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. 

Heart Rate-Based Identification of Users of IoT Wearables: A Supervised Learning Approach

Online proctoring has become a necessity in online teaching. Video-based crowd-sourced online proctoring solutions are being used, where an exam-taking student's video is moni-tored by third-parties, leading to privacy concerns. In this paper, we propose a privacy-preserving online proctoring system. The proposed image-hashing-based system can detect the student's excessive face and body movement (i.e., anomalies) that is resulted when the student tries to cheat in the exam. The detection can be done even if the student's face is blurred or masked in video frames. Experiment with an in-house dataset shows the usability of the proposed system.

Waheeb Yaqub

Papers

Heart Rate-Based Identification of Users of IoT Wearables: A Supervised Learning Approach

Privacy-Preserving Online Proctoring using Image-Hashing Anomaly Detection

Proctoring Online Exam Using Eye Tracking

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