Abstract: Various systems and methods that enhance an exercise or other physical activity performed by a user are described. In some embodiments, a classification system communicates with a media hub to receive images and perform various methods for classifying or detecting poses, exercises, and/or movements performed by a user during an activity. In some embodiments, the systems and methods include a movements database (dB) that stores information as entries relating individual movements to data associated with the individual movements. Various systems, including class generation systems and body focus/activity systems, can utilize the movements database when presenting class content to users and/or presenting exercise information (e.g., muscle groups worked or targeted) to the users.

Abstract: Various systems and methods that enhance an exercise or other physical activity performed by a user are described. In some embodiments, a classification system communicates with a media hub to receive images and perform various methods for classifying or detecting poses, exercises, and/or movements performed by a user during an activity. In some embodiments, the systems and methods include a movements database (dB) that stores information as entries relating individual movements to data associated with the individual movements. Various systems, including class generation systems and body focus/activity systems, can utilize the movements database when presenting class content to users and/or presenting exercise information (e.g., muscle groups worked or targeted) to the users.

Abstract: Disclosed are technologies for generating training data for identification neural networks. Series of images are captured of a plurality of merchandise items from different angles and with different background assortments of other merchandise items. A labeled training dataset is generated for the plurality of merchandise items. The series of captured images is normalized, where the merchandise occupies a threshold percentage of pixels in the normalized image. The training dataset is extended by applying augmentation operations to the normalized images to generate a plurality of augmented images. Each image is stored in the training dataset as a unique training data point for the given merchandise item it depicts. Labels are generated mapping each training data point to attributes associated with the depicted merchandise item. Input neural networks are trained on the labeled training dataset to perform real-time identification of selected merchandise items placed into a self-checkout apparatus by a user.