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32 results on '"Kerschbaumer, Andrea"'

1. Automated dysgraphia detection by deep learning with SensoGrip

Author

Bublin, Mugdim, Werner, Franz, Kerschbaumer, Andrea, Korak, Gernot, Geyer, Sebastian, Rettinger, Lena, Schoenthaler, Erna, and Schmid-Kietreiber, Matthias

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction
Abstract

Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

Published
2022

2. Developmental Evaluation of an e-Counselling and Learning Application for Parents of Children with Attention Deficit Hyperactivity Disorder

Author

Kerschbaumer, Andrea, Gstöttner, Lisa-Sophie, Schönthaler, Erna, Szabó, Károly, Putz, Peter, Hauser, Carina, Werner, Franz, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Salvi, Dario, editor, Van Gorp, Pieter, editor, and Shah, Syed Ahmar, editor

Published
2024
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5. Participatory design and needs assessment for a pressure-sensitive pen and mobile application (SensoGrip) for children with handwriting problems.

Author

Rettinger, Lena, Klupper, Carissa, Hauser, Carina, Schönthaler, Erna, Kerschbaumer, Andrea, Werner, Katharina, and Werner, Franz

Subjects
FINGER physiology, HANDWRITING, PRESSURE, FOCUS groups, RESEARCH funding, PILOT projects, OCCUPATIONAL therapists, DESCRIPTIVE statistics, OCCUPATIONAL therapy, ASSISTIVE technology, ADULT education workshops, NEEDS assessment, SPECIAL education, GRIP strength, PSYCHOSOCIAL factors, CHILDREN
Abstract

Problems with handwriting are a common referral to occupational therapy in children and are often associated with inadequate pen grip and tip pressure. Technologies providing immediate feedback on applied forces are currently not available. Therefore, the aim was to identify user's perspectives on the optimal functionality of a pressure-sensitive pen with an integrated feedback system. To ensure that the product meets the requirements of the users, user needs of paediatric occupational therapists, elementary or special education teachers and children were collected in two focus groups and a children's workshops before starting the technical development process. Focus group transcripts were analyzed and rephrased as user stories to identify user-centred requirements. In a second step, user stories were merged and ranked regarding importance and feasibility. 120 user story requirements were generated from the focus groups and children's workshops, indicating requirements regarding design and functionality for the pen and mobile application. Visual feedback, appropriate size and weight, customizability, ease of settings and usability of the pen were pivotal. This study comprehensively researched the requirements for a device that may facilitate children in adjusting their handwriting pressure and can guide further research and evaluation. Description of a participatory design process for the development of a pressure-sensitive pen. Understand the needs of different stakeholders for an assistive device for children with handwriting difficulties. Inform the further development of a pressure-sensitive pen with immediate pressure-feedback. [ABSTRACT FROM AUTHOR]

Published
2024
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7. dHealth 2023. Proceedings of the 17th Health Informatics Meets Digital Health Conference Edited by / Pfeifer, Bernhard ; Schreier, Günter ; Baumgartner, Martin ; Hayn, Dieter / Evidence-Based and User-Centred Development of an Ecounseling & Learning-System for Parents of Children with ADHD

Author

Gstöttner, Lisa-Sophie, Kerschbaumer, Andrea, Hauser, Carina, Schönthaler, Erna, Szabó, Károly, Werner, Franz, and Putz, Peter

Abstract

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders in childhood and adolescence. It is characterized by attention deficit, hyperactivity, and impulsivity as the main symptoms. These can lead to increased stress in everyday life for the entire family. The e-counseling and e-learning application is being developed within the ELSA project to support parents of ADHD-diagnosed children in everyday life. The requirements identified included, for example, advice on children’s restlessness or measures against sibling rivalry.

Published
2023
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8. Sensors / Handwriting Evaluation Using Deep Learning with SensoGrip

Author

Bublin, Mugdim, Werner, Franz, Kerschbaumer, Andrea, Korak, Gernot, Geyer, Sebastian, Rettinger, Lena, Schönthaler, Erna, and Schmid-Kietreiber, Matthias

Subjects
machine learning, smart sensors, deep learning
Abstract

Handwriting learning disabilities, such as dysgraphia, have a serious negative impact on children’s academic results, daily life and overall well-being. Early detection of dysgraphia facilitates an early start of targeted intervention. Several studies have investigated dysgraphia detection using machine learning algorithms with a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated the fine grading of handwriting capabilities by predicting the SEMS score (between 0 and 12) with deep learning. Our approach provided a root-mean-square error of less than 1 with automatic instead of manual feature extraction and selection. Furthermore, the SensoGrip smart pen SensoGrip was used, i.e., a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

Published
2023

13. Urinary tract endometriosis

Author

Rehder, Peter, Glodny, Bernhard, Pichler, Renate, Kerschbaumer, Andrea, and Mitterberger, Michael

Published
2009

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