Tongue in cheek: a novel concept in assistive human machine interface

Abstract

In this paper we describe a novel human machine interface system aimed primarily at those who have experienced loss of extremity motor function. The system enables the control of a wide range of assistive technologies such as wheelchairs, prosthetics, computers and general electrical goods at the ‘flick of a tongue’. This system could benefit a huge sector of people including those who have suffered a spinal cord injury, stroke or quadriplegia.The technology focuses on a unique hands‐free interface whereby users can issue commands simply by performing subtle tongue movements; these tongue motions are continually monitored by a small microphone positioned comfortably within the ear canal. Due to the physiological connections between these regions and the distinctive nature of the signals, these commands can be detected and distinguished allowing a control signal to be issued.This inexpensive device offers significant advantages over existing technologies by providing unobtrusive, hygienic control through natural tongue motion. New software has been implemented, achieving over 97% correct classification across four different tongue movements for seven test subjects. Feasibility of the system as an interface for a variety of devices is demonstrated through simulation studies including controlling a prosthetic manipulator and power wheelchair.

Feature and Classification Analysis for Detection and Classification of Tongue Movements From Single-Trial Pre-Movement EEG

“…6. Grand average topological plots of the Alpha [8][9][10][11][12][13] Hz and Beta [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz bandpower at movement onset, for the four different movement types averaged across all subjects and all trials. The power was calculated as meansquare of the bandpass filtered signal, referenced to common average reference, from 0.5 seconds before until movement onset.…”

“…This is a challenge to provide individuals with severe disabilities. Individuals with tetraplegia who still have motor functionality above the neck, such as individuals with SCI or early spinal-onset ALS, can use some control options such as lip-sip-suck control [7], [8], eye-tracking [9]- [11], or electrooculography (EOG) or facial electromyography (EMG) [12], or tongue control [13]- [20]. However, if the cranial nerves are also affected the individual may be in a locked-in state (LIS) and can then only use a brain-computer interface (BCI) as a control option.…”

“…Furthermore, the stimuli might cause fatigue and/or require eye-gaze which some LIS patients do not have. As an alternative to stimuli-based BCI, Nam et al utilized the glossokinetic potentials (GKP), which are elicited when the tip of the tongue makes contact with tissue inside the mouth [13], [18]- [20], to successfully control a robotic wheelchair through EEG measurements [18]. While it showed a new control option, it requires physical tongue movements similar to other proposed tongue control interfaces that achieve Aalborg University, 9220 Aalborg, Denmark (e-mail: rlk@hst.aau.dk; naja@hst.aau.dk).…”

Controlling a Drone by the Tongue – A Pilot Study on Drone Based Facilitation of Social Activities and Sports for People with Complete Tetraplegia

“…Ghovanloo et al [5] used magnetic sensors to monitor the tongue movement and convert it to control commands for electronic devices. Further, the "Think-a-Move" system analyzes the acoustic patterns generated by flicking the tongue to the gum and identifies the intended commands [6]. Detecting the tongue movement by optic sensors [7] and by processing EEG signals [8] are other approaches for designing a tongue-operated assistive device.…”

Innovaciones en sistemas e interfaces humano-máquina: aplicación a las tecnologías de rehabilitación

“…Se han diseñado diferentes tipos de interfaz siguiendo estas técnicas. De esta forma, se encuentran interfaces que se encargan de detectar la posición de la cabeza, dónde esta funciona como un joystick [214] y [215], detección de guiños [216] y [217], movimientos de la lengua [218], [219], [220] y [221], expiraciones e inspiraciones (en inglés, sniffing) [222] y mediante cambios en la posición corporal del usuario [223].…”