A dataset for complex activity recognition withmicro and macro activities in a cooking scenario
This provides a resource for researchers in activity recognition, but it is incremental as it focuses on dataset creation without introducing new methods.
The authors tackled the lack of datasets for complex activity recognition by creating a new sensor-based dataset in a cooking scenario, featuring simultaneous macro and micro activity labels, and reported baseline classification results using traditional pipelines.
Complex activity recognition can benefit from understanding the steps that compose them. Current datasets, however, are annotated with one label only, hindering research in this direction. In this paper, we describe a new dataset for sensor-based activity recognition featuring macro and micro activities in a cooking scenario. Three sensing systems measured simultaneously, namely a motion capture system, tracking 25 points on the body; two smartphone accelerometers, one on the hip and the other one on the forearm; and two smartwatches one on each wrist. The dataset is labeled for both the recipes (macro activities) and the steps (micro activities). We summarize the results of a baseline classification using traditional activity recognition pipelines. The dataset is designed to be easily used to test and develop activity recognition approaches.