In this talk, we discuss the paper “Tackling inter-subject variability in smartwatch data using factorization models” by Arman Naseri et. al, Scientific Reports, 2025.<\/p>\n
Abstract<\/strong><\/p>\n Smartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However, differences among subjects (inter-subject variability) limit a model to generalize to unseen subjects. This study focused on binary classification tasks using heart rate and step counter from smartwatches, including night\/day and inactive\/active classification, as well as sleep and SpO2-related (oxygen saturation) tasks. To address inter-subject variability, we explored different transforming and normalization regimes for time series including per-subject and population-based strategies. We propose a modified factorized autoencoder, which separates the data into two latent spaces capturing class-specific and subject-specific information. Our proposed generalized factorized autoencoder and triplet factorized autoencoder improved classification accuracy over the baseline from 74.8 (\u00b1\u00a010.5) to 83.1 (\u00b1\u00a05.1) and 83.4 (\u00b1\u00a05.3), respectively, for night\/day classification, gains for inactive\/active classification were modest, improving from 84.3 (\u00b1\u00a09.4) to 86.9 (\u00b1\u00a04.4) and 86.6 (\u00b1\u00a04.3), respectively. Our study highlights challenges of handling inter-subject variability in smartwatch data and how factorization models can be used to enable more robust and personalized health monitoring solutions for diverse populations.<\/p>\n","protected":false},"excerpt":{"rendered":" In this talk, we discuss the paper “Tackling inter-subject variability in smartwatch data using factorization models” by Arman Naseri et. al, Scientific Reports, 2025. Abstract Smartwatches enable longitudinal and continuous … <\/p>\n