Live Demonstration: AI-Assisted Magnetic Skin tracker for Speech Recognition

Montserrat Ramirez De Angel, Abdullah S. Almansouri, Khaled Nabil Salama

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish (US)
Title of host publication2023 IEEE SENSORS, SENSORS 2023 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350303872
StatePublished - 2023
Event2023 IEEE SENSORS, SENSORS 2023 - Vienna, Austria
Duration: Oct 29 2023Nov 1 2023

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference2023 IEEE SENSORS, SENSORS 2023


  • machine learning
  • magnetic skin
  • wearable sensors

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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