Early Implementation Experience with Wearable Cognitive Assistance Applications

Chen, Zhuo; Jiang, Lu; Hu, Wenlu; Ha, Kiryong; Amos, Brandon; Pillai, Padmanabhan; Hauptmann, Alex; Satyanarayanan, Mahadev

doi:10.1145/2753509.2753517

Cited by 45 publications

(38 citation statements)

References 19 publications

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“…Furthermore, the video guides for more complex tasks need to have strategies to help the user better follow the workflow, as demonstrated in the case of the sewing machine task. Recent work has proposed mechanisms to model and keep track of the workflow [16,13,12,1]. One approach, for example, that would improve this issue, is a mechanism to evaluate the user's actions and workflow and highlight any missing steps.…”

Section: Discussionmentioning

confidence: 99%

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Automated capture and delivery of assistive task guidance with an eyewear computer

Leelasawassuk

Damen

Mayol-Cuevas

2017

Proceedings of the 8th Augmented Human International Conference

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. ABSTRACTIn this paper we describe and evaluate an assistive mixed reality system that aims to augment users in tasks by combining automated and unsupervised information collection with minimally invasive video guides. The result is a fully self-contained system that we call GlaciAR (Glass-enabled Contextual Interactions for Augmented Reality). It operates by extracting contextual interactions from observing users performing actions. GlaciAR is able to i) automatically determine moments of relevance based on a head motion attention model, ii) automatically produce video guidance information, iii) trigger these guides based on an object detection method, iv) learn without supervision from observing multiple users and v) operate fully on-board a current eyewear computer (Google Glass). We describe the components of GlaciAR together with user evaluations on three tasks. We see this work as a first step toward scaling up the notoriously difficult authoring problem in guidance systems and an exploration of enhancing user natural abilities via minimally invasive visual cues.

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Section: Discussionmentioning

confidence: 99%

“…A related system to ours is the Gabriel system [16] which also uses Glass to guide users on tasks. In that case tasks are 2D such as assembling figures with colored blocks or drawing.…”

Section: Guidance and Authoringmentioning

confidence: 99%

Automated capture and delivery of assistive task guidance with an eyewear computer

Leelasawassuk

Damen

Mayol-Cuevas

2017

Proceedings of the 8th Augmented Human International Conference

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“…Chen et al [31] propose a cognitive assistance application using Google Glass with cloudlet. In their implementation, named "Gabriel," the glass is used as input device of viewing (video), position (GPS), and acceleration information of the user.…”

Section: Augmented Realitymentioning

confidence: 99%

Multi-access Edge Computing: A Survey

Tanaka

Yoshida

Möri

et al. 2018

Journal of Information Processing

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Multi-access Edge Computing (MEC) can be defined as a model for enabling business oriented, cloud computing platform within multiple types of the access network (e.g., LTE, 5G, WiFi, FTTH, etc.) at the close proximity of subscribers to serve delay sensitive, context aware applications. To pull out the most of the potential, MEC has to be designed as infrastructure, to support many kind of IoT applications and their eco system, in addition to sufficiently management mechanism. In this context, various research and standardization efforts are ongoing. This paper provides a comprehensive survey of the state-of-the-art research efforts on MEC domain, with focus on the architectural proposals as infrastracture, the issue of the partitioning of processing among the user devices, edge servers, and a cloud, and the issue of the resource management.

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“…Reseachers at CMU have set out a vision for angel on your shoulder cognitive assistance that takes the familiar models of detailed directions given by current GPS systems and applies them to everyday living [3]. Such systems combine wearable sensing and presentation with local processing in order to guide users through complex tasks, telling them what to do next (with audio-visual cues) and correcting any erroneous actions.…”

Section: Cognitive Assistance For Task Completion and Decision Supportmentioning

confidence: 99%

“…To address these challenges, the Gabriel platform [3] uses a cloudlet computing infrastructure [15] in which each cognitive process (e.g. face recognition, motion classification) is encapsulated in its own virtual machine (VM).…”

Section: Cognitive Assistance For Task Completion and Decision Supportmentioning

confidence: 99%