计算机科学
块(置换群论)
良性阵发性位置性眩晕
增采样
卷积(计算机科学)
核(代数)
任务(项目管理)
计算
人工智能
动作(物理)
功能(生物学)
失败
模式识别(心理学)
算法
图像(数学)
人工神经网络
眩晕
数学
医学
并行计算
物理
量子力学
几何学
外科
管理
组合数学
进化生物学
经济
生物
作者
Jin Xiao,Jiaxuan Jiang,Yuee Li,Zhong Wang
标识
DOI:10.1016/j.bspc.2023.105601
摘要
In clinical examination and repositioning for BPPV(benign paroxysmal positional vertigo), analyzing the head position trajectory at the algorithm level to obtain information on each examination and repositioning posture helps to provide guidance for subsequent treatment, which has important practical value in clinical applications. This paper uses the improved ShuffleNetV2 on the basic the data obtained by the swivel chair for action recognition, and locates the interval where the action occurs. Specific network improvements include: the introduction of the SE channel attention block, which improves the performance of the network with a small amount of parameters and computation; the Hardswish activation function is used instead of ReLU in the original network; by reducing the layers in the network, deleting the second 1*1 convolution in the right branch of the downsampling block, and changing the kernel size of the DW convolution, the network can be lightweight while maximizing the capability of action recognition. The network's performance has been enhanced, resulting in an accuracy of 98.53% with only 71,444 parameters and 15.76M FLOPs, while maintaining a recognition speed of 97.1 images per second. It achieves a good balance between speed and accuracy, enabling it to effectively accomplish the task of action recognition.
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