Modeling of Child Stress-State Identification Based on Biometric Information in Mobile Environment

Table 1

Summary of related studies.

Objects (topics)	Signals used	Analysis methodologies	References
Automatic identification of stress causes of employees	GSR	Adaptive windowing	Bakker et al. [5]
Detecting real-world driving stress	HR, EMG, respiration	Continuous correlations	Healey and Picard [6]
Multilevel assessment model for monitoring elder’s health condition	HR, EEG, ECG	SVM, DT, expectation maximization	Jung and Yoon [7]
Personal health system for detecting stress	GSR	Latent Dirichlet allocation, SVM	Setz et al. [8]
Stress elicitation by examination	HR	Latent Dirichlet allocation	Melillo et al. [9]
	Voice, GSR	DT, SVM, K-means	Kurniawan et al. [10]
Activity-aware mental stress detection (sitting, standing, and walking)	HR, GSR, accelerometer	DT, SVM, Bayes network	Sun et al. [18]
Automatic detection of the expiratory and inspiratory phases in newborn cry signal	Voice	Hidden Markov model	Abou-Abbas et al. [20]
Automatic classification of infant crying for early disease detection	Voice	Genetic selection of a fuzzy model	Rosales-Pérez et al. [21]
Automatic cry detection in early childhood	Voice	Gentle-boost	Ruvolo and Movellan [11]