Abstract
In the study, we developed a smartphone-based electronical scale validity assessment framework. 374 college students are recruited to fill in Beck Depression Inventory. A total of 544 filling of scales are collected, which may be filled accordingly or concealed. Via an electronical scale based WeChat applet and backend application, temporal and spatial behavioral data of subjects during the scale-filling process are collected. We established an assessment model of the validity of the scale-filling based on the behavior data with machine learning approaches. The result shows that smartphone behavior has significant features in the dimension of time and space under different motivations. The framework achieves an valuable assessment of the effectiveness of the scale, whose key indicators such as accuracy, sensitivity and precision are over 80% under multiple dimension behavior data classification. The framework has a good application prospect in the field of psychological screening.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China [Grant No. 61632014, No. 61627808, No. 61210010], in part by the National Basic Research Program of China (973 Program) under Grant 2014CB744600, in part by the Program of Beijing Municipal Science & Technology Commission under Grant Z171100000117005.
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Yang, M., Tang, J., Tang, L., Hu, B. (2019). Smartphone Behavior Based Electronical Scale Validity Assessment Framework. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_35
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