Use Of ECG/ EOG/ EEG for the Purpose of Health Monitoring of Partially or Fully Paralyzed Patient and to Communicate with the Patient in Case of an Emergency
Abstract
It is never felt safe to leave a partially or fully disable person alone with a caretaker, it does not matter how much care is taken by the caretaker. In this paper, three different ways are described how the condition of a partially or fully disabled person can be monitored and in case of any problem, the partially or fully disabled person can communicate with his/ her family members. In the first way using Electrocardiography (ECG) the condition of the heart of the disabled person is continuously monitored and whenever something unwanted has happened a message is sent to his/her caring family member with the help of a microcontroller. This also can be done using Electrooculography (EOG). This can be applied to a partially paralyzed person. Here it is mentioned that the person is partially paralyzed because the EOG works on the movement of the cornea so the person should be capable of moving his/ her cornea. Now if the cornea of the person is bent over a set point a message is sent to a specific person. The same can be done with the help of EEG using brain signals. The brain signals are continuously monitored and in case of any unexpected or absurd signal message will be sent to the disabled person’s family member.
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References
Zakir Hussain, Ravindran, “Health Monitoring System for Comatose Patients: A Survey”, International Journal of Advance Research, Ideas and Innovations in Technology, volume 3, issue 6, 30 November 2017
Anand Kumar Joshi, Arun Tomar, Mangesh Tomar, “A Review Paper on Analysis of Electrocardiograph (ECG) Signal for the Detection of Arrhythmia Abnormalities”, volume 3, issue 10, October 2014.
Swati Vaid, Preeti Singh Chamandeep Kaur, “EEG signal analysis for BCI interface: a review”, pp. 143-147, doi: 10.1109/ACCT.2015.72
Shiliang Sun, Jin Zhou, "A Review of Adaptive Feature Extraction and Classification Methods for EEG-Based Brain-Computer Interfaces", IEEE Joint Conference (IJCNN) on Neural Networks International, pp. 1746-1753, 2014.
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