Activity recognition and classification are emerging fields of research that enable many human-centric applications in the sports domain. One of the most critical and challenged aspects of coaching is improving the performance of athletes. Hence, in this paper, the Adaptive Evolutionary Neuro-FuzzyInference System (AENFIS) has been proposed for sports person activity classification based on the biomedical signal, trial accelerator data and video surveillance. This paper obtains movement data and heart rate from the developed sensor module. This small sensor is patched onto the user’s chest to get physiological information. Based on the time and frequency domain features, this paper defines the fuzzy sets and assess the natural grouping of data via expectation-maximization of the probabilities. Sensor data feature selection and classification algorithms are applied, and a majority voting is utilized to choose the most representative features. The experimental results show that the proposed AENFIS model enhances accuracy ratio of 98.9%, prediction ratio of 98.5%, the precision ratio of 95.4, recall ratio of 96.7%, the performance ratio of 97.8%, an efficiency ratio of 98.1% and reduces the error rate of 10.2%, execution time 8.9% compared to other existing models.

中文翻译：

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基于模糊模型和视频监控的运动训练阶段动作分类与分析

活动识别和分类是新兴的研究领域，可以在体育领域实现许多以人为中心的应用。教练中最关键和最具挑战性的方面之一是提高运动员的表现。因此，在本文中，基于生物医学信号、试验加速器数据和视频监控，自适应进化神经模糊推理系统（AENFIS）被提出用于运动员活动分类。本文从开发的传感器模块中获取运动数据和心率。这个小型传感器贴在用户的胸部以获取生理信息。基于时域和频域特征，本文定义了模糊集，并通过概率的期望最大化来评估数据的自然分组。应用传感器数据特征选择和分类算法，并利用多数投票来选择最具代表性的特征。实验结果表明，提出的AENFIS模型提高了98.9%的准确率、98.5%的预测率、95.4的准确率、96.7%的召回率、97.8%的性能比、98.1%的效率比，并降低了与其他现有模型相比，错误率为 10.2%，执行时间为 8.9%。