  Emotion identification from gait aims to automatically determine persons
affective state, it has attracted a great deal of interests and offered immense
potential value in action tendency, health care, psychological detection and
human-computer(robot) interaction.In this paper, we propose a new method of
identifying emotion from natural walking, and analyze the relevance between the
traits of walking and affective states. After obtaining the pure acceleration
data of wrist and ankle, we set a moving average filter window with different
sizes w, then extract 114 features including time-domain, frequency-domain,
power and distribution features from each data slice, and run principal
component analysis (PCA) to reduce dimension. In experiments, we train SVM,
Decision Tree, multilayerperception, Random Tree and Random Forest
classification models, and compare the classification accuracy on data of wrist
and ankle with respect to different w. The performance of emotion
identification on acceleration data of ankle is better than wrist.Comparing
different classification models' results, SVM has best accuracy of identifying
anger and happy could achieve 90:31% and 89:76% respectively, and
identification ratio of anger-happy is 87:10%.The anger-neutral-happy
classification reaches 85%-78%-78%.The results show that it is capable of
identifying personal emotional states through the gait of walking.
