Deep Learning Framework For Internet Of Things For People With Disabilities

摘要

Many people around the world suffer from neurological impairments and face many difficulties in performing their routine daily tasks. Caregiver services can become exhausted, expensive, and depressing for the patient over time. In the last few decades, numerous research is conducted on developing BCIs that can help a neurological impaired individual to communicate and functionally rehabilitate. Advances in the field of the Internet of Things (IoT) has opened the door for new opportunities in smart assistive technologies, like smart home, elderly care, or robotic applications. In this paper, we present an IoT-based non-invasive brain-computer interface (BCI) system. This system uses electroencephalography (EEG) signals to help severely paralyzed and locked-in patients regain the ability to do a simple daily task, such as locking/unlocking the door, turning on/off the lights or TV, and communicate with others, caregivers or healthcare providers, by sending text notifications. For our BCI, we have also built a deep learning classifier to understand the patient intent and trigger the corresponding workflow on connected IoT devices. This model has achieved a global accuracy of 72.82% for 103 subjects and 93.33% subject specific transfer learning for 10 subjects on the EEG Motor Movement/Imagery (MI) dataset [1] for MI tasks for 2 classes. Our BCI is also integrable with EEG headsets (like Emotiv) and IoT devices like Google Home and Alexa.