Digital Solution For Physically Challenged People

Categories: Society

In the global population 10% are physically challenged and 1.9% are paralysed. As physically challenged, elderly and paralysed people has a problem with their body that makes it difficult for them to do things that other people can do easily. Disabilities can affect a person's capacity to communicate, interact with others and being independent and lack self-confidence.

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Hence to overcome these problems some systems were previously developed such as voice-controlled and Hand gesture controlled wheel chair. But the existing systems are not satisfactorily applicable to paralyzed and physically challenged people.

Therefore a system is being proposed with the use of brain wave sensor. Our proposed system helps the person to do work independently and helps them to behave like a normal life in fulfilling their basic needs and also helps them to lead a safe life. In our project we control the multiple modes by using these commands. The intention of the project is to develop a system that can assist the physically challenged, elder and paralysed people in their daily life to do some work independently of others using the Brain-Computer interface (BCI) system.

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Keywords: Brain wave sensor, Raspberry Pi 3 controller, Brain-Computer Interface (BCI) system, Paralysed people, Physically challenged people.


Being disabled shouldn't mean being disqualified from having access to each side of life. According to census 2011, In India there are 2.68 crores and 9,585,635 are physically challenged and paralysed people. In our society, individuals with disabilities are faced with stigma and discrimination. This affects a person's capacity to communicate, interact with others and being independent and lack self-confidence.

To overcome this problem some systems were previously developed to assist the disabled, paralysed and elderly people. In the existing system they had disadvantages like people have to depend on others to operate, there were multiple electrodes in brain wave sensor in order to analyse the wave signals and home appliances can be controlled using voice control. In this paper we proposed a system, the raw data from the brain wave sensor will be sent to the Raspberry pi which will process the raw data and perform the corresponding action such as operating relay to control the home appliances as well as helper robot which is used to provide food and water in two different mode based on the signals received from the brain.

The brainwave sensor is the main component used in this Proposed system. Brain is home to approximately 100 billion neurons, nerve cells that allow you to react to signals. When a group of neurons experience this change in electrical impulse, they generate an electrical field which resembles a small vibration and which can be then detected on the scalp by an EEG sensors9. Brain wave speed is measured in Hertz (cycles per second) and they are divided into bands delineating slow, moderate and fast waves. The INFRA-LOW (.5HZ) waves are thought to be the basis cortical rythms that underlie our higher brain function. The DELTA WAVES (.5 to 3HZ) waves are generated in deepest meditation and dreamless sleep. The THETA WAVES (3 to 8HZ) waves occur most often in sleep but are also dominant in deep meditation. It is our gateway to learning, memory and intuition. The ALPHA WAVES (8 TO 12HZ) waves occur in flowing thoughts and meditative states. It aids mental coordination, calmness, alertness, mind/body integration and learning. The BETA WAVES (12 to 38HZ) waves are normal waking state of consciousness. It is a fast activity, present when we are, attentive, engaged in problem-solving, judgment, decision making, focused mental activity. The GAMMA WAVES (38 to 42HZ) waves are the fastest of brain waves (high frequency, like a flute) and pass information rapidly and quietly.

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 physically challenged people.

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 an electrical discharge. This paper dealing with the signals from brain. The signal generated by brain was received by the brain sensor and it will divide into packets and the packet data transmitted to wireless medium (Bluetooth). The wave measuring unit will receive the brain wave raw data and it will convert into signals in Raspberry pi 3. The system consists of two modes. In the first mode, the instructions will be sent to the home section to operate the modules (bulb, fan). The system operated with human brain assumption and the on-off condition of the home appliances is based on changing the muscle movement with blinking.

Literature Review

In the literature, enormous studies and analysis of smart solution for physically challenged, paralysed, elderly people has been carried out using brain wave sensor. Li Yingda and Yang Jianping1 illustrated a system by using Neuro sky headset for extraction of the brain wave signals and it has been sent to the android computing unit through wireless transmission and using hybrid algorithm the data have been processed and the command has been sent to the ARM controller for controlling the direction of wheelchair. Yashraj S. Soni4 demonstrated a system by using the brain sense headband. It consists of dry and reference electrode from that packet of data is sent to the PC where the raw data have been extracted and this data are used to control the touch panel input to the ARM Processor to get the required output in the LCD. Rohit Mankar,,7 considered that the brain waves have been extracted using the brain wave sensor and the raw data have been extracted by processing the brain waves in the MATLAB. The extracted data have been sent to the Arduino from the PC through USB cable. The command is used to control the LED blinking. Wei Tuck Le,6, illustrated by using EPOC headset with 14 channel electrodes which use three built-in suits (Expressive suite, Affective suite, cognitive suite) to determine signals based on the signals from EPOC headset, the mouse click for virtual home automation in GUI can be activated by the gyro . The above systems are proposed using neural networks for signal processing and the problems related to the above systems are that it can be accessed only for the limited instructions.

Proposed Methodology

To overcome the problems faced by the physically challenged, elderly, paralysed people in their daily life the following system has been proposed. The proposed system can be divided into three main section (1) Patient section; (2) Mode selection section; (3) Receiving section (Data processing unit).

Patient section

Brain wave sensor is used to record the electrical activity of the brain. The device consists of a headset, an ear-clip and a sensor arm. The headset's reference and ground electrodes are on the ear clip and electrode is on the sensor arm resting on the forehead above the eye. The brainwave sensor senses attention and meditation of the brain and eye blinks. Human brain consists of millions of interconnected neurons. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and transmitted through Bluetooth medium and thus raw data will be obtained.

Mode selection section

In our proposed system, RF transmitter acts as a switch and it contains three modes of selection. In first mode home appliances will be controlled using eye blink. In the second mode, the tray1 and tray2 can be lifted using attention, meditation and eye blink. In Third mode helper robot (motor drive1) can be controlled using eye blink. By using the above methodology many instructions can be performed with limited processing of eye blink, attention and meditation level.

  • RF transmitter
  • Encoder
  • Antenna
  • 9v battery


Fig 3 Block diagram of mode selection section

  • Receiving unit (Data processing unit)
  • Antenna
  • Decoder
  • RF receiver
  • Raspberry Pi 3
  • Motor drive2 L293D
  • Motor drive1 L293D
  • Relay2
  • Relay1
  • Fan
  • Light
  • Robot wheel2 2
  • 22
  • 22
  • Robot wheel1 1
  • Tray2
  • Tray1

Receiving section

Raspberry pi is an ARM-based credit card sized SBC (Single Board Computer). It runs Debian based GNU/LINUX operating system. It has onboard Wifi, SD card slot, Bluetooth support and a 64bit processor. Through which raw data has been processed in the Python3 IDE platform. Based on the signals processed home appliances like fan and light will be controlled with the help of relay and helper robot is also be controlled.

Experimental Setup And Result Analysis

Mind-controlled module interconnected with the brain using brain-sensing module.

Calibration output for different persons

Based on the concentration and eye blinking level of the person's raw signals has been generated by brain wave sensor by comparing the voltage range detected in the forehead electrode and the reference electrode in the brain . The extracted data is sent to the Raspberry Pi 3 through Bluetooth at the baud rate of 56700bps. The calibration of the Person's concentration and eye blinking is done in Raspberry Pi itself is shown in the figure 6. This kind of application is for paralyzed, elderly and physically challenged people to make them to lead an independent life. The calibrated value is varied for person to person is shown in the analysis table 1.

Table 1 Analysis of Concentration and eye blinking range for different persons.

  • Person Calibration value
  • Minimum value Maximum value
  • Person 1 300 1100
  • Person 2 400 1500
  • Person 3 900 2500

Output of the home appliance control

If he/she want to control the home appliance, the mode 1 has to be selected. Based on the level of concentration and eye blinking count as shown in the table 2, the corresponding home appliance has been controlled.

Table 2 Output produced in the Mode 1

  • Eye blinking count Output obtained
  • Blink 1 Light control
  • Blink 2 Fan control
  • Output of helper robot control

If he/she is in need of any basic things, the mode 2 and mode 3 has to be selected. Based on the level of attention, meditation and eye blink level of the person as shown in table 3 and 4., desired actions has been performed.

Table 3 Output produced in the Mode 2 Table 4 Output produced in the Mode 3

Blink count Output of movement obtained

  • Blink 1 Straight
  • Blink 2 Left
  • Blink 3 Right
  • Blink 4 Stop
  • Brain signal output Output obtained
  • Attention >20 Tray 1 is lifted
  • Meditation >20 Tray 2 is lifted


This system makes the life of the physically challenged, paralysed and elderly people sophisticated and increase the confidence level by making them independent in taking decision on their own. The results appended are acceptable, where the error rate has been reduced around 0.2 by using mode selection method compared to previous analysis. The execution time for each instruction take 1 to 5 seconds depending on the level of persons concentration. All the using tools are cheap expect the brain wave sensor which is somehow expensive. The quality of brain control depends on the level of concentration.

Future Work

In the future an additional smartness can be provided through IoT. The research and development of brain-controlled robot with artificial intelligence have received a great deal of attention because they can help the neuromuscular affected people to a quality life. Improving the BCI system performance to make brain-controlled robots usable in real-world situations.


  • Li Yingda, Yang Jianping, Intelligent Wheelchair based on Brainwave, International Conference on Intelligent Transportation, Big Data & Smart city- 2018.
  • Djemaa Boucha, Ayoub Amiri, Controlling Electronic Devices remotely by voice and brain wave, International Conference on Mathematics and information Technology, Adrar, Algeria December 4 - 5, 2017.
  • Sneha Pushpa, Chandrashekar, EEG based Brain computer interface for controlling Home Appliances, International Journal Of Innovative Research in Science, Engineering and Technology - Vol.5, Issue 6, June 2016.
  • Yashraj S.Soni, The Biometric user authentication using the brain waves, IEEE Transcation on information forensics and security-2015.
  • Ritika Pahuja, Narender Kumar, Android Mobile Phone Controlled Bluetooth Robot Using 8051 Microcontroller - 2014.
  • Wei Tuck Lee, Humaira Nisar, Aamir S.Malik, Kim Ho Yeap, A Brain-Computer Interface for Smart HomeControl, ISCE-2014.
  • Rohit Mankar, Pranav Bawane, Tushar Landge, Palash Dalai, Vivek Gour: Brainwave Sensing, INDICON-2014.
  • G.Paratizio Campisi, Daraia La Rocca, Brain waves for Automatic Bio-metric based user Recognition, IEEE Transaction on Information Forensics and Security, May 2014.
  • Rosslin John Robles and Tai-hoon Kim, Applications, Systems and Methods in Smart Home Technology: A Review, International Journal of Advanced Science and Technology-February 2010.
  • S.Baca, Cubrailo, Towards a general definition of Biometrics, International Journal of Computer science Issues, 2009.
Updated: Nov 01, 2022
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Digital Solution For Physically Challenged People. (2019, Dec 20). Retrieved from

Digital Solution For Physically Challenged People essay
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