Oral motion classification of the elderly for prevention and rehabilitation of dysphagia

Sasaki, Makoto; Ito, Shumpei; Kamata, Katsuhiro; Yoshikawa, Masahiro; Shibamoto, Isamu; Nakayama, Atsushi

doi:10.1299/mej.19-00076

Cited by 5 publications

(7 citation statements)

References 10 publications

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“…In a recent attempt to acquire and classify signals measured on various muscles of the tongue, surface electrodes (part of a 22-channel active electrode mount) were attached to the underside of the jaw for capturing the tongue movement patterns. The signal acquisition approach used in this study was extended to classify tongue positions and during a dry swallow in 12 elderly subjects without dysphagia [24]. Further details of this investigation are awaited.…”

Section: A Inability To Exploit Tongue Muscles' Characteristicsmentioning

confidence: 99%

“…For example, attempts are being made to design minimally invasive tongue-computer interface mechanisms and classify tongue position using machine learning techniques. Attention is also being paid to the cost and operational difficulty related issues of the TMIS's [7,[24][25][26].…”

Section: Operational and Functional Limitationsmentioning

confidence: 99%

“…Sasaki, M. 2020 [24] Oral motion classification Surface electromyographic (EMG) signals acquired using electrodes and classified using a support vector machine.…”

Section: Quain and Khan 2014 [17]mentioning

confidence: 99%

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Design of a Portable Device: Toward Assisting in Tongue-Strengthening Exercises and Dysphagia Management

Khan,

Smart,

Bogaardt

et al. 2024

IEEE Access

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A Tongue-Machine Interaction System (TMIS) can be a valuable tool for tongue strengthening training which could contribute to rehabilitation of patients with dysphagia. Consequently, a TMIS would also help in mending the oropharyngeal pattern of swallowing. The adoption of TMIS's in clinical practice has been limited in the past since many of them required patients to have a palatal plate or some other 'component of interactivity' mounted in the mouth and/or on the tongue. This paper discusses design and functionality related problems of tongue-computer interaction (TCI) devices and demonstrates incorporation of important TCI features in a TMIS. The design and implementation of a portable, low-cost, minimally invasive and, easy to learn TMIS having four major modules is presented. One of the system modules houses an array of infrared (IR) light emitting diodes. A connected module generates tongue position-based signals and enables tongue to either operate a mouse, use a virtual keyboard or press a button to operate appliances. A Wii remote IR camera and a computer serve as the other two modules. When tested on eight male and three female healthy participants, the TMIS achieved 42° horizontal and 33° vertical range of operation with the maximum signal range of 5 meters. Users could type up to 7.2 words per minute using the TMIS. Overall, the reported TMIS can support indirect exercise for tongue strengthening and treating swallowing difficulties.

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Section: A Inability To Exploit Tongue Muscles' Characteristicsmentioning

confidence: 99%

Section: Operational and Functional Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Portable Device: Toward Assisting in Tongue-Strengthening Exercises and Dysphagia Management

Khan,

Smart,

Bogaardt

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…So, it is difficult to establish generic characteristics of tongue muscles and incorporate them in a TMIS interface design. In a recent attempt to acquire and classify signals measured on various muscles of the tongue, surface electrodes (part of a 22-channel active electrode mount) were attached to the underside of the jaw for capturing the tongue movement patterns [20][21][22]. The signal acquisition approach used in this study was extended to classify tongue position for indirect training of swallowing in elderly subjects [22].…”

Section: A Inability To Exploit Tongue Muscles' Characteristicsmentioning

confidence: 99%

“…For example, attempts are being made to design minimally invasive tongue-computer interface mechanisms and classify tongue position using machine learning techniques. Attention was also paid to the cost and operational difficulty related issues of the TSIS's [7,22,27,28].…”

Section: Operational and Functional Limitationsmentioning

confidence: 99%