Analysis of cognitive states during bodily exploration of mathematical concepts in visually impaired children

Volta, Erica; Niewiadomski, Radosław; Olugbade, Temitayo; Gilio, Carla; Cocchi, Elena; Bianchi-Berthouze, Nadia; Gori, Monica; Volpe, Gualtiero

doi:10.1109/acii.2019.8925521

Cited by 9 publications

(15 citation statements)

References 39 publications

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“…during a consultation interview with a therapist [25]) or the type of activity is constant throughout (e.g. the detection of pain and anxiety in game-based physical rehabilitation [53,71]). Bodily expression is also used to inform healthcare applications, e.g.…”

Section: Affective Movement Behavior Detectionmentioning

confidence: 99%

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

Wang

Gao

Mathur

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Protective behavior exhibited by people with chronic pain (CP) during physical activities is very informative to understanding their physical and emotional states. Existing automatic protective behavior detection (PBD) methods rely on pre-segmentation of activities predefined by users. However, in real life, people perform activities casually. Therefore, where those activities present difficulties for people with CP, technology-enabled support should be delivered continuously and automatically adapted to activity type and occurrence of protective behavior. Hence, to facilitate ubiquitous CP management, it becomes critical to enable accurate PBD over continuous data. In this paper, we propose to integrate human activity recognition (HAR) with PBD via a novel hierarchical HAR-PBD architecture comprising graph-convolution and long short-term memory (GC-LSTM) networks, and alleviate class imbalances using a class-balanced focal categorical cross-entropy (CFCC) loss. Through in-depth evaluation of the approach using a CP patients' dataset, we show that the leveraging of HAR, GC-LSTM networks, and CFCC loss leads to clear increase in PBD performance against the baseline (macro F1 score of 0.81 vs. 0.66 and precision-recall area-under-the-curve (PR-AUC) of 0.60 vs. 0.44). We conclude by discussing possible use cases of the hierarchical architecture in CP management and beyond. We also discuss current limitations and ways forward.

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Section: Affective Movement Behavior Detectionmentioning

confidence: 99%

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

Wang

Gao

Mathur

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…In [28] where the body itself was leveraged as a learning tool, the authors found performance of 0.63 F1 score (with 10fold cross-validation) in discriminating between three levels of engagement in children using full-body features. The setting used in their study is similar to that investigated in [10] based on the weDraw-1 Movement dataset which consists of motion capture data captured while children explored mathematical problems using their bodies, e.g.…”

Section: B Learningmentioning

confidence: 99%

The AffectMove 2021 Challenge - Affect Recognition from Naturalistic Movement Data

Olugbade

Sagoleo

Ghisio

et al. 2021

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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We ran the first Affective Movement Recognition (AffectMove) challenge that brings together datasets of affective bodily behaviour across different real-life applications to foster work in this area. Research on automatic detection of naturalistic affective body expressions is still lagging behind detection based on other modalities whereas movement behaviour modelling is a very interesting and very relevant research problem for the affective computing community. The AffectMove challenge aimed to take advantage of existing body movement datasets to address key research problems of automatic recognition of naturalistic and complex affective behaviour from this type of data. Participating teams competed to solve at least one of three tasks based on datasets of different sensors types and reallife problems: multimodal EmoPain dataset for chronic pain physical rehabilitation context, weDraw-1 Movement dataset for maths problem solving settings, and multimodal Unige-Maastricht Dance dataset. To foster work across datasets, we also challenged participants to take advantage of the data across datasets to improve performances and also test the generalization of their approach across different applications.

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“…Mathematics [6] Train the visually impaired children to solve Mathematical tasks using their bodies.…”

Section: Cognitive Statesmentioning

confidence: 99%

“…Each of them was presented in a different unique approach. There are elements of affective in some of the existing applications which allow the low vision children to gain emotion and actively get involved in the learning activities [14], [6]. Also, some applications train the low vision children to interact with the teachers and the provided content [19].…”

Section: Multimodal Touchmentioning

confidence: 99%

“…Moreover, high skill is required in accessing the Math information that is mostly available in the form of graphics as that information has to be utilized in solving Mathematical problems. Prior research indicates that low vision children face difficulties locating information in Math graphics, particularly those that involve expressions and objects [6]. Based on the series of interviews that have been carried out with experts [1], [2], [3]; understanding the Mathematical concept is the most critical subject for the low vision children in schools.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Development of Affective 4-Dimensional Mobile Mathematics for Low Vision Alpha Generation

Aziz¹,

Ahmad²,

Rahman³

et al. 2021

TEM Journal

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In this digital era, low vision children should learn Mathematics by using similar learning content provided for mainstream students. Teachers need to express their feeling, thought, emotions, and actions to ensure the low vision learners can grasp the mathematics content. However, it is too tough for low vision children to adapt to the common learning content which is in contrast with their needs. Consequently, this study aims to develop Affective 4- Dimensional Mobile Mathematics for Low Vision Alpha Generation. This study adapted the 3-Phases Development Model. Accordingly, the findings of this study are the design and development of the abovementioned application.

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Analysis of cognitive states during bodily exploration of mathematical concepts in visually impaired children

Cited by 9 publications

References 39 publications

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

The AffectMove 2021 Challenge - Affect Recognition from Naturalistic Movement Data

Design and Development of Affective 4-Dimensional Mobile Mathematics for Low Vision Alpha Generation

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