BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach

TL;DR

This paper presents BWCNN, a real-time AI system using CNNs to interpret eye-blinks for communication in ALS patients, achieving high accuracy and low latency on IoT devices.

Contribution

It introduces a CNN-based system optimized for real-time blink pattern recognition to assist ALS patients, demonstrating the effectiveness of InceptionV3 architecture.

Findings

InceptionV3 achieved 99.20% accuracy.

System latency was reduced to 94ms.

The approach is suitable for clinical IoT applications.

Abstract

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.