Improving human movement sensing with micro models and domain knowledge

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dc.contributor.advisor Brown, Kenneth en
dc.contributor.advisor O'Sullivan, Barry en Scheurer, Sebastian 2021-09-10T08:59:01Z 2021-09-10T08:59:01Z 2021-06-18 2021-06-18
dc.identifier.citation Scheurer, S. 2021. Improving human movement sensing with micro models and domain knowledge. PhD Thesis, University College Cork. en
dc.identifier.endpage 185 en
dc.description.abstract Human sensing is concerned with techniques for inferring information about humans from various sensing modalities. Examples of human sensing applications include human activity (or action) recognition, emotion recognition, tracking and localisation, identification, presence and motion detection, occupancy estimation, gesture recognition, and breath rate estimation. The first question addressed in this thesis is whether micro or macro models are a better design choice for human sensing systems. Micro models are models exclusively trained with data from a single entity, such as a Wi-Fi link, user, or other identifiable data-generating component. We consider micro and macro models in two human sensing applications, viz. Human Activity Recognition (HAR) from wearable inertial sensor data and device-free human presence detection from Wi-Fi signal data. The HAR literature is dominated by person-independent macro models. The few empirical studies that consider both micro and macro models evaluate them with either only one data-set or only one HAR algorithm, and report contradictory results. The device-free sensing literature is dominated by link-specific micro models, and the few papers that do use macro models do not evaluate their micro counterparts. Given the little and contradictory evidence, it remains an open question whether micro or macro models are a better design choice. We evaluate person-specific micro and person-independent macro models across seven HAR benchmark data-sets and four learning algorithms. We show that person-specific models (PSMs) significantly outperform the corresponding person-independent model (PIM) when evaluated with known users. To apply PSMs to data from new users, we propose ensembles of PSMs, which are improved by weighting their constituent PSMs according to their performance on other training users. We propose link-specific micro models to detect human presence from ambient Wi-Fi signal data. We select a link-specific model from the available training links, and show that this approach outperforms multi-link macro models. The second question addressed in this thesis is whether human sensing methods can be improved with domain knowledge. Specifically, we propose expert hierarchies (EHs) as an intuitive way to encode domain knowledge and simplify multi-class HAR, without negatively affecting predictive performance. The advantages of EHs are that they have lower time complexity than domain-agnostic methods and that their constituent classifiers are statistically independent. This property enables targeted tuning, and modular and iterative development of increasingly fine-grained HAR. Although this has inspired several uses of domain-specific hierarchical classification for HAR applications, these have been ad-hoc and without comparison to standard domain-agnostic methods. Therefore, it remains unclear whether they carry a penalty on predictive performance. We design five EHs and compare them to the best-known domain-agnostic methods. Our results show that EHs indeed can compete with more popular multi-class classification methods, both on the original multi-class problem and on the EHs' topmost levels. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.rights © 2021, Sebastian Scheurer. en
dc.rights.uri en
dc.subject Human sensing en
dc.subject Machine learning en
dc.subject Device-free sensing en
dc.subject Inertial sensors en
dc.subject Human activity recognition en
dc.subject Hierarchical classification en
dc.subject Presence detection en
dc.title Improving human movement sensing with micro models and domain knowledge en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD - Doctor of Philosophy en
dc.internal.availability Full text available en
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.contributor.funder Enterprise Ireland en
dc.description.status Peer reviewed en Computer Science and Information Technology en
dc.internal.conferring Autumn 2021 en
dc.internal.ricu Insight - Centre for Data Analytics en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres Supplement/12/RC/2289s2/IE/INSIGHT - Irelands Big Data and Analytics Research Centre Supplement/ en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/ en
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© 2021, Sebastian Scheurer. Except where otherwise noted, this item's license is described as © 2021, Sebastian Scheurer.
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