Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.,  propose a new method of identifying emotion from natural walking, and analyze the relevance beten the traits of walking and affective states. After obtaining the pure acceleration data of wrist and ankle,  set a moving average filter window with different sizes w, then extract 114 features including time-domain, frequency-domain, por and distribution features from each data slice, and run principal component analysis (PCA) to reduce dimension. In experiments,  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.
['Liqing Cui', 'Shun Li', 'Wan Zhang', 'Zhan Zhang', 'Tingshao Zhu']
I am writing a research paper on a idea Identifying Emotion from Natural Walking
[{"from": "human", "value": "I am writing a research paper on a idea Identifying Emotion from Natural Walking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.,  propose a new method of identifying emotion from natural walking, and analyze the relevance beten the traits of walking and affective states. After obtaining the pure acceleration data of wrist and ankle,  set a moving average filter window with different sizes w, then extract 114 features including time-domain, frequency-domain, por and distribution features from each data slice, and run principal component analysis (PCA) to reduce dimension. In experiments,  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."}]