An Intelligent Internet of Medical Things-Based Wearable Device for Monitoring of Neurological Disorders

In general, epilepsy is considered to be one of most prevalent neurological disorders and frequently appears as sudden seizures resulting in injuries, accidents, sudden unexpected death, etc. Also, it is reported that around 60 million people across the globe are experiencing various seizures due to epilepsy. So, there is demand for ambulatory seizure detection devices to prevent such accidents and to improve the quality of life for epilepsy patients. In this work, an intelligent Internet of Medical Things (IoMT)-based wearable device is designed and developed to monitor seizures in epilepsy patients. Due to the lack of an accelerometer dataset for epileptic seizures, the proposed device was developed, and a dataset mimicking seizure-like activities was generated. Further, the proposed device utilizes an MPU6500-based inertial measurement unit (IMU) which is integrated into an ESP32 microcontroller board. The ESP32 has a built-in wireless fidelity (WiFi) + Bluetooth (BLE) un that supports MicroPython v1.22.1 programming. Also, the machine learning algorithms such as Decision Trees (DT), Support Vector Machines (SVM), and Random Forest (RF) were programmed using MicroPython v1.22.1 programming and deployed on a tiny edge computing device to monitor the activity of the epileptic patients. All the adopted machine learning algorithms were compared in terms of performance metrics such as accuracy, precision, recall, false positive rate (FPR), etc., and the efficacy of the device was analysed. Results demonstrate that the proposed device is capable of identifying the activities of individuals, which is highly useful for epilepsy patients to monitor epileptic seizures. Furthermore, the proposed device was deployed with an RF algorithm since it exhibits an accuracy of 95% which is better compared to other machine learning algorithms. Also, the proposed device is simple and cost-effective and, in the event of a seizure event, can alert caretakers of epilepsy patients with an FPR of less than 4%.