Abstract
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%.
Original language | English (US) |
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Title of host publication | 2023 IEEE SENSORS, SENSORS 2023 - Conference Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350303872 |
DOIs | |
State | Published - 2023 |
Event | 2023 IEEE SENSORS, SENSORS 2023 - Vienna, Austria Duration: Oct 29 2023 → Nov 1 2023 |
Publication series
Name | Proceedings of IEEE Sensors |
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ISSN (Print) | 1930-0395 |
ISSN (Electronic) | 2168-9229 |
Conference
Conference | 2023 IEEE SENSORS, SENSORS 2023 |
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Country/Territory | Austria |
City | Vienna |
Period | 10/29/23 → 11/1/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- machine learning
- magnetic skin
- wearable sensors
ASJC Scopus subject areas
- Electrical and Electronic Engineering