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.

应用人工智能和机器学习技术，利用低成本的可穿戴式惯性测量单元进行在线聚类处理，以达到对运动员活动的自动解释性预测，区分正确、错误和作弊的识别，且取得近 100％的平均性能指标。

基于实验数据的竞技北欧行走无监督可解释活动预测

Unsupervised explainable activity prediction in competitive Nordic  Walking from experimental data

We propose a novel architecture and method of explainable classification with
Concept Bottleneck Models (CBMs). While SOTA approaches to Image Classification
task work as a black box, there is a growing demand for models that would
provide interpreted results. Such a models often learn to predict the
distribution over class labels using additional description of this target
instances, called concepts. However, existing Bottleneck methods have a number
of limitations: their accuracy is lower than that of a standard model and CBMs
require an additional set of concepts to leverage. We provide a framework for
creating Concept Bottleneck Model from pre-trained multi-modal encoder and new
CLIP-like architectures. By introducing a new type of layers known as Concept
Bottleneck Layers, we outline three methods for training them: with
$\ell_1$-loss, contrastive loss and loss function based on Gumbel-Softmax
distribution (Sparse-CBM), while final FC layer is still trained with
Cross-Entropy. We show a significant increase in accuracy using sparse hidden
layers in CLIP-based bottleneck models. Which means that sparse representation
of concepts activation vector is meaningful in Concept Bottleneck Models.
Moreover, with our Concept Matrix Search algorithm we can improve CLIP
predictions on complex datasets without any additional training or fine-tuning.
The code is available at: this https URL

提出了一种使用概念瓶颈模型 (CBMs) 进行可解释分类的新架构和方法，该方法能够提供解读结果，通过引入称为概念瓶颈层的新类型层，训练 CBMs 取得了显著的准确度提升，并且通过概念矩阵搜索算法改进了复杂数据集上的预测结果。