SmartEAR: Smartwatch-based Unsupervised Learning for Multi-modal Signal Analysis in Opportunistic Sensing Framework
| Autor: | Debanjan Borthakur, Harishchandra Dubey, Kunal Mankodiya, Joshua V. Gyllinsky, Andrew Peltier |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Self-organizing map
FOS: Computer and information sciences Artificial neural network business.industry Computer science Interface (computing) Wearable computer 02 engineering and technology Machine learning computer.software_genre 020202 computer hardware & architecture Smartwatch Computer Science - Computers and Society Contextual design ComputingMethodologies_PATTERNRECOGNITION Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence business Cluster analysis computer |
| Zdroj: | CHASE |
| Popis: | 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. 6 pages, 8 figures, 1 table |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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