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Mind Controlled Wheelchair

A wheelchair, we can imagine that a chair it can be move with the help of wheels, it can be used for the person who have difficulty of walking or Impossible due to disability , injury, or illness.

Price : 14000

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Course Duration
Approx 12

Course Price
₹ 14000

Course Level
Advanced

Course Content

Smart wheelchairs play a significant role in supporting disabled people. Individuals with motor function impairments due to some disorders such as strokes or multiple sclerosis face frequent moving difficulties. Hence, they need constant support from an assistant. This paper presents a brain-controlled wheelchair model to assist disabled and paralyzed patients. The wheelchair is controlled by interpreting Electroencephalogram (EEG) signals, also known as brain waves. In the EEG technique, an electrode cap is positioned on the user’s scalp to receive EEG signals, which are detected and transformed by the Arduino microcontroller into motion commands, which drive the wheelchair. The proposed wheelchair is implemented using an Arduino-based robot controlled by a human brain wave using a Brain-Computer Interface (BCI). The human brain wave is captured using a low-cost Neurosky MindWave headset. The proposed wheelchair has the potential to have an immense effect on the healthcare industry. The use of this braincontrolled wheelchair can improve the quality of life of a paralyzed patient.

 Introduction Paralysis is a medical condition that severely restricts the movement of a person. It typically happens because of a nervous system injury. Strokes (29%), spinal cord injuries (23%), and multiple sclerosis (17%) are the leading causes of paralysis. Paralyzed people face many difficulties. About 16 million people in Bangladesh are paralyzed to some extent. It is a major challenge to develop a technology that would provide maneuvering support to a fully paralyzed person. Smart wheelchairs may assist disabled people in maneuvering. Maneuvering support is essential to make them independent. It also reduces the required care from family members. Smart wheelchairs integrate with a range of humanoid computer interface technologies, such as speech, head motion, Electromyography (EMG), wrist motion, and EEG, in addition to self-directed course-plotting, obstacle escaping, and additional features. 

Less than 5% of power wheelchair users use very advanced control mechanisms such as eye contact or tongue pad interfaces (perhaps as few as 1%) . Madrasasetal. suggested one of the primary illustrations of independent wheelchairs, fitting a wheelchair with a visualization system and utilizing sonar to locate paths and correct the wheelchair’s direction in hallways. Another solution with NavChair was proposed by Levinetal., a power-driven wheelchair outfitted with an obstacle escaping procedure and several methods to monitor movements over entranceways or prevent wall collisions. Monte-sano et al. proposed an independent wheelchair for children with cerebral disabilities, having complicated steering states to assist in tight entranceways and occupied or messy situations.

The Vector Field Histogram (VFH) approach was formerly established to avoid obstacles by robots and was later ported to the wheelchair. The NavChair has a navigation structure planned to sidestep obstacles, track walls, and enable movement without risk. For the self-localization of a motorized wheelchair, current ceiling lights in an enclosed setting are used as mapping symbols. Therefore, the chair is limited to one house, whose arrangement is encoded in the development of the wheelchair. “Wellesley” is the name given by Holly Yanco, first at Wellesley College and now at Massachusetts Institute of Technology (MIT), to the chair used for investigational progress. This chair has an integral method to its success that is close to a subsumption architecture, a control architecture that couples sensory information to action selection. Using a graphical interface, the chair user points in the direction in which the chair should head.

The problems discussed in other studies can be overcome by designing a special wheelchair controlled by a person’s brain waves to move voluntarily. Different states of the brain are the product of various nervous activity patterns. These patterns drive waves categorized by their amplitudes and frequencies, such as beta waves—concentration-related waves between 12 Hz and 30 Hz. In contrast, alpha waves, between 8 Hz and 12 Hz, are linked to a calm mental position.

Conclusion This paper presents a prototype of a smart brain-controlled wheelchair for paralyzed patients. The prototype of the wheelchair has been implemented using NeuroSky technology. This proposed wheelchair will potentially have an immense effect on the healthcare industry and the lives of disabled people. Some extra features can be added in the future to improve the functionality and practicality of the wheelchair. Obstacle detection using an ultrasonic sensor can be implemented. A patient health monitoring system can also be added to the wheelchair. If an emergency occurs, a notification will be sent to those responsible for the patient. Real-time Internet of Things (IoT)-based patient monitoring devices can be integrated with the wheelchair to monitor the user’s physiological parameters such as heart rate device, Spo2 level, ECG, and temperature. A provision to send Short Message Service (SMS) alerts can be added for guardians in case of wheelchair malfunctions due to mechanical problems or accidents.

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