Searched for: subject%3A%22activity%22
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document
Liu Cheng, Alexander (author)
Discussions of intelligence in the built-environment began in the late 1960s and early 1970s [1]–[7]. They belonged to a broader technical and technological discourse, engaged across a variety of domains and disciplines, to explore potential opportunities entailed by the Information Age. During this nascent period, and partly due to the novelty...
doctoral thesis 2023
document
Liu Cheng, Alexander (author), Cruz, Patricio (author), Guachamin, Wilson (author), Cevallos, Carlos (author), Ribadeneira, Benito (author), Ortiz, Esteban (author), Vega, Nestor Llorca (author)
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,...
conference paper 2019
document
Liu Cheng, Alexander (author), Vega, Nestor Llorca (author), Latorre, Galoget (author), Coba, Daniel (author)
This paper presents a context-Aware light-Tracking and-redirecting system guided by hand-gesture recognition. It is conceived as yet another mechanism within an ongoing development of a more intuitive and technically sophisticated Ambient Intelligence / Active and Assisted Living ecosystem. The detailed system consists of individual nodes...
conference paper 2019
document
Liu Cheng, Alexander (author), Llorca, N. (author), Latorre, G. (author)
This paper details the development of an open-source eye- and gaze-tracking mechanism designed for open, scalable, and decentralized Active and Assisted Living (AAL) ecosystems built on Wireless Sensor and Actuator Networks (WSANs). Said mechanism is deliberately conceived as yet another service-feature in an on-going implementation of an...
conference paper 2019
document
Liu Cheng, Alexander (author)
This essay promotes Artificial Intelligence (AI) via Machine Learning (ML) as a fundamental enabler of technically intelligent built-environments. It does this by detailing ML’s successful application within three deployment domains: (1) Human Activity Recognition, (2) Object as well as Facial-Identity and -Expression Recognition, and (3) Speech...
journal article 2018
Searched for: subject%3A%22activity%22
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