  Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative
disease of the brain and the spinal cord, which leads to paralysis of motor
functions. Patients retain their ability to blink, which can be used for
communication. Here, We present an Artificial Intelligence (AI) system that
uses eye-blinks to communicate with the outside world, running on real-time
Internet-of-Things (IoT) devices. The system uses a Convolutional Neural
Network (CNN) to find the blinking pattern, which is defined as a series of
Open and Closed states. Each pattern is mapped to a collection of words that
manifest the patient's intent. To investigate the best trade-off between
accuracy and latency, we investigated several Convolutional Network
architectures, such as ResNet, SqueezeNet, DenseNet, and InceptionV3, and
evaluated their performance. We found that the InceptionV3 architecture, after
hyper-parameter fine-tuning on the specific task led to the best performance
with an accuracy of 99.20% and 94ms latency. This work demonstrates how the
latest advances in deep learning architectures can be adapted for clinical
systems that ameliorate the patient's quality of life regardless of the
point-of-care.
