CAPPIMU: A Composite Activities Dataset for Human Activity Recognition Utilizing Plantar Pressure and IMU Sensors

Human composite activity recognition enhances smart homes to better understand and respond to user needs, and plays a key role in activity assistance. However, the current public datasets for composite activities are limited in the variety of activities and the number of subjects they include, which hinders a thorough and complete assessment of activity identification methodologies. Regarding these problems, this paper proposes a publicly available dataset named CAPPIMU (Composite Activities with Plantar Pressure and IMU sensors). The dataset consists of 21 activities and two different modal synchronization data, including 15 composite activities and 6 simple activities. These modalities include plantar pressure sensors and IMUs (Inertial Measurement Units) at nine different body locations. Compared with single-modal sensors, multimodal sensors can provide a richer representation of information for activity recognition tasks. Moreover, we conduct a thorough examination of the classification effects exerted by plantar pressure and inertial data from various locations on the recognition of activities, utilizing a selection of widely-recognized deep learning models. The experimental results show that it is possible to classify these 21 household human activities with high accuracy, and the right wrist is found to be the optimal location for activity recognition with single-place IMU sensors.