Skip to main content

Semantic Enrichment and Visualisation of HAR Data: Ontology Development from Unstructured Data Sets Metadata

  • Conference paper
  • First Online:
Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024) (UCAmI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1212))

  • 525 Accesses

Abstract

Human Activity Recognition (HAR) is crucial for a wide range of applications, including healthcare monitoring and smart home systems. Despite significant advancements, existing HAR systems often face challenges related to the semantic interpretation and meaningful visualization of the data set metadata and its quality. This research presents a novel approach to address these challenges through ontology-based semantic enrichment and visualisation of unstructured metadata to facilitate better understanding and analysis of HAR data. Text mining techniques were utilised for extracting information from diverse file formats to develop an ontology. The extracted information was pre-processed and cleaned using removal of punctuation, lower casing, removal of stop words, tokenisation, lemmatisation, and removal of non-alphabetic tokens. The processed tokens were compared to a predefined key-value pairs data dictionary to develop the ontology via matched tokens. The proposed approach was demonstrated to achieve the effectiveness through a case study utilising the metadata from the Opportunity dataset. The effectiveness of the ontology is evaluated and validated using a quantitative approach involving metrics such as coverage, consistency, and precision. The results demonstrate that the proposed approach not only enhances the semantic complexity of HAR data but also provides useful visual representations, hence improving the understanding and decision-making in HAR applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://python-docx.readthedocs.io/en/latest/.

  2. 2.

    https://pypi.org/project/beautifulsoup4/.

  3. 3.

    https://scrapy.org/.

  4. 4.

    https://owlready2.readthedocs.io/en/latest/.

  5. 5.

    https://rdflib.readthedocs.io/en/stable/.

  6. 6.

    https://graphviz.org/.

  7. 7.

    http://owlgred.lumii.lv/ http://owlgred.lumii.lv/online_visualization/e0i7.

References

  1. Yadav, S.K., Tiwari, K., Pandey, H.M., Akbar, S.A.: A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions. Knowl.-Based Syst. 223, 106970 (2021). https://doi.org/10.1016/j.knosys.2021.106970

  2. Alam, G., McChesney, I., Nicholl, P., Rafferty, J.: Open datasets in human activity recognition research—issues and challenges: a review. IEEE Sens. J. 23(22), 26952–26980 (2023). https://doi.org/10.1109/JSEN.2023.3317645

  3. Alam, G., McChesney, I., Nicholl, P., Rafferty, J.:  An approach to extract and compare metadata of human activity recognition (HAR) data sets.  In: Bravo, J., Ochoa, S., Favela,  J. (eds.) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). LNNS. Springer International Publishing, Cham, pp. 717–728 (2023). https://doi.org/10.1007/978-3-031-21333-5_71

  4. Sen, A.: Metadata management: past, present and future. Decis. Support. Syst. 37(1), 151–173 (2004). https://doi.org/10.1016/S0167-9236(02)00208-7

  5. Alam, G., McChesney, I., Nicholl, P., Rafferty, J.: Knowledge-Driven Approach for Quality Assessment of HAR Data Sets: An Automated Tool. In: IEEE International Conference on Ubiquitous Intelligence and Computing (2023). Accessed: Dec. 30, 2023.  https://pure.ulster.ac.uk/en/publications/knowledge-driven-approach-for-quality-assessment-of-har-data-sets

  6. Horsman, G.:  A template for creating and sharing ground truth data in digital forensics. J. Forensic Sciences, vol. n/a, no. n/a, https://doi.org/10.1111/1556-4029.15524

  7. Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., De Munari, I.: IoT wearable sensor and deep learning: an integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet Things J. 6(5), 8553–8562 (2019). https://doi.org/10.1109/JIOT.2019.2920283

  8. Curran, P.J., Hussong, A.M.: Integrative data analysis: The simultaneous analysis of multiple data sets. Psychol. Methods 14(2), 81–100 (2009). https://doi.org/10.1037/a0015914

    Article  Google Scholar 

  9. UCI Machine Learning Repository: OPPORTUNITY Activity Recognition Data Set. Accessed 27 Nov 2021.  https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition

  10. Miah, M.S.U., et al.: Sentence boundary extraction from scientific literature of electric double layer capacitor domain: tools and techniques. Applied Sci. 12(3), Art. no. 3 (2022). https://doi.org/10.3390/app12031352

  11. Dietrich, D.: Metadata management in a data staging repository. J. Libr. Metadata 10(2–3), 79–98 (2010). https://doi.org/10.1080/19386389.2010.506376

  12. Gangemi, A., Catenacci, C., Ciaramita, M., Lehmann, J.: Modelling ontology evaluation and validation. In: Sure, Y., Domingue, J (eds.) The Semantic Web: Research and Applications,  pp. 140–154. Springer, Berlin (2006). https://doi.org/10.1007/11762256_13

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gulzar Alam .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alam, G., McChesney, I., Nicholl, P., Rafferty, J. (2024). Semantic Enrichment and Visualisation of HAR Data: Ontology Development from Unstructured Data Sets Metadata. In: Bravo, J., Nugent, C., Cleland, I. (eds) Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024). UCAmI 2024. Lecture Notes in Networks and Systems, vol 1212. Springer, Cham. https://doi.org/10.1007/978-3-031-77571-0_43

Download citation

Publish with us

Policies and ethics