Abstract
The passage of electric signals throughout the human body produces an electromagnetic field, known as the human-biofield, carries information about a person's psychological health. The human biofield can be rehabilitated by using healing techniques like sound therapy, and many others in smart grid. However, psychiatrists, and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results as a novel. The present research is based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization-interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on prediction and overall satisfaction. The participants were strongly satisfied with the prediction level (~81%) and future recommendation level (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In future, experimentation using different therapies, and integrating more advanced techniques are anticipated to open a new gateways in healthcare.