Edge of Medical Things Implementation for Deep Learning-Based Cognitive Task Recognition

Abstract

The emergence of edge devices for the Internet of Medical Things (IoMT) has enabled the integration of low-power resource-limited hardware units with communication technologies for smart healthcare applications including, brain-computer interface (BCI) and mental health management. Implementation of BCI systems on edge devices can provide high clinical decision accuracy with low latency and power consumption while reducing cloud-based training and communication. Hence, this article presents the edge of medical things (EoMT) implementation for cognitive task recognition (CTR) from electroencephalogram (EEG) signals in portable BCI systems. The proposed implementation exploits deep learning-based CTR on different computing platforms for edge devices including Arduino Due and Portenta H7 micro-controllers, Intel Movidius Neural Compute Stick 2 (NCS2), and Mimas v2 Spartan 6 field-programmable gate array (FPGA) board, along with IoMT communication technologies toward transmission of CTR information to caregivers. The real-time CTR performance results on different public and self-recorded databases indicate that the EoMT implementation scheme achieves high accuracies of 99.30 percent and 99.02 percent for public databases, and 96 percent and 89.50 percent for self-recorded databases, with low power consumption and latency, which makes it suitable for the IoMT context.