Publication

Abstract : Wearable fitness devices are widely used to monitor physiological signals such as heart rate (BPM) and speed during physical activity. However, these signals often suffer from noise, technical inaccuracies, and context-dependent variability. In this study, we investigate unsupervised anomaly detection methods to identify abnormal segments in real-world data collected from runners using wearable sensors. The dataset includes over 180,000 measurements from 43 running sessions, with speed and BPM values aligned and preprocessed to build a multivariate time series. We compare four approaches representative of different anomaly detection paradigms: distance-based (k-Nearest Neighbors), classification-based (One-Class SVM), probabilistic (Kernel Density Estimation), and sequence-based deep learning (TadGAN). Classical methods operate on pointwise values and capture global anomalies with high precision, but they fail to detect contextual or collective anomalies. TadGAN, in contrast, is trained on overlapping sequences and demonstrates the ability to identify local patterns of abnormality across time. Our results highlight the complementarity of these methods and the importance of modeling temporal structure when anomalies are subtle or context-dependent. Although TadGAN fails to capture extreme point anomalies, its performance on sequence-level detection suggests promising directions for future research in health-aware fitness monitoring. All analyses were conducted without labels, under purely unsupervised conditions.