Robust Modelling of an Artificial Intelligence Assisted Brainwaves based Robotic Motion Control System using Internet of Things and EEG Signals

Large-scale customization is becoming more apparent in consumer markets. More complicated products with frequent modifications to its variants necessitate new assembly procedures. Close cooperation between human workers and robots is an idea to tackle issues within this trend. At now, there are limitations and hurdles that prohibit close cooperation in an effort to safeguard human operators. This occurs because safety systems aren’t very good at predicting future actions or intentions; they mostly respond to potential dangers. Predicting human behavior is still quite difficult, although there are probabilistic models that try to get around these problems. It would be ideal, then, to be able to physically gauge future movement intentions. The article introduces a new method for measuring the intention to move the upper limbs using a portable electroencephalogram (EEG). Up to 0.7 seconds before to executing a motor activity, the human brain continually assesses and evaluates it. This suggests that a safety system might benefit from early movement warning capabilities. A new data processing approach, Brainwave based Robotic Motion Control (BRMC), is presented to quickly categorize EEG-signals with little fine-tuning work. To assess the efficacy of the suggested scheme, it is cross-validated with the traditional model known as Mobile based Robotic Motion Control (MRMC) and is based on the logic of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. Furthermore, we discovered that a suggested model named BRMC can distinguish between signals that include blinks and ones that do not. Additionally, we discovered that BRMC can classify blink counts, and that various blink counts are utilized for forward, left, and right movement. The engine is slowed down by a model that has been taught to distinguish between blink signals and those that do not. According to the results of the experiments, the prototype can be controlled to go to a location that has already been selected by just blinking an eyelid and using a single electrode commercial headset.