Serious Games Group, Technical University of Darmstadt, 64289 Darmstadt, Germany.
Sensors (Basel). 2024 Jan 23;24(3):742. doi: 10.3390/s24030742.
Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user's current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of daily living activities. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning (ML) methods commonly used in HAR. Although CNN architectures achieve at least 94% test accuracy in all scenarios, two traditional ML architectures surpass them in the default scenario, with support vector machines (SVMs) achieving 99.00 ± 0.34% test accuracy. The removal of all sensors except one foot sensor reduced the performance of traditional ML architectures but improved the performance of CNN architectures on our data set, with our Scaling-FCN reaching the highest accuracy of 99.86 ± 0.11% on the test set. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively, although traditional ML architectures can still compete with or even surpass CNNs when favorable input data are utilized.
移动健身应用程序为用户提供了实时反馈当前健身活动的机会。对于此类应用程序，使用可用的移动传感器（如惯性测量单元 (IMU)）准确跟踪用户的当前健身活动至关重要。卷积神经网络 (CNN) 在不同的时间序列分类任务中表现出色，包括日常活动的识别。然而，健身活动对人体活动识别任务 (HAR) 提出了独特的挑战，包括个体活动之间的相似度更高，以及用于模型训练的数据更少。在本文中，我们使用 IMU 数据评估 CNN 在健身活动识别任务 (FAR) 中的适用性，并确定输入数据大小和传感器计数对性能的影响。为此，我们将三个现有的 CNN 架构应用于 FAR 任务，并设计了第四个 CNN 变体，我们称之为缩放全卷积网络 (Scaling-FCN)。我们设计了一个预处理管道，并记录了 20 名参与者的跑步运动数据集，在该数据集中，我们评估了四个网络的各自识别性能，并将它们与 HAR 中常用的三种传统机器学习 (ML) 方法进行了比较。虽然在所有场景中，CNN 架构的测试准确率至少达到 94%，但在默认场景中，有两种传统的 ML 架构超过了它们，其中支持向量机 (SVM) 的测试准确率达到 99.00 ± 0.34%。除了一个脚部传感器外，去除所有传感器降低了传统 ML 架构的性能，但提高了我们数据集上 CNN 架构的性能，我们的 Scaling-FCN 在测试集上达到了 99.86 ± 0.11%的最高准确率。我们的结果表明，CNN 通常非常适合健身活动识别，如果有选择地丢弃传感器，可以显著提高性能，尽管当使用有利的输入数据时，传统的 ML 架构仍然可以与 CNN 竞争甚至超越 CNN。
