TY - GEN
T1 - Live Demonstration
T2 - 2023 IEEE SENSORS, SENSORS 2023
AU - Angel, Montserrat Ramirez De
AU - Almansouri, Abdullah S.
AU - Salama, Khaled Nabil
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Speech-sound disorder (SSD) afflicted people can have any combination of difficulties with perception, articulation/motor production, and phonotactics, that may impact their speech intelligibility and acceptability, thus finding challenging to communicate with the public. As a result, many patients suffer from frustration, isolation, and depression. Natural-verbal communication for SSD people is now more feasible than ever thanks to advancements in wearable artificial skins and machine learning. An Assistive Magnetic Skin System (AM2S) is proposed to enable SSD afflicted people to communicate with their mouths. Using magnetic field sensors integrated into Magnetphones, the system reads the movement of the mouth by tracking the movement of magnetic skin patches attached next to the bottom lip. The measured magnetic field signals data is then processed using a Fine k-Nearest Neighbor (KNN) classifier model. The classified data can be exported verbally on speakers, or visually on a display. AM2S successfully identifies the full English alphabets with average success rate of 94.96%.
AB - Speech-sound disorder (SSD) afflicted people can have any combination of difficulties with perception, articulation/motor production, and phonotactics, that may impact their speech intelligibility and acceptability, thus finding challenging to communicate with the public. As a result, many patients suffer from frustration, isolation, and depression. Natural-verbal communication for SSD people is now more feasible than ever thanks to advancements in wearable artificial skins and machine learning. An Assistive Magnetic Skin System (AM2S) is proposed to enable SSD afflicted people to communicate with their mouths. Using magnetic field sensors integrated into Magnetphones, the system reads the movement of the mouth by tracking the movement of magnetic skin patches attached next to the bottom lip. The measured magnetic field signals data is then processed using a Fine k-Nearest Neighbor (KNN) classifier model. The classified data can be exported verbally on speakers, or visually on a display. AM2S successfully identifies the full English alphabets with average success rate of 94.96%.
KW - machine learning
KW - magnetic skin
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85179756531&partnerID=8YFLogxK
U2 - 10.1109/SENSORS56945.2023.10325234
DO - 10.1109/SENSORS56945.2023.10325234
M3 - Conference contribution
AN - SCOPUS:85179756531
T3 - Proceedings of IEEE Sensors
BT - 2023 IEEE SENSORS, SENSORS 2023 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 October 2023 through 1 November 2023
ER -