Research on the Design of Wearable Mental Health Monitoring Device Based on RT-Thread Multi-threaded Programme
In order to achieve long-term monitoring of individual mental health and obtain real-time data to objectively assess the psychological condition,the study designs a wearable device that collects users'voice,behavioural and environmental data,and com-pletes the assessment of mental health with the help of the Principal Component Analysis feature compression method and the integra-tion idea.The experimental results indicate that the time-domain amplitude variance of the synthesized acceleration and the distribu-tion of the characteristic values of the second resonance peak can effectively distinguish mental health states.The distribution of vari-ance values among low scoring participants in the scale is relatively concentrated,and the density curve shows a Gaussian distribution in the range of 1 000-3 500 Hz,which is consistent with the actual mental health status of the participants.The decision classification model designed for research has a high accuracy,with a root mean square error converging to 0.563,a high recall rate,and excellent overall performance of the model.The wearable mental health monitoring device designed in this study can effectively monitor user be-havioural data in the long term,providing a convenient,objective,comprehensive and personalized way of mental health manage-ment.
RT-Thread multithreading programmespeech eigenvalueslow-frequency data eigenvaluesprincipal component analysispearson correlation coefficient
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