Voice-Controlled Interaction of Medical Robots Using Deep Learning with Gated Recurrent Units

Industry 4.0 has revolutionized the development of healthcare technology, especially during the COVID-19 pandemic. The development of robots and artificial intelligence (AI) has aided medical professionals in providing patient care. To enhance patient-robot interactions, whether for consultations or basic conversations, a robust communication system is essential. Therefore, a machine learning method capable of understanding and processing human language, Natural Language Processing (NLP), is required, with voice chatbots as its foundation. The data processing stages involve data filtering, speech-to-text conversion, chatbot interaction, and text-to-speech synthesis. The model was trained with five different test parameters and classified using the Gated Recurrent Unit (GRU) method. The results of the testing demonstrate that the method performs exceptionally well, achieving an accuracy rate of 96.77% and a success rate of 85.56%.