DOMINO: A Dataset for Context-Aware Human Activity Recognition using Mobile Devices

Arrotta, Luca; Civitarese, Gabriele; Presotto, Riccardo; Bettini, Claudio

doi:10.1109/mdm58254.2023.00063

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Cited by 2 publications

References 25 publications

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ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models

Arrotta,

Bettini,

Civitarese

et al. 2024

2024 IEEE International Conference on Smart Computing (SMARTCOMP)

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ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models

Arrotta,

Bettini,

Civitarese

et al. 2024

2024 IEEE International Conference on Smart Computing (SMARTCOMP)

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Semantic Loss

Arrotta,

Civitarese,

Bettini

2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Deep Learning models are a standard solution for sensor-based Human Activity Recognition (HAR), but their deployment is often limited by labeled data scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate these issues by infusing knowledge about context information into HAR deep learning classifiers. However, existing NeSy methods for context-aware HAR require computationally expensive symbolic reasoners during classification, making them less suitable for deployment on resource-constrained devices (e.g., mobile devices). Additionally, NeSy approaches for context-aware HAR have never been evaluated on in-the-wild datasets, and their generalization capabilities in real-world scenarios are questionable. In this work, we propose a novel approach based on a semantic loss function that infuses knowledge constraints in the HAR model during the training phase, avoiding symbolic reasoning during classification. Our results on scripted and in-the-wild datasets show the impact of different semantic loss functions in outperforming a purely data-driven model. We also compare our solution with existing NeSy methods and analyze each approach's strengths and weaknesses. Our semantic loss remains the only NeSy solution that can be deployed as a single DNN without the need for symbolic reasoning modules, reaching recognition rates close (and better in some cases) to existing approaches.

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DOMINO: A Dataset for Context-Aware Human Activity Recognition using Mobile Devices

Cited by 2 publications

References 25 publications

ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models

ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models

Semantic Loss

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