An Arch-Shaped Intraoral Tongue Drive System with Built-in Tongue-Computer Interfacing SoC

Park, Hangue; Ghovanloo, Maysam

doi:10.3390/s141121565

Cited by 27 publications

(9 citation statements)

References 36 publications

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“…Others have demonstrated the use of extra oral interfaces to control limited movements of robotic arms in task specific set-ups [9, 10]. In the recent years, new intra oral tongue based computer interfaces have been proposed for control of computers [11–16]. The high flexibility of the tongue allows for the precise selection of intraoral sensors to generate highly reliable control signals.…”

Section: Introductionmentioning

confidence: 99%

Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Struijk

Egsgaard

Lontis

et al. 2017

J NeuroEngineering Rehabil

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BackgroundFor an individual with tetraplegia assistive robotic arms provide a potentially invaluable opportunity for rehabilitation. However, there is a lack of available control methods to allow these individuals to fully control the assistive arms.MethodsHere we show that it is possible for an individual with tetraplegia to use the tongue to fully control all 14 movements of an assistive robotic arm in a three dimensional space using a wireless intraoral control system, thus allowing for numerous activities of daily living. We developed a tongue-based robotic control method incorporating a multi-sensor inductive tongue interface. One abled-bodied individual and one individual with tetraplegia performed a proof of concept study by controlling the robot with their tongue using direct actuator control and endpoint control, respectively.ResultsAfter 30 min of training, the able-bodied experimental participant tongue controlled the assistive robot to pick up a roll of tape in 80% of the attempts. Further, the individual with tetraplegia succeeded in fully tongue controlling the assistive robot to reach for and touch a roll of tape in 100% of the attempts and to pick up the roll in 50% of the attempts. Furthermore, she controlled the robot to grasp a bottle of water and pour its contents into a cup; her first functional action in 19 years.ConclusionTo our knowledge, this is the first time that an individual with tetraplegia has been able to fully control an assistive robotic arm using a wireless intraoral tongue interface. The tongue interface used to control the robot is currently available for control of computers and of powered wheelchairs, and the robot employed in this study is also commercially available. Therefore, the presented results may translate into available solutions within reasonable time.

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Section: Introductionmentioning

confidence: 99%

Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Struijk

Egsgaard

Lontis

et al. 2017

J NeuroEngineering Rehabil

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“…One paper used computer vision and an endoscope camera attached to a mouthguard to identify tongue movements [ 66 ]. One study applied four 3-axis magnetic sensors to track the movements of a magnetic tracer glued on the tongue [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

“…For the inductive system, the overall throughput in typing with the keyboard keys was 1.73 bits/s [ 98 ], for typing with the mousepad it was 0.60 bits/s [ 99 ]. The magnetic system had an overall throughput of 1.25 bits/s in virtual maze navigation and tapping tasks [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

Performance of Oral Cavity Sensors: A Systematic Review

Bueno

Kwong

Bergmann

2023

Sensors

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Technological advancements are enabling new applications within biomedical engineering. As a connection point between the outer environment and the human system, the oral cavity offers unique opportunities for sensing technologies. This paper systematically reviews the performance of measurement systems tested in the human oral cavity. Performance was defined by metrics related to accuracy and agreement estimation. A comprehensive search identifying human studies that reported on the accuracy or agreement of intraoral sensors found 85 research papers. Most of the literature (62%) was in dentistry, followed by neurology (21%), and physical medicine and rehabilitation (12%). The remaining papers were on internal medicine, obstetrics, and aerospace medicine. Most of the studies applied force or pressure sensors (32%), while optical and image sensors were applied most widely across fields. The main challenges for future adoption include the lack of large human trials, the maturity of emerging technologies (e.g., biochemical sensors), and the absence of standardization of evaluation in specific fields. New research should aim to employ robust performance metrics to evaluate their systems and incorporate real-world evidence as part of the evaluation process. Oral cavity sensors offer the potential for applications in healthcare and wellbeing, but for many technologies, more research is needed.

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“…In the future, these functions will be implemented in the form of SoC (System on Chip) which can be small-sized and placed inside the mouth. Figure 6 illustrates a device with the function of communicative expression which uses the tongue to press the keypad attached to the palatal space [ 33 ].…”

Section: Biosignal-based Speech Recognitionmentioning

confidence: 99%

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

Lee

Seong

Ozlu

et al. 2021

Sensors

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Voice is one of the essential mechanisms for communicating and expressing one’s intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase. Novel approaches should have been developed for speech recognition and production because that would seriously undermine the quality of life and sometimes leads to isolation from society. In this review, we survey mouth interface technologies which are mouth-mounted devices for speech recognition, production, and volitional control, and the corresponding research to develop artificial mouth technologies based on various sensors, including electromyography (EMG), electroencephalography (EEG), electropalatography (EPG), electromagnetic articulography (EMA), permanent magnet articulography (PMA), gyros, images and 3-axial magnetic sensors, especially with deep learning techniques. We especially research various deep learning technologies related to voice recognition, including visual speech recognition, silent speech interface, and analyze its flow, and systematize them into a taxonomy. Finally, we discuss methods to solve the communication problems of people with disabilities in speaking and future research with respect to deep learning components.

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An Arch-Shaped Intraoral Tongue Drive System with Built-in Tongue-Computer Interfacing SoC

Cited by 27 publications

References 36 publications

Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Performance of Oral Cavity Sensors: A Systematic Review

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

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