We investigate current techniques for modeling cloth deformation on human bodies in 3D, with a particular focus on the recent approach proposed by TailorNet. We introduce the representations for clothed digital humans in literature, and compare TailorNet to other recent data-driven models used for clothing prediction. We also talk about traditional physics- based simulation methods, and how they compare to data-driven methods in general and TailorNet in particular. Finally, we talk about fabric-embedded on-body sensors and how cloth artefacts can affect their readings. Use of body sensor networks to analyze human movement outside of laboratory environments is limited, because of the undesirable motion artefacts corrupting the movement signals. Inertial sensors worn on top of regular clothes are affected by the extra motion introduced by movement of clothes, making it is important to separate the body motion from cloth motion by identifying optimal locations for sensor placement, such that they are least influenced by the high-frequency wrinkles.