Today, there are standard and well established procedures within the Human Activity Recognition (HAR) pipeline. However, some of these conventional approaches lead to accuracy overestimation. In particular, sliding windows for data segmentation followed by standard random k-fold cross validation, produce biased results. An analysis of previous literature and present-day studies, surprisingly, shows that these are common approaches in state-of-the-art studies on HAR. It is important to raise awareness in the scientific community about this problem, whose negative effects are being overlooked. Otherwise, publications of biased results lead to papers that report lower accuracies, with correct unbiased methods, harder to publish. Several experiments with different types of datasets and different types of classification models allow us to exhibit the problem and show it persists independently of the method or dataset.

针对人体活动识别(HAR)中常见的问题，本研究通过实验证明，使用滑动窗口和标准的随机k折交叉验证方法进行数据分段及验证会导致结果的高估，这对科学界具有重要的警示意义，并呼吁使用正确和无偏的方法来减少此问题的发生。

太美好以致难以置信：对人类活动识别中（再）现行做法的性能高估