Deep-Learning-for-Human-Activity-Recognition:CNN,DeepConvLSTM,SDAE和LightGBM的Keras实施,用于基于传感器的人类活动识别(HAR)

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用于人类活动识别的深度学习(和机器学习) CNN,DeepConvLSTM,SDAE和LightGBM的Keras实施,用于基于传感器的人类活动识别(HAR)。 该存储库包含卷积神经网络(CNN)[1],深度卷积LSTM(DeepConvLSTM)[1],堆叠降噪自动编码器(SDAE)[2]和用于人类活动识别(HAR)的Light GBM的keras(tensorflow.keras)实现。 )使用智能手机传感器数据集, UCI智能手机[3]。 表1.在UCI智能手机数据集上的五种方法之间的结果摘要。 方法 准确性 精确 记起 F1分数 轻型GBM 96.33 96.58 96.37 96.43 CNN [1] 95.29 95.46 95.50 95.47 DeepConvLSTM [1] 95.66 95.71 95.84 95.72 SDAE [

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