WEAR is an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). The dataset comprises data from 18 participants performing a total of 18 different workout activities with untrimmed inertial (acceleration) and camera (egocentric video) data recorded at 10 different outside locations. Unlike previous egocentric datasets, WEAR provides a challenging prediction scenario marked by purposely introduced activity variations as well as an overall small information overlap across modalities.
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MuscleMap136 is a dataset for video-based Activated Muscle Group Estimation (AMGE) aiming at identifying currently activated muscular regions of humans performing a specific activity. Video-based AMGE is an important yet overlooked problem. To this intent, the MuscleMap136 dataset features 15K video clips with 136 different activities and 20 labeled muscle groups.
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