Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction.

ABSTRACT: With the continuous development of new wearable devices, sensor-based human activity recognition is enjoying enormous popularity in research and industry. The signals from inertial sensors allow for the detection, classification, and analysis of human activities such as jogging, cycling, or swimming. However, human activity recognition is often limited to basic activities that occur in short, predetermined periods of time (sliding windows). Complex macro-activities, such as multi-step sports exercises or multi-step cooking recipes, are still only considered to a limited extent, while some works have investigated the classification of macro-activities, the automated understanding of how the underlying micro-activities interact remains an open challenge. This study addresses this gap through

SUBMITTER: Wieland C 

PROVIDER: S-EPMC10893427 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature