Wallhack1.8k

该数据集名为Wallhack1.8k，包含了1806个在测试环境中收集的人体活动CSI幅度频谱图，该环境旨在评估跨场景和跨系统的人体活动识别性能。该数据集主要用于训练基于卷积神经网络（CNN）的人体活动识别（HAR）模型，以区分粗略和精细的身体运动。该数据集的样本量为1806，其任务是人体活动识别。Named Wallhack1.8k, this dataset comprises 1806 CSI amplitude spectrograms of human activities collected in a test environment, which is designed to evaluate the performance of human activity recognition (HAR) across diverse scenarios and systems. This dataset is mainly utilized for training Convolutional Neural Network (CNN)-based Human Activity Recognition (HAR) models to differentiate between coarse and fine-grained bodily motions. It has a sample size of 1806, with the core task being human activity recognition.