Activity recognition research has shifted focus from distinguishing full-body motion patterns to recognizing complex interactions of multiple entities. Manipulative gestures – characterized by interactions between hands, tools, and manipulable objects – frequently occur in food preparation, manufacturing, and assembly tasks, and have a variety of applications including situational support, automated supervision, and skill assessment. With the aim to stimulate research on recognizing manipulative gestures we introduce the 50 Salads dataset. It captures 25 people preparing 2 mixed salads each and contains over 4h of annotated accelerometer and RGB-D video data. Including detailed annotations, multiple sensor types, and two sequences per participant, the 50 Salads dataset may be used for research in areas such as activity recognition, activity spotting, sequence analysis, progress tracking, sensor fusion, transfer learning, and user-adaptation.
The dataset includes
RGB video data 640×480 pixels at 30 Hz
Depth maps 640×480 pixels at 30 Hz
3-axis accelerometer data at 50 Hz of devices attached to a knife, a mixing spoon, a small spoon, a peeler, a glass, an oil bottle, and a pepper dispenser.
Synchronization parameters for temporal alignment of video and accelerometer data
Annotations as temporal intervals of pre- core- and post-phases of activities corresponding to steps in a recipe