Retrieval-based Annotation for Multi-Channel Time Series Data of Human Activities

Recent years have seen a rise in the number of labelled human activity datasets to support supervised learning of activity recognition. However, synchronisation and manual annotation of various multi-channel time-series data are cumbersome. Prior research focused on creating datasets that are convenient to annotate, leading to a scarcity of natural activity data that includes various subjects and activities. Therefore, an offline manual annotation tool for efficient labelling activities is desired. This work presents a semi-automatic annotation technique for multi-channel time-series human activity data, utilising a retrieval-based approach to reduce annotation effort. We present an annotation tool that accepts a variety of input data types and supports both manual and semi-automatic annotation. We benchmark the different approaches.

  • Published in:
    Annotation of Real-World Data for Artificial Intelligence Systems
  • Type:
    Inproceedings
  • Authors:
    Rueda, Fernando Moya; Nair, Nilah Ravi; Spiekermann, Raphael; Altermann, Erik; Oberdiek, Philipp; Reining, Christopher; Fink, Gernot A.
  • Year:
    2025
  • Source:
    https://link.springer.com/chapter/10.1007/978-3-032-09117-8_4#Sec1

Citation information

Rueda, Fernando Moya; Nair, Nilah Ravi; Spiekermann, Raphael; Altermann, Erik; Oberdiek, Philipp; Reining, Christopher; Fink, Gernot A.: Retrieval-based Annotation for Multi-Channel Time Series Data of Human Activities, Annotation of Real-World Data for Artificial Intelligence Systems, 2025, 53--73, Springer, https://link.springer.com/chapter/10.1007/978-3-032-09117-8_4#Sec1, Rueda.etal.2025a,

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