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Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities
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| Zeitschriftentitel: | Scalable Computing: Practice and Experience |
|---|---|
| Personen und Körperschaften: | , |
| In: | Scalable Computing: Practice and Experience, 22, 2021, 2, S. 259-272 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Scalable Computing: Practice and Experience
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| Schlagwörter: |
| author_facet |
Ziyad, Shabana R Ziyad, Armaan Ziyad, Shabana R Ziyad, Armaan |
|---|---|
| author |
Ziyad, Shabana R Ziyad, Armaan |
| spellingShingle |
Ziyad, Shabana R Ziyad, Armaan Scalable Computing: Practice and Experience Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities General Computer Science |
| author_sort |
ziyad, shabana r |
| spelling |
Ziyad, Shabana R Ziyad, Armaan 1895-1767 Scalable Computing: Practice and Experience General Computer Science http://dx.doi.org/10.12694/scpe.v22i2.1901 <jats:p>Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.</jats:p> Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities Scalable Computing: Practice and Experience |
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| title |
Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
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Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
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Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
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Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
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Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
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Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
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emergency rapid response to epileptic seizures - a novel iot framework for smart cities |
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General Computer Science |
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http://dx.doi.org/10.12694/scpe.v22i2.1901 |
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2021 |
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259-272 |
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<jats:p>Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.</jats:p> |
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| description | <jats:p>Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.</jats:p> |
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| spelling | Ziyad, Shabana R Ziyad, Armaan 1895-1767 Scalable Computing: Practice and Experience General Computer Science http://dx.doi.org/10.12694/scpe.v22i2.1901 <jats:p>Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.</jats:p> Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities Scalable Computing: Practice and Experience |
| spellingShingle | Ziyad, Shabana R, Ziyad, Armaan, Scalable Computing: Practice and Experience, Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities, General Computer Science |
| title | Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
| title_full | Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
| title_fullStr | Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
| title_full_unstemmed | Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
| title_short | Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
| title_sort | emergency rapid response to epileptic seizures - a novel iot framework for smart cities |
| title_unstemmed | Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities |
| topic | General Computer Science |
| url | http://dx.doi.org/10.12694/scpe.v22i2.1901 |