Analyze and select the characteristic physiological parameters that can effectively feedback the pilot's physiological state, establish a high simulation flight test platform system, and conduct simulated fatigue flight test. Real-time acquisition of four different physiological signals, namely, EMG, ECG, skin electricity and respiratory signals, during the flight of pilots under different physiological conditions, and statistics and analysis of subjective questionnaire of flight fatigue. The physiological signals collected in different states during the experiment are filtered and denoised, and the initial data are preprocessed, so as to improve the jumble of data. And calculating and analyzing the characteristic physiological parameters of EMG signals, heart rate and heart rate variability of ECG signals and other time-frequency domain indexes; Analyze the phase average, standard deviation, maximum value and other time-frequency domain indexes of skin electrical signals and respiratory signals. Organize the multimodal physiological parameters of the participants, which is convenient for later machine learning analysis.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Pilot Multi-physiological Information Monitoring Based on SVM Method

  • Fulong Jing,
  • Lin Li,
  • Zhong Liu,
  • Yue Wang,
  • Yang Du

摘要

Analyze and select the characteristic physiological parameters that can effectively feedback the pilot's physiological state, establish a high simulation flight test platform system, and conduct simulated fatigue flight test. Real-time acquisition of four different physiological signals, namely, EMG, ECG, skin electricity and respiratory signals, during the flight of pilots under different physiological conditions, and statistics and analysis of subjective questionnaire of flight fatigue. The physiological signals collected in different states during the experiment are filtered and denoised, and the initial data are preprocessed, so as to improve the jumble of data. And calculating and analyzing the characteristic physiological parameters of EMG signals, heart rate and heart rate variability of ECG signals and other time-frequency domain indexes; Analyze the phase average, standard deviation, maximum value and other time-frequency domain indexes of skin electrical signals and respiratory signals. Organize the multimodal physiological parameters of the participants, which is convenient for later machine learning analysis.