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.

Silvia García-Méndez

Francisco de Arriba-Pérez

Francisco J. González-Castaño

Javier Vales-Alonso

IEEE Consumer Electronics Magazine

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本期刊涵盖与“IEEE Consumer Electronics Magazine”相关的多个领域，包括视频技术、音频技术、白色家电、家庭护理产品、移动通信、游戏、空气护理产品、家庭医疗设备、健身设备、家庭自动化和网络设备、消费太阳能技术、家庭影院、数字成像、车载技术、无线技术、有线和卫星技术、家庭安全、家庭照明、人机界面、人工智能、家庭计算以及消费者存储技术等。

Category	Quartile
计算机科学	4
计算机科学, 工程电子与电气	3
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计算机科学, 电信学	4

Unsupervised Explainable Activity Prediction in Competitive Nordic Walking from Experimental Data

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