Spatial Augmented Reality Based Customer Satisfaction Enhancement and Monitoring System

This research proposes a customer-interaction scheme that enhances customer-satisfaction through spatial-augmented-reality and deep-learning-based emotion-recognition for restaurant 4.0, by monitoring user-experience levels using facial expressions. The customer-satisfaction analytical-model provides environmental knowledge-based dynamic capabilities to the human-centered dining environment, which is also utilized as an input to logistics 4.0 supply chain management, business-processes, and decision-support-systems for future analytics. The perception of the quality of food was enhanced by the recreation of the live environment on the preparation of the menu ordered, utilizing the waiting time after each ordering session. The dishes are being portrayed in three-dimensional (3D) virtual menu also, adding SAR features in a special angle creating a 3D illusion to the naked eye. Menu suggestions are also proposed depending on the recommendations of the analytical model. The deep learning model monitors customer-satisfaction levels through emotion recognition. The performance was analyzed with numerous experimental evaluations with ambient light levels and desired viewing angles, where we found optimal angle values for spatial augmented reality applications. A survey was carried out to analyze user perception on evaluating the proposed system. The results revealed 87.5% of responders were very satisfied with the 3D virtual experience. 79.2% indicated rareness of feedback on food and service and more than 50% liked a system that will automatically measure their satisfaction. The results indicate enhanced customer-satisfaction levels compared to existing schemes. The resulting insights may lay a foundation for a novel approach towards the management of user-experience, business-processes, decision support systems, and supply chain management of restaurant 4.0.