Detecting affective states in virtual rehabilitation

Rivas, Jesús Joel; Orihuela‐Espina, Felipe; Sucar, L. Enrique; Palafox, Lorena; Hernández-Franco, Jorge; Bianchi-Berthouze, Nadia

doi:10.4108/icst.pervasivehealth.2015.259250

Cited by 19 publications

(9 citation statements)

References 22 publications

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“…Most of the early studies [28], [29] and a number of more recent work [30], [31] focused on discrimination between people with chronic pain and those without. Other studies have similarly investigated differentiation between two levels of pain [32], [33]. One exception is [34] where 11 levels of pain were detected.…”

Section: B Automatic Pain Detection Based On Bodily Expressionsmentioning

confidence: 99%

EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions

Egede

Song

Olugbade

et al. 2020

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)

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The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of multi-modal machine learning and multimedia processing methods of chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours. The objective of the challenge is to promote research in the development of assistive technologies that help improve the quality of life for people with chronic pain via real-time monitoring and feedback to help manage their condition and remain physically active. The challenge also aims to encourage the use of the relatively underutilised, albeit vital bodily expression signals for automatic pain and painrelated emotion recognition. This paper presents a description of the challenge, competition guidelines, bench-marking dataset, and the baseline systems' architecture and performance on the Challenge's three sub-tasks: pain estimation from facial expressions, pain recognition from multimodal movement, and protective movement behaviour detection.

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Section: B Automatic Pain Detection Based On Bodily Expressionsmentioning

confidence: 99%

EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions

Egede

Song

Olugbade

et al. 2020

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)

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“…Moreover, research has been focused on the automatic recognition and evaluation of emotion and affective states [5,7] using the 3D information of users' movements. For example, in [5], authors introduced a set of 3D movement features that could be computed from the Kinect's skeleton.…”

Section: Analysis Of Motor Movements and Gesturesmentioning

confidence: 99%

Assessing the Force and Timing control of Children with Motor Problems using Elastic Displays

Cibrian¹,

Beltrán.²,

Tentori³

2018

Proceedings of the Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare – De

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Children with motor problems have difficulties to control their timing and strength. For the last four years, we have been developing BendableSound, an elastic display that enables users to play sounds when interacting with a fabric. In this paper, we report a set of measurements and coordination tasks that we integrated into BendableSound to be used to measure strength and timing control. During a deployment study with 22 children with autism presenting motor problems, we found out that the scores of the coordination tasks in BendableSound are correlated with the scores of a clinical tool measuring coordination. This result could mean that elastic displays could be used as an assessment tool for children with motor problems.

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“…In a previous work [16] a dataset was constructed, which contains the records of the rehabilitation sessions of 2 stroke patients that attended therapies to recover the mobility of their upper limb. The patients (an extroverted man and an introverted woman, as judged by the psychiatrists who contributed to this study) participated in 45-minute average sessions that took place in different days (max 3 per week) in a period of 4 weeks.…”

Section: Hand Movements and Fingers' Pressure Datasetmentioning

confidence: 99%

Automatic recognition of pain, anxiety, engagement and tiredness for virtual rehabilitation from stroke: A marginalization approach

Rivas

Palafox

Hernández-Franco

et al. 2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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Virtual rehabilitation taps affective computing to personalize therapy. States of anxiety, pain and engagement (affective) and tiredness (physical or psychological) were studied to be inferable from metrics of 3D hand location-proxy of hand movement-and fingers' pressure relevant for upper limb motor recovery. Features from the data streams characterized the motor dynamics of 2 stroke patients attending 10 sessions of motor virtual rehabilitation. Experts tagged states manifestations from videos. We aid classification contributing with a marginalization mechanism whereby absent input is reconstructed. With the hand movement information absent, marginalization statistically outperformed a base model where such input is ignored. Marginalized classification performance was (Area below ROC curve: µ ± σ) 0.880 ± 0.173 and 0.738 ± 0.177 for each patient. Marginalization aid classification sustaining performance under input failure or permitting different sensing settings.

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Detecting affective states in virtual rehabilitation

Cited by 19 publications

References 22 publications

EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions

EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions

Assessing the Force and Timing control of Children with Motor Problems using Elastic Displays

Automatic recognition of pain, anxiety, engagement and tiredness for virtual rehabilitation from stroke: A marginalization approach

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