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

arXiv: https://arxiv.org/abs/2006.01232 · HTML: https://mlsanity.com/paper/2006.01232/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence](https://mlsanity.com/paper/2103.15990/) (2021)
- [Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches](https://mlsanity.com/paper/2105.06295/) (2021)
- [Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers](https://mlsanity.com/paper/2106.15017/) (2021)
- [Walk4Me: Telehealth Community Mobility Assessment, An Automated System for Early Diagnosis and Disease Progression](https://mlsanity.com/paper/2305.05543/) (2023)
- [Controllable Response Generation for Assistive Use-cases](https://mlsanity.com/paper/2112.02246/) (2021)
- [MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning](https://mlsanity.com/paper/2408.04243/) (2024)

## Abstract (arXiv)

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.