Document Type
Conference Proceeding
Date of Original Version
2018
Abstract
Wrist-bands such as smartwatches have become an unobtrusive interface for collecting physiological and contextual data from users. Smartwatches are being used for smart healthcare, telecare, and wellness monitoring. In this paper, we used data collected from the AnEAR framework leveraging smartwatches to gather and store physiological data from patients in naturalistic settings. This data included temperature, galvanic skin response (GSR), acceleration, and heart rate (HR). In particular, we focused on HR and acceleration, as these two modalities are often correlated. Since the data was unlabeled we relied on unsupervised learning for multi-modal signal analysis. We propose using k-means clustering, GMM clustering, and Self-Organizing maps based on Neural Networks for group the multi-modal data into homogeneous clusters. This strategy helped in discovering latent structures in our data.
Citation/Publisher Attribution
Borthakur, D., Peltier, A., Dubey, H., Gyllinsky, J., & Mankodiya, K. (2018, September 26-28). SmartEAR: Smartwatch-based Unsupervised Learning for Multi-modal Signal Analysis in Opportunistic Sensing Framework. CHASE '18, Washington, DC. doi: 10.1145/3278576.3278599
Available at: http://dx.doi.org/10.1145/3278576.3278599
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