Emotion recognition based on physiological data has attracted increasing attention in physiological monitoring, affective computing, and other fields. This paper proposes a method to classify human's emotion for health monitoring in physical activities by using machine learning. Participants completed the experiment including walking, running, and other physical activities. The data of photoplethysmography (PPG) and electrodermal activity (EDA) were recorded by wearable sensors on participants. After the data processing and feature extraction, two classifiers, support vector machine (SVM) and random forest (RF) were applied independently on the dataset to classify human's emotion, including calm, excited, relaxed, bored, and afraid. As a result, the SVM classifier achieved an accuracy of 81.87% and the accuracy of RF classifier is 86.61%. These results demonstrated the effectiveness of the proposed method on emotion recognition in human's physical activities.

To investigate the aseismic behavior of steel-jacket strengthened columns with recycled aggregate concrete, low-cycle reversed loading tests were conducted on one normal reinforced concrete column and nine steel-jacket strengthened columns with recycled aggregate concrete. Then in ABAQUS, using the fiber beam model verified by experiments, the numerical simulation on steel-jacket strengthened columns was conducted, and the related influencing factors were analyzed by parametric analysis. The results show that the aseismic behavior of columns is significantly improved by taking this strengthen scheme. Based on the range of parameters adopted in this research, the lateral resistance capacity increases with the increase of axial compression ratio in a range less than 0.58, but it decreases obviously and the degree of damage develops faster with the increase of axial compression ratio in a range larger than 0.58. The aseismic performance can be improved by increasing the thickness of steel-jacket, but the effect gradually decreases with the increase of thickness. The axial compression ratio of the original column has a great influence on the mechanical properties and failure modes of the strengthened column, and the initial compressive-bending stress significantly reduces the flexural capacity of the strengthened columns which have high axial compression ratio. When the strengthened column is under the effective constraint of steel-jacket, the influence of recycled aggregate concrete on the overall bearing capacity of columns is not obvious, but it would lead to the decrease of aseismic performance.