Generating a Visual-Inertial Odometry Dataset based on a Helmet Prototype for Recognizing Human Activities

Abstract

Human activity recognition (HAR) is an important area for elderly care. Because with an effective HAR clinical management authorities can monitor movement, abnormality, human behavior, chronicle diseases, and suddenly fall remotely. HAR may also reduce the workload of a caregiver. Our research mainly focuses on HAR for sudden fall detection and prediction. Usually, raw signals or features extracted from raw signals are used in HAR developmental works, which can increase false alarm rates (FAR). Besides, it is hard to differentiate various human activities through the illustration of this time-series signal. If these activities can be patterned in regular shape and can be expressed with a simple mathematical equation, then the recognition algorithm can not only detect daily activities but also predict them. Therefore, we will present a new and much effective technical way by using visual-inertial odometry (VIO) for human activity recognition in this paper. We consider walking, running and jumping activities to show our claims. From the results, we can see that considered human activities are easy to differentiate. 'Goodness of fit’ of these activities will show how we could model mathematically them.