TF-C Pretrain HAR

- Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency - Paper link: - Github repo: https://github.com/mims-harvard/TFC-pretraining - Project website: HAR contains recordings of 30 health volunteers performing six daily activities such as walking, walking upstaris, walking downstairs, sitting, standing, and laying. The prediction labels are the six activities. The wearable sensors on a smartphone measure triaxial linear acceleration and triaxial angular velocity at 50 Hz. After preprocessing and isolating out gravitational acceleration from body acceleration, there are nine channels in total. To line up the semantic domain with the channels in the dataset use during fine-tuning Gesture we only use the three channels of body linear accelerations.