  Artificial Intelligence (AI) has found application in Human Activity
Recognition (HAR) in competitive sports. To date, most Machine Learning (ML)
approaches for HAR have relied on offline (batch) training, imposing higher
computational and tagging burdens compared to online processing unsupervised
approaches. Additionally, the decisions behind traditional ML predictors are
opaque and require human interpretation. In this work, we apply an online
processing unsupervised clustering approach based on low-cost wearable Inertial
Measurement Units (IMUs). The outcomes generated by the system allow for the
automatic expansion of limited tagging available (e.g., by referees) within
those clusters, producing pertinent information for the explainable
classification stage. Specifically, our work focuses on achieving automatic
explainability for predictions related to athletes' activities, distinguishing
between correct, incorrect, and cheating practices in Nordic Walking. The
proposed solution achieved performance metrics of close to 100 % on average.
