Using physiological signals to detect natural interactive behavior

Mohammad, Yasser; Nishida, Toyoaki

doi:10.1007/s10489-010-0241-4

Cited by 32 publications

(14 citation statements)

References 15 publications

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“…From these experiments, they found a strong relationship between GSR features and mental workload. Moreover, GSR data was also used in order to detect emotions [6], [12] and stress states [7], [13]. Other studies combined the outputs of different sensors to build more reliable models.…”

Section: Previous Workmentioning

confidence: 99%

“…However, results for post hoc tests are not significant in the two cases. After studying the behavior of EEG metrics, we analyze in a next step the GSR metric (Galvanic Skin Response) which gives an indication of emotions valence or a level of stress [6], [7]. The figure below illustrates the variation of this metric accounting the average values for each participant and a threshold computed by considering the average of all the data as we mentioned in the last section.…”

Section: A Eeg and Gsr Metrics Evolution In Learningmentioning

confidence: 99%

“…It measures actually the individual degree of sweating, known as the excitation factor of emotions (low or strong). It is also correlated with cognitive load, stress [6] and frustration [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Electrophysiological Features in Cognitive Tasks: An Empirical Study

Ghali¹,

Ouellet²,

Frasson³

2016

IJIET

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Abstract-Learners' performances in intelligent tutoring systems or e-learning environments depend on various factors such as the nature of the task presented, their cognitive and affective abilities, etc. In this paper, we focus on studying in detail the variation of these different factors and more specifically the electroencephalogram (EEG metrics) and how they differ according to a category and a type of cognitive tasks. We also studied the possibility of predicting a learner's performance using feature selection and multiple regressions. Primarily, results shows that learners' scores could be predicted using in descending order the difficulty level of the task, the type of a task, the duration of a task and the EEG workload metric by building a multiple regression model that fit our data.

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Section: Previous Workmentioning

confidence: 99%

Section: A Eeg and Gsr Metrics Evolution In Learningmentioning

confidence: 99%

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Using Electrophysiological Features in Cognitive Tasks: An Empirical Study

Ghali¹,

Ouellet²,

Frasson³

2016

IJIET

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“…Human dialog knowledge is designed into two layers: domain and discourse knowledge, and it is integrated with the data-driven model in generation time. Recent studies have also addressed important points related to the use of semantic agents, multilevel concepts and behavioral model [47], automatic user-profil generation [50], or using physiological signals to detect natural interactive behaviors [89,100]. A statistical user model supported by a RTree structure and several search spaces is presented in [31].…”

Section: Modeling the User Intentionmentioning

confidence: 99%

Modeling the user state for context-aware spoken interaction in ambient assisted living

2014

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Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfil these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influenc of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.

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“…By analyzing the user's attitude based on physiological signals, many studies of HRI have utilized sensory presentation methods to elicit physical / mental conditions in real-time. [11], [12], [13] In these studies, the purpose of using the user's physiological signals is to detect and classify the emotional state of the user while executing tasks with a robot. Additionally, in several studies, robot behavior reflects the user's emotional state by analyzing physiological signals in real-time.…”

mentioning

confidence: 99%

Robot behavior control system based on user's attitude using SCL

Munekata¹,

Tsujimoto²,

Ono³

2015

2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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In order to have simple conversations between users and companion robots, it is necessary that robots evaluate the user's attitude. We developed a robot behavior control system utilizing a user's skin conductance level (SCL). SCL is used as an indication of physiological arousal. This system controls the time the robot remains in one program based on the user's arousal level before changing topics. In the results, we found several tendencies of the user's response based on the SCL. We suggest a possibility for utilizing a user's physiological data to communicate with a robot.

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Using physiological signals to detect natural interactive behavior

Cited by 32 publications

References 15 publications

Using Electrophysiological Features in Cognitive Tasks: An Empirical Study

Using Electrophysiological Features in Cognitive Tasks: An Empirical Study

Modeling the user state for context-aware spoken interaction in ambient assisted living

Robot behavior control system based on user's attitude using SCL

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