A Lightweight Deep Learning Solution for mmWave Human Activity Recognition in Smart Health based on Discrete Fourier Transformation
A Lightweight Deep Learning Solution for mmWave Human Activity Recognition in Smart Health based on Discrete Fourier Transformation
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DOI：
10.1109/icc45041.2023.10279304
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Yichen Gao;Shaoen Wu;Honggang Wang
中科院分区：
文献类型：
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作者：
Yichen Gao;Shaoen Wu;Honggang Wang
基于毫米波（mmWave）的人体活动识别在智能健康研究用户生活方式方面具有重要意义。在实际的健康物联网场景中，快速准确的人类活动识别至关重要。在这项工作中，我们设计并实现了一个轻量级的深度学习解决方案，用于基于离散傅里叶变换的人类活动识别。该模型具有相当少的模型参数，同时具有较高的活动识别精度。该解决方案的核心是神经网络内部的离散傅立叶变换模块，该模块在简单分类器进行活动识别之前，将毫米波雷达活动数据的时间特征转换为频率特征。我们已经在毫米波人类活动识别中对该解决方案与其他传统深度学习模型进行了广泛的评估。评估结果表明，基于dft的神经网络可以达到与其他传统神经网络模型相同的精度，但计算量非常小。
Millimeter wave (mmWave) based human activity recognition is important in smart health in terms of studying user lifestyle. In practical health IoT scenarios, fast and accurate human activity recognition is critically important. In this work, we design and implement a lightweight deep learning solution for human activity recognition based on discrete Fourier transformation. The model has a fairly small number of model parameters while offering high accuracy in activity recognition. The core of the solution is a discrete Fourier transform module inside a neural network, which converts the temporal features of mmWave radar activity data into frequency features before activity recognition is performed by a simple classifier. We have extensively evaluated this solution against other traditional deep learning models in mmWave human activity recognition. The evaluation demonstrates that the DFT-based network can achieve the same accuracy as other traditional neural network models, but with a very small computational load.