for people who are suffering from paralysis or a similar condition, and who have limited movement. For disabled people, controlling a household device such as light, fan air-condition, etc., will be difficult without any assistance. Smart Blind Stick using Raspberry Pi

Smart Blind Stick using Raspberry Pi

Existing system:

  • Voice command is the most used technology for Home Automation
  • It can’t be used in a noisy environment
  • The system accuracy is high in a closed environment. But actual Accuracy in daily life is very low.
  • The Voice system is irritating and ineffective

Smart Blind Stick using Raspberry Pi

Proposed system:

The proposed approach helps people physically disabled to control home appliances using Electroencephalogram signals (EEG). For home automation, identifying and locating the user and calculating their viewing angle are needed. The proposed approach has mainly four modules and is given below.

  • Image acquisition and feature extraction
  • Calculating the user’s viewing angle
  • Capturing and identifying the EEG signal
  • Device control


  • Allow paralyzed people to control prosthetic limbs with their limbs
  • Transmit visual image to the mind of a blind person
  • Transmit auditory data to the mind of a deaf person
  • Allow persons to control home automation



  1. Mind Wave Mobile Or  Brainsense
  2. Laptop or Pc
  3. Arduino Uno/Mega
  4. Bluetooth Module ( HC-05 )
  5. USB Cable for Arduino
  6. Bluetooth dongle, if your pc does not have internal Bluetooth
  7. Relay Module
  8. Home appliances



  • MATLAB 2013b (32 bit
  • Arduino


  • X. Gao, D. Xu, M. Cheng, and S. Gao, A BCI-based environmental controller for the motion-disabled,? IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, pp. 137? 140, June 2003.
  •  C. Zickler , V. Di Donna, V. Kaiser, A. Al-Khodairy , S. Kleih, A. Kubler, M. Malavasi, D. Mattia, S. Mongardi, C. Neuper, et al., ?By applications for people with disabilities: dening user needs and user requirements,? Assistive technology from adapted equipment to inclusive environments, AAATE, vol. 25, pp. 185? 189, 2009.
  •  S. P. Levine, J. E. Huggins, S. L. Bement, R. K. Kushwaha, L. A. Schuh, M. M. Rohde, E. A. Passaro, D. A. Ross, K. V. Elisevich, and B. J. Smith, A direct brain interface based on event-related potentials,? IEEE Transactions on Rehabilitation Engineering, vol. 8, pp. 180? 185, Jun 2000.

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