User Interaction Templates for the Design of Lifelogging Systems

Hopfgartner, Frank; Yang, Yang; Zhou, Lijuan; Gurrin, Cathal

doi:10.1007/978-1-4471-5301-6_10

Cited by 8 publications

(5 citation statements)

References 34 publications

(29 reference statements)

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“…[Lar13] propose quantified self (QS) Spiral an interactive visualization technique that aims to capture the quantified self-data and let the user explore those recurring patterns. [Hop13] present different visualization techniques like Comic-book style Visual diary inspired by the squarified treemap pattern, timeline, master detailed having the appearance of a thumbnail gallery, a social interaction radar graph and a activity yearly calendar. [Dua17] investigate in virtual reality by realizing a virtual reality lifelog prototype.…”

Section: Multimodal Visualizationmentioning

confidence: 99%

A new model driven architecture for deep learning-based multimodal lifelog retrieval

Abdallah¹,

Feki²,

Ammar³

et al. 2018

CSRN

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Nowadays, taking photos and recording our life are daily task for the majority of people. The recorded information helped to build several applications like the self-monitoring of activities, memory assistance and long-term assisted living. This trend, called lifelogging, interests a lot of research communities such as computer vision, machine learning, human-computer interaction, pervasive computing and multimedia. Great effort have been made in the acquisition and the storage of captured data but there are still challenges in managing, analyzing, indexing, retrieving, summarizing and visualizing these captured data. In this work, we present a new model driven architecture for deep learning-based multimodal lifelog retrieval, summarization and visualization. Our proposed approach is based on different models integrated in an architecture established on four phases. Based on Convolutional Neural Network, the first phase consists of data preprocessing for discarding noisy images. In a second step, we extract several features to enhance the data description. Then, we generate a semantic segmentation to limit the search area in order to better control the runtime and the complexity. The second phase consist in analyzing the query. The third phase which based on Relational Network aims at retrieving the data matching the query. The final phase treat the diversity-based summarization with k-means which offers, to lifelogger, a key-frame concept and context selection-based visualization.

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Section: Multimodal Visualizationmentioning

confidence: 99%

A new model driven architecture for deep learning-based multimodal lifelog retrieval

Abdallah¹,

Feki²,

Ammar³

et al. 2018

CSRN

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“…vast amount of visual data may need to be aligned to GPS data) compared to the numerical fitness data, and requires careful consideration on how to present and summarize such information. In relation to the user interaction in lifelogging, Hopfgartner et al [3] have outlined various design templates that may help to support different visualization schemes and perhaps invoke further research efforts in this direction. The authors have suggested that visual diaries should summarize a user's day, a comic-book style interface should be used when the screen space is limited (handheld devices) and a master-detailed interface should be used to facilitate lifelog image exploration.…”

Section: Background Workmentioning

confidence: 99%

“…Despite the growing number of studies using lifelogging such as with wearable cameras, there has been little consideration on how to visualize and present the lifelogs in a user-friendly yet meaningful way. A handful of the existing works [3,4] have proposed a number of design guidelines and visualization techniques to present the lifelog images, but they have not reported any user studies to evaluate the effectiveness of the proposed techniques. It is our conjecture that visualization of lifelog images is a necessary aspect, which must be considered, when designing lifelogging tools to enrich the user experience and support their needs.…”

Section: Introductionmentioning

confidence: 99%

A user-study examining visualization of lifelogs

Chowdhury

Ferdous

Jose

2016

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)

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With continuous advances in the pervasive sensing and lifelogging technologies for the quantified self, users now can record their daily life activities automatically and seamlessly. In the existing lifelogging research, visualization techniques for presenting the lifelogs and evaluating the effectiveness of such techniques from a lifelogger's perspective has not been adequately studied. In this paper, we investigate the effectiveness of four distinct visualization techniques for exploring the lifelogs, which were collected by 22 lifeloggers who volunteered to use a wearable camera and a GPS device simultaneously, for a period of 3 days. Based on a user study with these 22 lifeloggers, which required them to browse through their personal lifelogs, we seek to identify the most effective visualization technique. Our results suggest various ways to augment and improve the visualization of personal lifelogs to enrich the quality of user experience and making lifelogging tools more engaging. We also propose a new visualization feature-drill-down approach with details-ondemand, to make the lifelogging visualization process more meaningful and informative to the lifeloggers.

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“…In [8] Viégas and Wattenberg illustrate how such techniques can be seized by artists for artful designs, including project Last Clock, by Jussi Angesleva and Ross Cooper that is inspiring for the FlanoGraphs. In [9] Hopfgartner et al discuss user interface design cues for lifelogging applications. Such mobile augmented reality (MAR) applications yield at providing sensing, logging and displaying on the same device.…”

Section: Interfaces and Presentation Enginesmentioning

confidence: 99%

Scenarizing Metropolitan Views: FlanoGraphing the Urban Spaces

Jacobs¹,

Jacobs²,

Frisson³

et al. 2014

MultiMedia Modeling

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Abstract. The recent decade has seen a rapid evolution in the field of digital media. Mobile devices are now being integrated into every aspect of urban life. GPS, sensor technologies and augmented reality have transformed the new generation of mobile devices from a communication and information platform into a navigational tool, fostering new ways of perceiving reality and image building. Touch sensor technology has changed the screen into a joint input and display device. In this paper we present the FlanoGraph, an application for smartphones and tablets designed to take benefit of the changes induced by mobile devices. We first briefly outline the conceptual background, evoking the work of some researchers in the fields of 'Non Representational Theory', mobile media, and computational data processing. We then present and describe the FlanoGraph through a set of use cases. Finally, we conclude discussing some techniques necessary for the development of the application.

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User Interaction Templates for the Design of Lifelogging Systems

Cited by 8 publications

References 34 publications

A new model driven architecture for deep learning-based multimodal lifelog retrieval

A new model driven architecture for deep learning-based multimodal lifelog retrieval

A user-study examining visualization of lifelogs

Scenarizing Metropolitan Views: FlanoGraphing the Urban Spaces

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