Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition

arXiv:2310.02011v2 Announce Type: replace-cross
Abstract: A person's movement or relative positioning effectively generates raw electrical signals that can be read by computing machines to apply various manipulative techniques for the classification of different human activities. In this paper, a stratified multi-structural approach based on a Residual network ensembled with Residual MobileNet is proposed, termed as FusionActNet. The proposed method involves using carefully designed Residual blocks for classifying the static and dynamic activities separately because they have clear and distinct characteristics …

abstract apply arxiv classification computing cs.cv cs.lg data decoding human machines network paper person raw recognition residual type wearable