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.
