The integration of sensors into garments has paved the way for human activity recognition (AR), enabling users to engage in extended human motion recordings. The inherent fluidity of loose clothing allows it to mirror the wearer’s movements. From a statistical standpoint, clothing captures additional valuable insights beyond rigid body motions, improving AR. This work demonstrates how fabric’s orientation, layering and width contribute to the enhanced performance of AR with clothing in periodic motion. Experiments are reported in which a scotch yoke and a KUKA robot manipulator are used to induce the periodic motion of fabric cloth at different frequencies. These reveal that clothing-attached sensors exhibit higher frequency classification accuracy among sensors with an improvement of 27% for perpendicular-oriented fabric, 18% for triple-layered fabric, and 9% for large-width fabric, exceeding that seen with rigid attached sensors.