Visualizing Prioritized Typical and Potential Risks of Consumer Products by Graph Mining of an Accident Database

Abstract

Designing a safe product requires predicting how consumers will use the product and what sort of risks exist in their daily environment. However, assistive technology for risk assessment of consumer products used in the daily environment has not yet been established. One of the most promising approaches is to utilize data on actual accidents that have occurred in the past. This paper proposes a new method that uses recently developed data mining technology to predict the typical and potential risks of consumer products. The proposed method is as follows: 1) create a situational graph database by structuralizing accident data as a graph; 2) visualize the typical risk using this situational graph database; and 3) visualize the potential risk using two methods: a probabilistic latent semantic indexing (pLSI) method and a method based on the features of the product. Prioritizing design improvement requires considering severity of injury. To this end, a function for supporting severity control is also implemented. To demonstrate the effectiveness of the proposed system, we applied our system to a dataset of 681 cases of accidental burning or scalding injuries. Injury severity was evaluated using body area of burn and scald injuries.