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SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition.

Muhammad Asif Razzaq1, Ian Cleland2, Chris Nugent2

  • 1Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea.

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Summary
This summary is machine-generated.

This study introduces a semantic imputation framework to enhance sensor data quality for human activity recognition (HAR) in smart environments. The approach improves classification accuracy using deep learning models, achieving up to 95.78%.

Keywords:
BLEactivity recognitionneural networkontologiesproximitysegmentationsemantic imputationunobtrusive sensing

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Area of Science:

  • Pervasive computing and human-computer interaction.
  • Machine learning and artificial intelligence applications.
  • Sensor data analysis and data quality improvement.

Background:

  • Activities of daily living (ADL) recognition in smart environments is crucial for understanding human states in pervasive computing.
  • Integrating multi-sensor data for ADL recognition is challenging due to data dynamics, semantic/syntactic differences, and imperfect data.
  • Inaccurate sensor data significantly impacts the performance of machine learning classifiers for ADL recognition.

Purpose of the Study:

  • To propose a novel semantic imputation framework to enhance the quality of sensor data for ADL recognition.
  • To leverage ontology-based semantic similarity learning for identifying correlations among sensor events.
  • To demonstrate the effectiveness of the proposed framework in improving classification accuracy using deep learning.

Main Methods:

  • Developed a semantic imputation framework utilizing ontology-based semantic similarity learning.
  • Employed SPARQL queries to identify semantic correlations among sensor events.
  • Implemented time-series longitudinal imputation for handling imperfect sensor data.
  • Applied deep learning (artificial neural network) models on public datasets for validation.

Main Results:

  • Semantically imputed datasets using the proposed framework demonstrated higher accuracy when analyzed with artificial neural networks.
  • The approach achieved a classification accuracy of 95.78% on public datasets.
  • Comparative analysis confirmed the effectiveness and robustness of the learned models compared to state-of-the-art methods.

Conclusions:

  • The proposed semantic imputation framework effectively improves sensor data quality for human activity recognition.
  • Ontology-based semantic similarity learning and time-series imputation are valuable techniques for handling complex sensor data.
  • The study validates the significant impact of data quality improvement on the performance of deep learning models in smart environments.