2024/01/01 by Silvia García-Méndez, Francisco de Arriba-Pérez, Francisco J. González‐Castaño +3 · 1 voice
Computer Science · #Anomaly Detection Techniques and Applications #Data Stream Mining Techniques #Time Series Analysis and Forecasting #cs.AI #cs.HC #cs.LG
paper · pdf · doi:10.1109/mce.2024.3387019
openalex publication_date 2024/01/01 · arxiv published 2024/06/18 · arxiv updated 2024/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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. In addition, 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. 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.