2018/08/11 by Debanjan Borthakur, Andrew Peltier, Borthakur, Debanjan +8
Computer Science · Engineering · Neuroscience · Psychology · #Computers and Society (cs.CY) #EEG and Brain-Computer Interfaces #Emotion and Mood Recognition #FOS: Computer and information sciences #Non-Invasive Vital Sign Monitoring #cs.CY
paper · pdf · doi:10.48550/arxiv.1808.06473
6 pages, 8 figures, 1 table
arxiv created 2018/08/11 · openalex publication_date 2018/08/11 · arxiv updated 2018/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.