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Human Activity Recognition: A Comparative Study of Validation Methods and Impact of Feature Extraction in Wearable Sensors

Saeed Ur Rehman Orcid Logo, Anwar Ali Orcid Logo, Adil Mehmood Khan, Cynthia Okpala

Algorithms, Volume: 17, Issue: 12, Start page: 556

Swansea University Author: Anwar Ali Orcid Logo

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DOI (Published version): 10.3390/a17120556

Abstract

With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inap...

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Published in: Algorithms
ISSN: 1999-4893
Published: MDPI AG 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68579
Abstract: With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inappropriate split of testing and training datasets, causing these models to evaluate the same subjects that they were trained on, making them subject-dependent. This study comparatively discusses this validation approach with a universal approach, Leave-One-Subject-Out (LOSO) cross-validation which is not subject-dependent and ensures that an entirely new subject is used for evaluation in each fold, validated on four different machine learning models trained on windowed data and select hand-crafted features. The random forest model, with the highest accuracy of 76% when evaluated on LOSO, achieved an accuracy of 89% on k-fold cross-validation, demonstrating data leakage. Additionally, this experiment underscores the significance of hand-crafted features by contrasting their accuracy with that of raw sensor models. The feature models demonstrate a remarkable 30% higher accuracy, underscoring the importance of feature engineering in enhancing the robustness and precision of HAR systems.
Keywords: Machine learning; LOSO; human activity recognition
College: Faculty of Science and Engineering
Funders: This research received no external funding.
Issue: 12
Start Page: 556

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