Conference proceeding
Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vol.2018, pp.1193-1196
07/2018
Handle:
https://hdl.handle.net/2376/103552
PMID: 30440604
Abstract
Human activity recognition (HAR) is an important component in health-care systems. For example, it can enable context-aware applications such as elderly care and patient monitoring. Relying on a set of training data, supervised machine learning algorithms form the core intelligence of most existing HAR systems. Meanwhile, the accuracy of an HAR model highly depends on the similarity between the training and the operating context. Therefore, there is a need for developing machine learning algorithms that can easily adapt to the operating context at hand. In this paper, we propose a cross-subject transfer learning algorithm that links source and target subjects by constructing manifolds from feature-level representation of the source subject(s). Our algorithm assigns labels to the unlabeled data in the current context using the manifold learned from the source subject(s). The newly labeled data is used to develop a personalized HAR model for the current context (i.e., target subject). We demonstrate the efficacy of the algorithm using a publicly available dataset on HAR. We show that the proposed framework improves the accuracy of activity recognition by up to 24%.
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Details
- Title
- Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer
- Creators
- Ramyar Saeedi - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USAKeyvan Sasani - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USASkyler Norgaard - Dept. of Comput. Sci., Kalamazoo Coll., Kalamazoo, MI, USAAssefaw H Gebremedhin - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
- Publication Details
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vol.2018, pp.1193-1196
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- IEEE
- Identifiers
- 99900546623101842
- Language
- English
- Resource Type
- Conference proceeding