Search

Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

As quality of life has improved, the need for high-performance building materials that meet specific technological requirements has increased. Residential environments have also changed owing to climate change. A technology roadmap could define and systematically reflect a timeline for the development of future core technologies. The purpose of this research is to build a technology roadmap that could be utilized for the development of technology in the eco-friendly building material industry. This research is composed of multiple analysis processes—patent analysis, Delphi, and analytic hierarchy process analysis—that minimize the uncertainty caused by the lack of information in the eco-friendly construction industry by securing objective future forecast data. Subsequently, the quality function deployment test is implemented to verify the feasibility of the technology roadmap that is constructed. The design of various types of functional, low-carbon building materials could reduce carbon emissions and save energy by ensuring a hazardous-material-free market in the future. This design development roadmap is required to complement this technology roadmap.