bidirectional_HAR:使用双向LSTM进行人类活动识别
LSTM用于人类活动识别
使用智能手机传感器数据集（腰部连接的手机）基于LSTM的人类活动识别。 将运动类型分为以下六类：
步行，
WALKING_UPSTAIRS，
WALKING_DOWNSTAIRS，
坐下
常设，
铺设。
数据集
可以从下载数据集
点击此以观看有关如何收集数据的视频
通过应用噪声滤波器对传感器信号（加速度计和陀螺仪）进行预处理，然后在2.56秒和50％重叠（128个读数/窗口）的固定宽度滑动窗口中进行采样。 使用巴特沃斯低通滤波器将具有重力和人体运动成分的传感器加速度信号分离为人体加速度和重力。 假定重力仅具有低频分量，因此使用了具有0.3 Hz截止频率的滤波器。
模型
在此仓库中，我们采用了两层堆叠的基本LSTM，几乎使用了原始数据：只有重力效应已从加速度计中滤出，作为另一个3D功能的预处理步骤，以作为帮助学习的输入。
用法
安装TensorFlow r
文件下载
资源详情
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