Collection and curation of a large reference dataset for activity recognition
Abstract
The field of research on activity recognition is relatively young compared to others, like computer vision. In more mature fields, algorithms are usually tested on standardized, reference datasets. This way, algorithms coming from different groups can be tested in a fair manner, which accelerates the process of developing new knowledge. Collecting a reference dataset under realistic settings for activity recognition poses many challenges due to the large amount of sensors and sensor modalities which are needed to provide a sufficiently complete playground. We here report on some lessons learned while collecting such a reference dataset with a heterogeneous setup. We argue for the importance of a few principles to obtain a clean dataset, starting from the sampling and acquisition, down to the synchronization and labeling of the data.
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Data collection for HAR is challenging and time-consuming and involves general challenges on data collection, synchronization and multi-modal sensor arrangements.
“The data collection for HAR is a challenging and time consuming task that involves general challenges on data collection, synchronization and multi-modal sensor arrangements (Calatroni et al. 2011).”
From Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People (2020) · cites this paper as Calatroni et al. 2011
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- Authors
- Calatroni, A., Roggen, D., & Troster, G.
- Affiliations
- ETH Zurich (CH)
- ORCID
- Roggen, Daniel
- Venue
- 2011 IEEE International Conference on Systems, Man, and Cybernetics
- Publisher
- IEEE
- Published
- 2011-10
- Pages
- 30-35
- ISSN
- 1062-922X (ISSN-L)
- OpenAlex
- W1982791972
- Cited by
- 13
- References
- 10
- Citations by year
- 2025: 1 · 2022: 1 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 3 · 2016: 2 · 2014: 2 · 2013: 1
- FWCI
- 0.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- activity recognition, reference dataset, dataset collection, data curation, multi-sensor data, data synchronization, data annotation, data acquisition, sampling
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