Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition
This work addresses the challenge of making neuro-symbolic approaches practical for human activity recognition on resource-constrained devices, though it is incremental as it builds on existing methods.
The paper tackles the problem of deploying neuro-symbolic AI for context-aware human activity recognition by addressing computational inefficiencies and lack of real-world evaluation, proposing a semantic loss function that avoids symbolic reasoning during classification and shows competitive recognition rates on scripted and in-the-wild datasets.
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.