Abstract
As the world’s population ages, technology-based support for the elderly is becoming increasingly important. This study analyzes the relationship between natural standing behavior measured in a living space of elderly people and the classes of standing aids, as well as the physical and cognitive abilities contributing to household fall injury prevention. In total, 24 elderly standing behaviors from chairs, sofas, and nursing beds recorded in an RGB-D elderly behavior library were analyzed. The differences in standing behavior were analyzed by focusing on intrinsic and common standing aid characteristics among various seat types, including armrests of chairs or sofas and nursing bed handrails. The standing behaviors were categorized into two types: behaviors while leaning the trunk forward without using an armrest as a standing aid and those without leaning the trunk forward by using an arrest or handrail as a standing aid. The standing behavior clusters were distributed in a two-dimensional map based on the seat type rather than the physical or cognitive abilities. Therefore, to reduce the risk of falling, it would be necessary to implement a seat type that the elderly can unconsciously and naturally use as a standing aid even with impaired physical and cognitive abilities.
Highlights
With the aging of the world’s population, technology-based support for the daily lives of the elderly has become an important issue
We found that the standing behavior was categorized according to the product, and we were able to examine the relationship between standing behavior and products, body, and cognitive ability in a twodimensional map constructed by MDS
The main contributions of this study are as follows: (1) We analyzed, for the first time, the relationship between the natural standing behavior of elderly people and the intrinsic and common class of standing aids of sofas, chairs, and nursing beds, where natural standing behaviors occur in a living space
Summary
With the aging of the world’s population, technology-based support for the daily lives of the elderly has become an important issue. In order to realize safer living environments and consumer products for the elderly, it is necessary to develop systems that are suitable for the daily consumer product use behavior of elderly people with declining physical and cognitive abilities. Smart homes and related ambient sensing technologies powered by machine learning (ML) and Internet of Things (IoT) devices are being proposed to provide living environments that support the elderly’s safety. The travel pattern of elderly people with dementia was categorized by ML model based on collected movement data by active RFID activity monitoring systems [4]. Activity patterns of people with dementia were analyzed by ML methods based on data recorded via environmental sensors [6,7].
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