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          A.I.4Healthcare:  Applications from Audio to Spontaneous Physical  Activity

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½²×ùÌáÒª£ºIn  a traditional or classical A.I. paradigm, the human hand-crafted features are  extracted from the data by several signal processing methods, e.g., Fourier  transformation, wavelet transformation, empirical mode decomposition, etc.  Subsequently, a machine learning model can be trained when fed with those  features. Even though the performance and the robustness of the model could be  feasible for further implementations in real practice, the feature engineering  process, which needs specific domain knowledge, is still time-consuming, and  expensive. As an emerging technique, deep learning, can make it possible to make  models learn higher representations from the data itself. In  this presentation, Dr. Qian will present his main work in Technical University  of Munich, Germany, and his most recent work in The University of Tokyo. For his  work in Germany, the audio data can be used for diagnosing some diseases related  to the knowledge of body acoustics. For his work in Japan, the spontaneous  physical activity data can be good representations for screening the patients  suffering from the major depressive disorder.

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