基于多传感器融合的优化深度卷积神经网络用于拳击活动识别

IF 1.1 4区 医学 Q4 ENGINEERING, MECHANICAL
Brindha Jayakumar, Nallavan Govindarajan
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引用次数: 0

摘要

通过自动特征提取,深度学习的最新进展极大地增强了对体育运动中运动员活动的识别能力。在我们提出的工作中,我们专注于识别拳击运动中的六种不同拳法。我们在预处理阶段采用了滑动窗口技术,在拳击活动识别系统中转换从惯性测量单元(IMU)传感器获得的时间序列数据。我们的方法影响了基于传感器融合的深度卷积神经网络(DCNN)分类模型,从而准确识别出各种拳击动作,并获得了令人印象深刻的 F1 分数。该系统熟练地区分了类似的活动,例如刺拳和勾拳,由于手臂弯曲的微妙变化,现有系统对这两种拳法的分类出现了错误。通过实验,我们确定了拳击活动识别的最佳窗口大小,即 15-20 帧(相当于 0.15-0.25 秒)。窗口大小的选择显著缩短了推理时间。为了评估我们提出的模型,我们将其与标准 DCNN 和优化 DCNN 模型进行了比较。我们提出的优化 DCNN 模型提高了识别准确率,达到了令人印象深刻的 99%,F1 分数也提高了 87%。此外,该模型还显著缩短了推理时间,不到 1 毫秒。总之,我们的研究通过利用深度学习的力量,为体育相关的球员活动识别领域做出了贡献。通过将这些技术巧妙地结合在一起,我们在识别各种拳击时实现了出色的准确性、精确性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-sensor fusion based optimized deep convolutional neural network for boxing punch activity recognition
Recent advancements in deep learning have significantly enhanced the recognition of player activities in sports by enabling automatic feature extraction. In our proposed work, we focus on recognizing six distinct punches in the context of boxing. We incorporate the sliding window technique during the pre-processing stage to transform the time-series data obtained from Inertial Measurement Unit (IMU) sensors in a boxing punch activity recognition system. Our approach influences a sensor fusion-based Deep Convolutional Neural Network (DCNN) classification model to identify various boxing punches accurately, achieving an impressive F1 score. The system demonstrates proficiency in distinguishing similar activities, such as jab and hook punches where the existing systems made misclassifications due to subtle variations in arm flexion that differentiate the two. Through experimentation, we identify an optimal window size for boxing punch activity recognition, which falls within the range of 15–20 frames (equivalent to 0.15–0.25 s). This window size selection results in a notable reduction in inference time. To evaluate our proposed model, we conduct comparisons with a standard DCNN and an optimized DCNN model. Our proposed optimized DCNN model demonstrates enhanced recognition accuracy, achieving an impressive 99%, coupled with an improved F1 score of 87%. Furthermore, the model displays a remarkable reduction in inference time, clocking in at less than 1 ms. Overall, our research contributes to the field of sports-related player activity recognition by employing the power of deep learning. By expertly combining these techniques, we achieve remarkable accuracy, precision, and efficiency in recognizing various boxing punches.
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来源期刊
CiteScore
3.50
自引率
20.00%
发文量
51
审稿时长
>12 weeks
期刊介绍: The Journal of Sports Engineering and Technology covers the development of novel sports apparel, footwear, and equipment; and the materials, instrumentation, and processes that make advances in sports possible.
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