In recent years, due to changing the human lifestyle, the number of sport trainers has been increased. The conventional classifiers as Naive Bayes (NB), Decision Trees (DT) and Convolutional Neural Networks (CNNs) can be used in this domain to recognize and count sports activities of subjects and provide them qualified feedback. This paper uses literature studies and selected sport activities, namely squats, pull-ups and dips as the dataset based on three UWB sensors with additional inertial data, which contains the reduced data set consisting of 17 training sets and next for CNN training the 1444 samples describing exercises and 2024 samples with breaks, which were grouped in the ratio 70:15:15. The recognition accuracy of the NB and DT were 89.4 and 92.9 accordingly. Next, the extensive performance analysis of the CNN based on experiments for different kernel sizes, different number of filters for single and dual layer networks was carried out. Moreover, the innovative model for sport activities recognition in the form the combination of several networks forming Ensemble Neural Network (ENN) was created. The accuracy was at the level 94.81 of CNN and exceeded 95% of ENN. The proposed prototype of the measurement system and data acquisition platform for sport activities recognition was highlighted as the great potential in the privacy-training sport system.

近年来，由于人类生活方式的改变，体育教练员的数量不断增加。传统分类器如朴素贝叶斯 (NB)、决策树 (DT) 和卷积神经网络 (CNN) 可用于该领域，以识别和统计受试者的体育活动并为他们提供合格的反馈。本文使用文献研究和选定的体育活动，即深蹲、引体向上和俯卧撑作为数据集，基于三个带有额外惯性数据的 UWB 传感器，其中包含由 17 个训练集组成的缩减数据集，接下来用于 CNN 训练的 1444 个样本描述练习和 2024 个带休息的样本，按 70:15:15 的比例分组。NB和DT的识别准确率分别为89.4和92.9。下一个，基于不同内核大小、不同数量的单层和双层网络滤波器的实验，对 CNN 进行了广泛的性能分析。此外，还创建了多个网络组合形成集成神经网络 (ENN) 的体育活动识别创新模型。准确率达到 CNN 的 94.81 水平，超过 ENN 的 95%。所提出的用于体育活动识别的测量系统和数据采集平台的原型被强调为在隐私训练体育系统中的巨大潜力。准确率达到 CNN 的 94.81 水平，超过 ENN 的 95%。所提出的用于体育活动识别的测量系统和数据采集平台的原型被强调为在隐私训练体育系统中的巨大潜力。准确率达到 CNN 的 94.81 水平，超过 ENN 的 95%。所提出的用于体育活动识别的测量系统和数据采集平台的原型被强调为在隐私训练体育系统中的巨大潜力。