September 12, 2012IEEE Transactions on Human-Machine Systems

Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

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Population

Elderly residents at two nursing homes

Authors

MSManida SwangnetrNorth Carolina State University DKDavid KaberUniversity of Central Florida

Discussion

Member takes

Implication

May improve emotion detection in elderly-robot interactions; leaves open validation in larger clinical cohorts.

Structured PICO

Population

Elderly residents at two nursing homes

Intervention

Three-stage emotional state classification algorithm using physiological feature extraction (heart rate and galvanic skin response), statistical-based feature selection, and a machine-learning model

Outcome

Accurate classification of patient emotional states (arousal and valence)

A new wavelet-based algorithm using heart rate and galvanic skin response effectively classifies emotional states in elderly patients interacting with nursing robots.

Cite This Study

Swangnetr et al. (2012) studied this question.

synapsesocial.com/papers/69d99f175e5bcb4e3b837282 https://doi.org/10.1109/tsmca.2012.2210408

View Full Paper

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Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider