Abstract
Biometric identification from heart rate sequences provides a simple yet effective mechanism, that can neither be reverse engineered nor replicated, to protect user privacy. This study employs a highly efficient time series classification (TSC) algorithm, miniROCKET, to identify users by their heart rate. The approach adopted in this study employs user heart rate data, a simplified form of heart activity, captured during exercise, filtered, and contextualized within exercise routines, for user classification. Results from this study are empirically evaluated on a real-world data set, containing 115,082 workouts across 304 users, by three other state-of-the-art TSC algorithms. Our experiments showed that for 36 users, the variance explained by heart rate feature is 74.0%, when coupled with speed and altitude, the variance increases to 94.0%. For 304 users, the variance explained by heart-rate is 32.8% and increased to 65.9% with contextual features. This exploratory study highlights the potential of heart rate as a biometric identifier. It also underscores how contextual factors, such as speed and altitude change, can improve classification of timeseries data when coupled with smart data preprocessing.
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Desa, S.A.J., Suleiman, B., Yaqub, W. (2023). Heart Rate-Based Identification of Users of IoT Wearables: A Supervised Learning Approach. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_41
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