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Visualizing Personal Lifelog Data for Deeper Insights @ NTCIR-13 Lifelog-2 Cognitive Vision Lab, Department of Visual Computing Institute for Infocomm Research (I 2 R), A*STAR We aim to generate and visualize lifelog in- sights under the NTCIR -13 Lifelog-2, Lifelog In- sight (sub) Task (LIT). Authors: Q.L. Xu ([email protected]), V. Subbaraju, A.G. del Molino, J. Lin, F. Fang, J-H Lim, L. Li, V. Chandrasekhar Features (Deep Learning) Activity Annotation Training (Retrieval) Cluster Aggregate Correlate Animate Logging Data Compare External Data Retrieval Insight Mining Ground truth by Annotation Visualize Browse Search Narrate Advise Provide insights into the diet and blood sugar levels of the lifeloggers. Diet Describe the exercise, sleep and physical activities of both lifeloggers Exercise Socialisation levels are a good indicator of the health of individuals Social Provide insights onto the location and movement patterns of the lifeloggers Where Comparison between two individuals across multiple dimensions Compare Aim Method Visualization Templates Mobile App User Interface Generate minute-wise annotation of the users’ activities. Generate insights of users’ activities according to a suite of templates. Build a prototype mobile app to vis- ualize the insights.
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Visualizing Personal Lifelog Data for Deeper Insights ...research.nii.ac.jp/ntcir/workshop/OnlineProceedings13/pdf/ntcir/05... · Having a balanced diet is good for your health, such

Jul 18, 2020

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Page 1: Visualizing Personal Lifelog Data for Deeper Insights ...research.nii.ac.jp/ntcir/workshop/OnlineProceedings13/pdf/ntcir/05... · Having a balanced diet is good for your health, such

Visualizing Personal Lifelog Data for Deeper Insights @ NTCIR-13 Lifelog-2

Cognitive Vision Lab, Department of Visual Computing Institute for Infocomm Research (I2R), A*STAR

We aim to generate

and visualize lifelog in-

sights under the NTCIR

-13 Lifelog-2, Lifelog In-

sight (sub) Task (LIT).

 

Authors: Q.L. Xu ([email protected]), V. Subbaraju, A.G. del Molino, J. Lin, F. Fang, J-H Lim, L. Li, V. Chandrasekhar

Features (Deep Learning)

Activity Annotation

Training (Retrieval)

Cluster

Aggregate 

Correlate

Animate

Logging Data

Compare

External Data

Retrieval Insight Mining

Ground truth by Annotation

Visualize

Browse 

Search

Narrate

Advise

• Provide insights into the diet and blood sugar levels of the lifeloggers.Diet

• Describe the exercise, sleep and physical activities of both lifeloggersExercise

• Socialisation levels are a good indicator of the health of individualsSocial

• Provide insights onto the location and movement patterns of the lifeloggersWhere

• Comparison between two individuals across multiple dimensions Compare

Aim

Method

Visualization Templates

Mobile App User Interface

Generate minute-wise annotation of

the users’ activities.

Generate insights of users’ activities

according to a suite of templates.

Build a prototype mobile app to vis-

ualize the insights.