A Practical Wearable Sensor-Based Human Activity Recognition Research Pipeline
by , ,
Abstract:
Many researchers devote themselves to studying various aspects of Human Activity Recognition (HAR), such as data analysis, signal processing, feature extraction, and machine learning models. In response to the fact that few documents summarize and form intuitive paradigms for the entire HAR research pipeline, based on the purpose of sharing our years of research experience, we propose a practical, comprehensive HAR research pipeline, called HAR-Pipeline, composed of nine research aspects, aiming to reflect the overall perspective of HAR research topics to the greatest extent and indicate the sequence and relationship between the tasks. Supplemented by the outcomes of our actual series of studies as examples, we demonstrate the proposed pipeline's rationality and feasibility.
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PDF URL: https://www.csl.uni-bremen.de/cms/images/documents/publications/LiuHartmannSchultz_Healthinf2022.pdf
Reference:
A Practical Wearable Sensor-Based Human Activity Recognition Research Pipeline (Hui Liu, Yale Hartmann, Tanja Schultz), In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF: WHC, SCITEPRESS - Science and Technology Publications, 2022.
Bibtex Entry:
@inproceedings{liu2022pipeline,
  title = {A Practical Wearable Sensor-Based Human Activity Recognition Research Pipeline},
  author = {Liu, Hui and Hartmann, Yale and Schultz, Tanja},
  booktitle = {Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF: WHC},
  pages = {847--856},
  organization = {INSTICC},
  publisher = {SCITEPRESS - Science and Technology Publications},
  year = {2022},
  isbn = {978-989-758-552-4},
  issn = {2184-4305},
  doi = {10.5220/0010937000003123},
  url = {https://www.csl.uni-bremen.de/cms/images/documents/publications/LiuHartmannSchultz_Healthinf2022.pdf},
  abstract = {Many researchers devote themselves to studying various aspects of Human Activity Recognition (HAR), such as data analysis, signal processing, feature extraction, and machine learning models. In response to the fact that few documents summarize and form intuitive paradigms for the entire HAR research pipeline, based on the purpose of sharing our years of research experience, we propose a practical, comprehensive HAR research pipeline, called HAR-Pipeline, composed of nine research aspects, aiming to reflect the overall perspective of HAR research topics to the greatest extent and indicate the sequence and relationship between the tasks. Supplemented by the outcomes of our actual series of studies as examples, we demonstrate the proposed pipeline's rationality and feasibility.}
}