A Survey and Comparison of Activities of Daily Living Datasets in Real-life and Virtual Spaces

Swe Nwe Nwe Htun, Shusaku Egami, Ken Fukuda

Abstract: Academics, researchers, and industrial experts have made significant efforts in human activity recognition by considering different perspectives, such as benchmark datasets, utilization of smart sensors, and development of recognition algorithms. In addition, the emerging trend of avatar technologies has drawn the attention of researchers, which assemble cognitive abilities, including activity recognition, to realize social activities and overcome boundaries of human energy, time, and environment. These could widely assist older adults, independent living care, and medical care, by the data obtained from human activities. Since human behavior in real-life is extremely complex, assembling meaningful recognition is paramount. The performance of existing recognition models depends heavily on the datasets. However, it has constraints to acquiring datasets with rich information due to the limited budgets, the specific areas that can be used for human activity, the limited number of actors, and other ethical reasons. This paper considers the critical criteria to fill the requirements for human daily activity recognition and presents a survey of the existing datasets of activities of daily living in both real-life settings and virtual spaces. It also presents new challenges and potential advances in behavior recognition technology.