The scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. The traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sources – providing motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.
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Activity recognition in wearables using adversarial multi-source domain adaptation
Human activity recognition (HAR) from wearable sensors data has become ubiquitous due to the widespread proliferation of IoT and wearable devices. However, recognizing human activity in heterogeneous environments, for example, with sensors of different models and make, across different persons and their on-body sensor placements introduces wide range discrepancies in the data distributions, and therefore, leads to an increased error margin. Transductive transfer learning techniques such as domain adaptation have been quite successful in mitigating the domain discrepancies between the source and target domain distributions without the costly target domain data annotations. However, little exploration has been done when multiple distinct source domains are present, and the optimum mapping to the target domain from each source is not apparent. In this paper, we propose a deep Multi-Source Adversarial Domain Adaptation (MSADA) framework that opportunistically helps select the most relevant feature representations from multiple source domains and establish such mappings to the target domain by learning the perplexity scores. We showcase that the learned mappings can actually reflect our prior knowledge on the semantic relationships between the domains, indicating that MSADA can be employed as a powerful tool for exploratory activity data analysis. We empirically demonstrate that our proposed multi-source domain adaptation approach achieves 2% improvement with OPPORTUNITY dataset (cross-person heterogeneity, 4 ADLs), whereas 13% improvement on DSADS dataset (cross-position heterogeneity, 10 ADLs and sports activities).
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- Award ID(s):
- 1750936
- NSF-PAR ID:
- 10219627
- Date Published:
- Journal Name:
- Smart health
- Volume:
- 19
- ISSN:
- 2352-6483
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016/j.smhl.2020.100174
