Discriminating affective state intensity using physiological responses

Gasparini, Francesca; Giltri, Marta; Bandini, Stefania

doi:10.1007/s11042-020-09114-y

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…The audios and math calculations were chosen following the results obtained in [ 12 ] in order to induce equal levels of relaxation and equal levels of cognitive load. There was no intention of inducing several levels of cognitive load intensity.…”

Section: Methodsmentioning

confidence: 99%

“…Nowadays, physiological responses have become useful to better understand how different types of people react to different situations. They can be considered reliable indicators of uncontrolled and fair human reactions to external stimuli and are now widely used to detect and recognize affective states [ 11 ] and human behavior in different environments [ 12 ], and to detect different human emotions [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

Gasparini

Grossi

Giltri

et al. 2023

Sensors

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In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

Gasparini

Grossi

Giltri

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nowadays wearable devices, and smartphones easily integrate several sensors that measure physiological signals [36], making the acquisition of this data more comfortable and usable in day-life activities, even in case of elderly people. Physiological signals are already widely used in the emotion and affect recognition fields, having proved their effectiveness and usefulness in this area [6,15]. They have a recent wide application for interpreting and analyzing user's sleep behavior [29] as well as for daily activities recognition [36] and in e-health 1 3 application for monitoring and remote assistance [24].…”

Section: Introductionmentioning

confidence: 99%

Social and Active Inclusion of the Elderly in the City Through Affective Walkability

Bandini

Gasparini

2021

Rev Socionetwork Strat

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The development of cities aware of the needs of all citizens is a priority, especially in the case of elderly people. In this context, particular attention should be paid to the analysis of walkability. It has been proved that performing walking activities produces significant benefits both for physical and mental health, above all in elderly subjects. Besides traditional criteria adopted to evaluate walkability, we here propose a novel approach defined affective walkability. Being able to interpret the emotions of elderly walking in the urban environment and interacting with vehicles and other citizens, it is possible to evaluate if and to which extent an environment is perceived safe, comfortable and walkable. One way to obtain quantitative measures of walkability is to assess safety perception relying on physiological signals that can be considered indicators of emotions and mood. The assessment of affective walkability requires the design and performance of rigorous experiments to properly collect data. In this paper, the proposal of an affective walkability is presented and the developed experimental protocols, their performance and preliminary results are illustrated.

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Using Wearable Devices for Emotion Recognition in Mobile Human- Computer Interaction: A Review

Wang¹,

Zhang²

2022

HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments

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Discriminating affective state intensity using physiological responses

Cited by 4 publications

References 43 publications

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

Social and Active Inclusion of the Elderly in the City Through Affective Walkability

Using Wearable Devices for Emotion Recognition in Mobile Human- Computer Interaction: A Review

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