Understanding Parental Perceptions of Content-Specific Barriers to Preventing Unintentional Injuries in the Home

Background: Preventable injuries are the leading cause of death in children around the world, including in Japan. As children under the age of 5 years spend most of their time at home, home injury prevention is critical for child safety. The purpose of this study was to identify specific, focused, and precise barriers against injury prevention practice. Methods: We conducted an online survey to examine the barriers faced by parents when taking actions to prevent home injuries. Results: The results revealed common reasons why parents do not or cannot take a recommended action across injury types, and that the magnitude of importance for a specific barrier depends on the type of injury. Conclusions: Identifying content-specific barriers could help researchers and educators understand parents’ needs, discuss what barriers are more important than others by injury type, and develop effective strategies based on the 3Es of injury prevention (enforcement, engineering, and education)

Understanding Parental Perceptions of Content-Specific Barriers to Preventing Unintentional Injuries in the Home

Background: Preventable injuries are the leading cause of death in children around the world, including in Japan. As children under the age of 5 years spend most of their time at home, home injury prevention is critical for child safety. The purpose of this study was to identify specific, focused, and precise barriers against injury prevention practice. Methods: We conducted an online survey to examine the barriers faced by parents when taking actions to prevent home injuries. Results: The results revealed common reasons why parents do not or cannot take a recommended action across injury types, and that the magnitude of importance for a specific barrier depends on the type of injury. Conclusions: Identifying content-specific barriers could help researchers and educators understand parents’ needs, discuss what barriers are more important than others by injury type, and develop effective strategies based on the 3Es of injury prevention (enforcement, engineering, and education)

Synthesizing Event-Centric Knowledge Graphs of Daily Activities Using Virtual Space

Artificial intelligence (AI) is expected to be embodied in software agents, robots, and cyber-physical systems that can understand the various contextual information of daily life in the home environment to support human behavior and decision making in various situations. Scene graph and knowledge graph (KG) construction technologies have attracted much attention for knowledge-based embodied question answering meeting this expectation. However, collecting and managing real data on daily activities under various experimental conditions in a physical space are quite costly, and developing AI that understands the intentions and contexts is difficult. In the future, data from both virtual spaces, where conditions can be easily modified, and physical spaces, where conditions are difficult to change, are expected to be combined to analyze daily living activities. However, studies on the KG construction of daily activities using virtual space and their application have yet to progress. The potential and challenges must still be clarified to facilitate AI development for human daily life. Thus, this study proposes the VirtualHome2KG framework to generate synthetic KGs of daily life activities in virtual space. This framework augments both the synthetic video data of daily activities and the contextual semantic data corresponding to the video contents based on the proposed event-centric schema and virtual space simulation results. Therefore, context-aware data can be analyzed, and various applications that have conventionally been difficult to develop due to the insufficient availability of relevant data and semantic information can be developed. We also demonstrate herein the utility and potential of the proposed VirtualHome2KG framework through several use cases, including the analysis of daily activities by querying, embedding, and clustering, and fall risk detection among older adults based on expert knowledge. As a result, we are able to develop a support tool that detects the fall risk with 1.0 precision, 0.6 recall, and 0.75 F1-score and visualize it with an explanation of its rationale. Using the cases explored in this work, we also clarify and classify the challenges that future research on synthetic KG generation systems should resolve in terms of simulation, schema, and human activity. Finally, we discuss the potential solutions for implementing advanced applications to support our daily life