Deep Learning Brain Actuated Bidirectional Communication and Rehabilitation aid for the Quadriplegic

The Brain controlled technology is the new revolution for aiding and rehabilitating the quadriplegic. Since the brain signals are dynamic in nature, it is difficult to accurately track the device's functioning without error. The bidirectional communication between the quadriplegic person must be established to evaluate and correct the feedback. The existing exoskeletons design bring frustration and difficulty to control for both the patient and the assistance. To solve these issues, Deep Learning Brain Actuated Bidirectional Communication (DLBABC) system is proposed. The system aims at alerting the caregivers with messages in an emergency situation by human intervention. The training in offline mode for specific movement and online mode using DLBABC is carried out to set the bidirectional communication with the assistant. The gesture with correct posture is analyzed and is converted into speech signal, allowing the quadriplegic to communicate with caregivers and friends.