Development of An Adaptive Staircase System Actuated by Facial-, Object-, and Voice-Recognition
Alexander Liu Cheng (Universidad Internacional SEK Ecuador, TU Delft - Architectural Engineering)
Patricio Cruz (Escuela Politecnica Nacional)
Wilson Guachamin (Escuela Politecnica Nacional)
Carlos Cevallos (Escuela Politecnica Nacional)
Benito Ribadeneira (Escuela Politecnica Nacional)
Esteban Ortiz (Escuela Politecnica Nacional)
Nestor Llorca Vega (Universidad Internacional SEK Ecuador, Universidad de Alcalá)
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Abstract
This paper details a proof-of-concept development of an adaptive staircase system-type capable of user-specific mechanical reconfigurations actuated by facial-, object-, and voice-recognition. The system is described via two variation-prototypes - developed at Technology Readiness Level 4 - as instances of the same system-type. Accordingly, each prototype is informed by the same use-case considerations and requirements. Nevertheless, by means of their mechanical particulars, advantages and disadvantages specific to each variation are identified and explored. The present adaptive staircase system-type consists of two main components, one computational and the other mechanical. The computational component is built upon an inherited System Architecture previously developed and implemented by the authors. More specifically, the computational component uses Google's TensorFlow for facial-recognition; BerryNet for multi-object detection; and VoiceIt for voice-recognition. These three cloud-compatible, -based, or -dependent recognition mechanisms are used to ascertain the identity three user-types: (1) a person without perceivable physical disabilities; (2) a person reliant on a walking-cane; and (3) a person on a wheelchair. With the exception of the first case, the computational component proceeds to actuate mechanical transformations pertinent to each variety of disabilities depending on which user-type is identified. The objective of this implementations is to present an intuitive and automated vertical mobility solution capable of supporting users with varying degrees of reduced mobility.