TY - GEN
T1 - Towards Adaptive Multi-modal Augmentative and Alternative Communication for Children with CP
AU - Zisman, Andrea
AU - Katz, Dmitri
AU - Bennasar, Mohamed
AU - Alrimawi, Faeq
AU - Price, Blaine
AU - Johnston, Anthony
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Effective communication can pose significant challenges for non-verbal children with Cerebral Palsy (CP). Augmentative and Alternative Communication (AAC) systems help many but can fail to meet the needs of some users. This research proposes a hybrid adaptive approach, utilizing sensors and machine learning (ML) algorithms to create a personalized mobile communication system for those whose abilities are ill-suited to existing approaches. The system aims to tailor to individual abilities, reducing the need for users to adapt to system requirements. Online surveys gathered data on gestures, actions, and sounds used by non-verbal CP children, informing a classification system and functional requirements. The participants reported 28 communication messages with diverse means of expression. Representative examples and their classification highlight the intricacies of non-verbal communication. The proposed architecture emphasizes real-time classification, multiple sensors, and a feedback loop for continuous improvement, enhancing communication for non-verbal children with CP.
AB - Effective communication can pose significant challenges for non-verbal children with Cerebral Palsy (CP). Augmentative and Alternative Communication (AAC) systems help many but can fail to meet the needs of some users. This research proposes a hybrid adaptive approach, utilizing sensors and machine learning (ML) algorithms to create a personalized mobile communication system for those whose abilities are ill-suited to existing approaches. The system aims to tailor to individual abilities, reducing the need for users to adapt to system requirements. Online surveys gathered data on gestures, actions, and sounds used by non-verbal CP children, informing a classification system and functional requirements. The participants reported 28 communication messages with diverse means of expression. Representative examples and their classification highlight the intricacies of non-verbal communication. The proposed architecture emphasizes real-time classification, multiple sensors, and a feedback loop for continuous improvement, enhancing communication for non-verbal children with CP.
KW - Adaptive Technology
KW - Augmentative Alternative Communication (AAC)
KW - Cerebral Palsy
KW - Machine Learning
KW - Multi-modal
KW - User-Centered Design
UR - http://www.scopus.com/inward/record.url?scp=85200342923&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-62849-8_20
DO - 10.1007/978-3-031-62849-8_20
M3 - Conference contribution
AN - SCOPUS:85200342923
SN - 9783031628481
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 167
BT - Computers Helping People with Special Needs - 19th International Conference, ICCHP 2024, Proceedings
A2 - Miesenberger, Klaus
A2 - Peňáz, Petr
A2 - Kobayashi, Makato
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Computers Helping People with Special Needs, ICCHP 2024
Y2 - 8 July 2024 through 12 July 2024
ER -