Electrodermal activity: Explorations in the psychophysiology of engagement with social robots in dementia

Perugia, Giulia; Rodríguez-Martín, Daniel; Boladeras, Marta Díaz; Mallofré, Andreu Català; Barakova, Emilia; Rauterberg, Matthias

doi:10.1109/roman.2017.8172464

Cited by 25 publications

(11 citation statements)

References 27 publications

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“…A systematic assessment of engagement through behavior could be greatly beneficial to measure the meaningfulness and effectiveness of activities for the person with dementia and could be used as a complement to the assessment of clinical benefits. We define engagement as the psychological state of well-being, enjoyment and active involvement that is triggered by meaningful activities and causes people with dementia to be absorbed by the activity, more energetic and in a more positive mood (Perugia et al, 2017c ).…”

Section: Introductionmentioning

confidence: 99%

Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB)

et al. 2018

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Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to grasp how engagement is naturally expressed through behavior or too complex to be traced back to an overall engagement state. We carried out a longitudinal study to develop a coding system of engagement-related behavior that could tackle these issues and to create an evidence-based model of engagement to make meaning of such a coding system. Fourteen elderlies with mild to moderate dementia took part in the study. They were involved in two activities: a game-based cognitive stimulation and a robot-based free play. The coding system was developed with a mixed approach: ethographic and Laban-inspired. First, we developed two ethograms to describe the behavior of participants in the two activities in detail. Then, we used Laban Movement Analysis (LMA) to identify a common structure to the behaviors in the two ethograms and unify them in a unique coding system. The inter-rater reliability (IRR) of the coding system proved to be excellent for cognitive games (kappa = 0.78) and very good for robot play (kappa = 0.74). From the scoring of the videos, we developed an evidence-based model of engagement. This was based on the most frequent patterns of body part organization (i.e., the way body parts are connected in movement) observed during activities. Each pattern was given a meaning in terms of engagement by making reference to the literature. The model was tested using structural equation modeling (SEM). It achieved an excellent goodness of fit and all the hypothesized relations between variables were significant. We called the coding system that we developed the Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the model the Evidence-based Model of Engagement-related Behavior (EMODEB). To the best of our knowledge, the ELICSE and the EMODEB constitute the first formalization of engagement-related behavior for dementia that describes how behavior unfolds over time and what it means in terms of engagement.

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Section: Introductionmentioning

confidence: 99%

Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB)

et al. 2018

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“…When stress stimulates the sympathetic nervous system, it also influences physiological parameters, such as muscle activity, heart rate, and pupil dilation [ 8 ]. EDA measurements have also been used to track mood changes in older people [ 9 ], assess the functional and behavioural health of people with dementia [ 10 ], investigate people with dementia's reactions to moral dilemmas [ 11 ], and compare individuals with dementia's engagement in social activities [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Using Measurements of Electrodermal Activity in the Assessment of Problematic Behaviour in Dementia

Melander

Kikhia

Olsson

et al. 2018

Dement Geriatr Cogn Disord Extra

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Background: A major and complex challenge when trying to support individuals with dementia is meeting the needs of those who experience changes in behaviour and mood. Aim: To explore how a sensor measuring electrodermal activity (EDA) impacts assistant nurses’ structured assessments of problematic behaviours amongst people with dementia and their choices of care interventions. Methods: Fourteen individuals with dementia wore a sensor that measured EDA. The information from the sensor was presented to assistant nurses during structured assessments of problematic behaviours. The evaluation process included scorings with the instrument NPI-NH (Neuropsychiatric Inventory-Nursing Home version), the care interventions suggested by assistant nurses to decrease problematic behaviours, and the assistant nurses’ experiences obtained by focus group interviews. Results: The information from the sensor measuring EDA was perceived to make behavioural patterns more visual and clear, which enhanced assistant nurses’ understanding of time-related patterns of behaviours. In turn, this enhancement facilitated timely care interventions to prevent the patterns and decrease the levels of problematic behaviour. Conclusion: With the addition of information from the sensor, nursing staff could target causes and triggers in a better way, making care interventions more specific and directed towards certain times throughout the day to prevent patterns of problematic behaviours.

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“…For instance, Greco et al 11 found statistical evidence concerning healthy patients and patients with bipolar disorder through features of EDA signals. Moreover, Perugia et al 12 discovered significant correlations between EDA signals and engagement in dementia patients.…”

Section: Introductionmentioning

confidence: 99%

Automatic Artifact Recognition and Correction for Electrodermal Activity in Uncontrolled Environments

Llanes-Jurado

Carrasco-Ribelles

Alcañíz

et al. 2021

Preprint

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Scholars are increasingly using electrodermal activity (EDA) to assess cognitive-emotional states in laboratory environments, while recent applications have recorded EDA in uncontrolled settings, such as daily-life and virtual reality (VR) contexts, in which users can freely walk and move their hands. However, these records can be affected by major artifacts stemming from movements that can obscure valuable information. Previous work has analyzed signal correction methods to improve the quality of the signal or proposed artifact recognition models based on time windows. Despite these efforts, the correction of EDA signals in uncontrolled environments is still limited, and no existing research has used a signal manually corrected by an expert as a benchmark. This work investigates different machine learning and deep learning architectures, including support vector machines, recurrent neural networks (RNNs), and convolutional neural networks, for the automatic artifact recognition of EDA signals. The data from 44 subjects during an immersive VR task were collected and cleaned by two experts as ground truth. The best model, which used an RNN fed with the raw signal, recognized 72% of the artifacts and had an accuracy of 87%. An automatic correction was performed on the detected artifacts through a combination of linear interpolation and a high degree polynomial. The evaluation of this correction showed that the automatically and manually corrected signals did not present differences in terms of phasic components, while both showed differences to the raw signal. This work provides a tool to automatically correct artifacts of EDA signals which can be used in uncontrolled conditions, allowing for the development of intelligent systems based on EDA monitoring without human intervention.

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Electrodermal activity: Explorations in the psychophysiology of engagement with social robots in dementia

Cited by 25 publications

References 27 publications

Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB)

Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB)

The Impact of Using Measurements of Electrodermal Activity in the Assessment of Problematic Behaviour in Dementia

Automatic Artifact Recognition and Correction for Electrodermal Activity in Uncontrolled Environments

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