1. 常州大学计算机与人工智能学院
2. 常州大学微电子与控制工程学院
纸质出版:2025
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郭坛, 时文雅, 郇战, 等. 基于轻量化网络的帕金森步态识别方法[J]. 传感器与微系统, 2025,(4):143-147.
郭坛, 时文雅, 郇战, 等. 基于轻量化网络的帕金森步态识别方法[J]. 传感器与微系统, 2025,(4):143-147. DOI: 10.13873/J.1000-9787(2025)04-0143-05.
为了提高帕金森步态的识别效率并保持高识别精度,提出了一种基于轻量化帕金森步态识别方法—多头量化时域卷积网络(MQ-TCN)。用TCN层替换深度可分离卷积中的逐通道卷积,并部署TTQ算法,减少模型的参数量和参数复杂度。其次,该研究还分析了帕金森步态数据的冗余性,在略微损失识别精度的前提下大幅降低了模型训练所需的存储空间,进一步提升了模型在轻量设备中的可部署能力。实验结果显示:改进的MQ-TCN平均识别精度达到94.9%
参数量仅为目前最小帕金森步态识别模型的5%
不但保持高效的识别精度,还大幅度降低了模型的参数量与参数复杂度,为后续帕金森步态识别工具在轻量设备上的部署提供了参考依据。
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