# Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data

arXiv: https://arxiv.org/abs/2406.12762 · HTML: https://mlsanity.com/paper/2406.12762/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [A benchmark of data stream classification for human activity recognition on connected objects](https://mlsanity.com/paper/2008.11880/) (2020)
- [Explainable machine learning multi-label classification of Spanish legal judgements](https://mlsanity.com/paper/2405.17610/) (2024)
- [Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing](https://mlsanity.com/paper/2405.14505/) (2024)
- [Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly](https://mlsanity.com/paper/2405.18845/) (2024)

## Abstract (arXiv)

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.