Multidimensional Time Series Segmentation of Human Activity Without Prior Knowledge

With the advancement of science and technology, human activity recognition (HAR) has emerged as an important auxiliary technology for smart homes, holding broad application prospects in security monitoring, aging at home, and human-computer interaction. An essential prerequisite for continuous HAR is the segmentation of human activities, where precise segmentation is crucial for accurate recognition. To address this issue, this paper proposes a human activity segmentation method that does not require prior knowledge, i.e., an adaptive human activity multidimensional time series segmentation method based on sample entropy dual-layer hidden Markov model (SEDHM). This method primarily includes an adaptive dimension selection strategy for multidimensional sequences based on sample entropy, a multidimensional sequence segmentation algorithm based on a dual-layer hidden Markov model (HMM), and a weighted cost function for iterative optimization of the algorithm. To validate the effectiveness and robustness of this method, the paper conducts segmentation calibration of continuous human activities in a activity capture dataset using MotionBuilder. Specifically, experimental results indicate that the proposed SEDHM reduces the root mean square error (RMSE) of human activity segmentation by 14.5% compared to four other segmentation algorithms.