Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging

TL;DR

This paper introduces a new visual lifelogging dataset tailored for lifestyle analysis, enabling the classification of daily activities and assessment of healthiness based on recognized behaviors from wearable camera images.

Contribution

The creation of VLDLA, a dataset with frequent, continuous images suitable for analyzing short-duration activities and lifestyle healthiness, filling a gap in current computer vision lifelogging research.

Findings

The method effectively classifies user activities from images.

The approach quantifies lifestyle healthiness using latent fluents.

Experimental results validate the analysis of healthy lifestyles.

Abstract

A healthy lifestyle is the key to better health and happiness and has a considerable effect on quality of life and disease prevention. Current lifelogging/egocentric datasets are not suitable for lifestyle analysis; consequently, there is no research on lifestyle analysis in the field of computer vision. In this work, we investigate the problem of lifestyle analysis and build a visual lifelogging dataset for lifestyle analysis (VLDLA). The VLDLA contains images captured by a wearable camera every 3 seconds from 8:00 am to 6:00 pm for seven days. In contrast to current lifelogging/egocentric datasets, our dataset is suitable for lifestyle analysis as images are taken with short intervals to capture activities of short duration; moreover, images are taken continuously from morning to evening to record all the activities performed by a user. Based on our dataset, we classify the user activities in each frame and use three latent fluents of the user, which change over time and are associated with activities, to measure the healthy degree of the user's lifestyle. The scores for the three latent fluents are computed based on recognized activities, and the healthy degree of the lifestyle for the day is determined based on the scores for the latent fluents. Experimental results show that our method can be used to analyze the healthiness of users' lifestyles.