Abstract
The human daily activity category represents individual lifestyle and pattern, such as sports and shopping, which reflect personal habits, lifestyle, and preferences and are of great value for human health and many other application fields. Currently, compared to questionnaires, social media as a sensor provides low-cost and easy-to-access data sources, providing new opportunities for obtaining human daily activity category data. However, there are still some challenges to accurately recognizing posts because existing studies ignore contextual information or word order in posts and remain unsatisfactory for capturing the activity semantics of words. To address this problem, we propose a general model for recognizing the human activity category based on deep learning. This model not only describes how to extract a sequence of higher-level word phrase representations in posts based on the deep learning sequence model but also how to integrate temporal information and external knowledge to capture the activity semantics in posts. Considering that no benchmark dataset is available in such studies, we built a dataset that was used for training and evaluating the model. The experimental results show that the proposed model significantly improves the accuracy of recognizing the human activity category compared with traditional classification methods.
Highlights
The knowledge about which activities and why people perform them at a certain time can be very valuable for identifying healthy lifestyles [1] and can be used in a wide range of applications.For example, doctors could better build the connection between patients’ habits and lifestyles and their health problems based on people’s daily activity patterns
Previous researches employed traditional classification methods [7,8,9], which ignore the contextual information or word order in posts and remain unsatisfactory for capturing the activity semantics of the words; there are still some challenges to accurately recognizing human daily actives in social media. Considering this problem, we propose a general model for recognizing the human activity category based on a deep learning sequence model
We propose a general model for human activity classification based on a deep learning sequence model and a series of solutions to capture the activity semantic by embedding, encoding and fusing text semantics, external knowledge and temporal information for machine learning
Summary
The knowledge about which activities and why people perform them at a certain time can be very valuable for identifying healthy lifestyles [1] and can be used in a wide range of applications.For example, doctors could better build the connection between patients’ habits and lifestyles and their health problems based on people’s daily activity patterns. We aim to automatically extract high-level logical activity [2] (e.g., sports, shopping, and entertainment) trajectories through a convenient, low-cost source, where most research studies rely on visual (e.g., cameras) and wearable devices to identify low-level physical activities (e.g., standing, walking, and sitting) [3,4] instead of high-level logical activities. For many applications, such as understanding the human lifestyle and healthy, high-level logical activity categories are more informative. There are currently few related studies, and new methods and data sources both need to be developed to achieve this goal
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