Enhanced Safety Protocols for Dizziness Detection Using EEG SIGNALS

Drowsiness & dizziness are leading causes of traffic and industrial accidents, costing lives and productivity. The factors which include such activity are blink rate, heart rate variability, respiration, and brain activity. Since, excessive sleepiness, is more likely to happen when a person is d

Project Title

Enhanced Safety Protocols for Dizziness Detection Using EEG SIGNALS

Project Area of Specialization

NeuroTech

Project Summary

Drowsiness & dizziness are leading causes of traffic and industrial accidents, costing lives and productivity. The factors which include such activity are blink rate, heart rate variability, respiration, and brain activity. Since, excessive sleepiness, is more likely to happen when a person is driving for extended periods in monotonous environments or when workers in heavy industries work 5 to 6 hours without taking any rest. Similarly, Brain activity also fluctuates in this case causing crests and troughs in the waves. Both can be measured with physical sensors. However, as a subjective state, it is difficult to detect such dizziness or fatigue for some other person. For years, researchers have attempted to find an accurate and stable detection method. The U.S. National Highway Traffic Safety Administration reports that drowsy driving is the cause of an estimated 40,000 injuries and 1550 deaths in car crashes every year. Also, the Korean Expressway Cooperation reports that, from 2010 to 2013, 1223 people died in Korean highway traffic accidents, 31% of which could be attributed to driver drowsiness. Many of these deaths could be avoided if driver drowsiness could be properly monitored and drivers are given early warnings. By 2015, a new method based on computer vision and image processing was proposed to detect the fatigue experienced by anesthesiologist. Although many new methods exist for detecting fatigue, many challenges remain unsolved. The major challenges are as follows: First, methods were often impeded by physiological factors such as blinking or breathing. Second, reliable datasets are not available. Finally, methods often attempted to deploy the original image classification technique to analyze bio signals extracted using only EMG or Electroencephalogram (EEG). To enable the detection of drowsiness both simply and inexpensively, many methods have been proposed, video-based methods (such as the detector of the degree (percentage) of eyelid closure over the pupils over time), and physiological-signal-based methods (such as those based on the ratio of low frequency to high frequency of heart rate variability and EEG (brain waves)). Among these methods, physiological-signal-based methods are considered to be the most reliable means of detection as these signals provide an indication of the true internal state; and compared to other physiological signals, the EEG, that is a non-invasive physiological means of measuring brain activity, is considered to have the closest relationship with drowsiness and doesn’t involve too much of setup equipment’s.

Project Objectives

•Our Project will immensely improve the safety protocol and prove to be revolutionary resulting in saving lives.

•Runtime Monitoring of the person 

•Web or Mobile Application

•Instant Notification for Instant Action.

Project Implementation Method

Our project is about implementing a safety protocol for industry workers through Electroencephalography (EEG) signals processing mainly and heartbeat signals processing subsidiary by monitoring the dizziness level through them using certain threshold.

Using the said method, the safety procedure team would be monitoring the workers concentration level, dizziness through Electroencephalography (EEG Signals) and heartbeats. We will also be using a buzzer and a certain low level shock device in case the worker feels dizziness or drowsiness will operating the machinery or during work, which would help in getting them stay attentive for a while until a person from safety team arrives and offers break or help.

Benefits of the Project

• Enhanced Safety Protocols for Industry Workers, Drivers and Doctors.
• Reduction in Accidents.
• Efficent Monitoring Procedure.

Technical Details of Final Deliverable

• Dizzines & Drowsiness will be detected by two sources: EEG and ECG.
• In case of EEG, Muse is used which is the brain sensing headbend in which Signal is processed, Concentrations are measured and on the basis of that if the frequency at some specific interval is high or low, decision is made.
• In case of ECG, ECG sensing Kit is used which senses Heart beat and on the basis of that take decision.We have also applied Real-Time Cloud storage of the sensor data in this part.
• Also a low level shock or buzzer can be used for alertness.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Telecommunication

Other Industries

Medical , Health

Core Technology

NeuroTech

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Good Health and Well-Being for People, Decent Work and Economic Growth, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com