Smart Elevator with Unsupervised Learning for Visitor Profiling and Personalised Destination Prediction

摘要

Elevator manufacturing companies make great effort to provide better user experience and to reduce elevator waiting time. Deep learning for visitor profiling offers more options for personalised service and enables the elevator to learn about its usage. In this paper, we present a solution that integrates conventional elevator with facial recognition, voice assistant and unsupervised learning and discuss some insights gained during the development. In the 3-month testing period, we experienced different social reactions that help us to examine people’s readiness to accept new technologies in their daily life. The novelty of the solution lies in a combination of different cognitive technologies like facial recognition, unsupervised classification of persons, recognition of voice commands and statistics-based prediction of passenger destinations.