Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging
This work provides a new dataset and method for computer vision researchers to analyze the healthiness of users' lifestyles, addressing a gap in existing lifelogging datasets.
This paper addresses the lack of suitable datasets for lifestyle analysis in computer vision by introducing the Visual Life Logging Dataset for Lifestyle Analysis (VLDLA). The VLDLA dataset enables the classification of user activities and the measurement of a user's lifestyle healthiness through three time-varying latent fluents associated with these activities.
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.