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• Crowd-AI Camera Sensing in the Real World

  Anhong Guo, Anuraag Jain, Shomiron Ghose, Gierad Laput, Chris Harrison, Jeffrey P. Bigham · 2018 · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

  This paper presents Zensors++, a hybrid crowd-AI camera sensing system that allows users to point a networked camera at a scene, define a natural language question about it (such as "Is the coffee machine in use?" or "How many people are in the room?"), and receive continuous,…

  crowdsourcing · computer vision · human computation · machine learning · smart environments

• Web Standards to Enable an Accessible and Inclusive Internet of Things (IoT)

  Shadi Abou-Zahra, Judy Brewer, Michael Cooper · 2017 · Proceedings of the 14th International Web for All Conference (W4A)

  Written by three W3C staff members, this paper argues that the Internet of Things (IoT) has the potential to disproportionately benefit people with disabilities — but only if accessibility is built in from the start. The authors examine IoT across three scales: the micro…

  Internet of Things · web standards · smart environments · assistive technology · interoperability

• Zensors: Adaptive, Rapidly Deployable, Human-Intelligent Sensor Feeds

  Gierad Laput, Walter S. Lasecki, Jason Wiese, Robert Xiao, Jeffrey P. Bigham, Chris Harrison · 2015 · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI 2015)

  Zensors presents a novel sensing platform that combines real-time human intelligence from online crowd workers with machine learning to create adaptive, rapidly deployable intelligent sensors. The system addresses a fundamental gap in smart environment technology: traditional…

  crowdsourcing · human computation · computer vision · smart environments · machine learning

• Real-Time Crowd Labeling for Deployable Activity Recognition

  Walter S. Lasecki, Young Chol Song, Henry Kautz, Jeffrey P. Bigham · 2013 · Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW 2013)

  Legion:AR is a system that provides deployable activity recognition by combining real-time crowd labeling with automatic recognition using Hidden Markov Models (HMMs). The system addresses a critical limitation of current activity recognition: automated systems must be trained…

  activity recognition · crowdsourcing · human computation · machine learning · aging in place

• Smart Rooms, Desks, and Clothes

  Alexander Pentland · 1998 · Proceedings of the Third International ACM Conference on Assistive Technologies (Assets '98)

  This keynote address from Assets '98, delivered by Alexander Pentland of MIT's Media Laboratory, outlines a vision for transforming everyday objects — rooms, desks, clothing, cars, and eyeglasses — into intelligent, perceptive systems that actively assist their users. Pentland…

  smart environments · wearable computing · perceptual computing · ambient intelligence · adaptive environments

• Augmenting Home and Office Environments

  Elizabeth Mynatt, Douglas Blattner, Meera M. Blattner, Blair MacIntyre, Jennifer Mankoff · 1998 · Proceedings of the Third International ACM Conference on Assistive Technologies (Assets '98)

  This panel paper brings together five researchers from Xerox PARC, CommunityVision Inc., UC Davis/Lawrence Livermore, Columbia University, and Georgia Tech to discuss how augmented environments — homes and offices enhanced with sensors, computing, and multimodal interfaces — can…

  home automation · augmented reality · ubiquitous computing · smart environments · multimodal interface

6 results.