Surface electromyographic control of a novel phonemic interface for speech synthesis

Abstract

Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of 8 training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.

Optimized and Predictive Phonemic Interfaces for Augmentative and Alternative Communication

“…Interfaces with symbols representing whole phrases, for example, provide very high speed to produce the given phrase, minimal flexibility (i.e., only certain phrases can be selected quickly or at all), and high cognitive load (Thistle & Wilkinson, 2013). Some interfaces use phonemes (which represent a particular sound in a spoken language) as targets (Black, Waller, Pullin, & Abel, 2008;Cler, Nieto-Castañón, Guenther, Fager, & Stepp, 2016;Cler, Nieto-Castañón, Guenther, & Stepp, 2014;Schroeder, 2005;, which may provide a good balance of speed, flexibility, and cognitive load.…”

Prediction of Optimal Facial Electromyographic Sensor Configurations for Human–Machine Interface Control

“…Finally, the identification of quantitative features was assessed in a healthy population, whereas the methods we developed aim to advance the use of sEMG as a computer access method for individuals with neurological impairments. Previous work by Cler, et al [1] included a case study of an individual with Guillain-Barré Syndrome who used the same sEMG-controlled cursor system and calibration task; the results of this study showed promise in the ability of handicapped persons to perform the calibration task. However, given the diverse manifestation of neurological disorders, it is difficult to generalize the ability of all handicapped persons to perform the calibration.…”

“…Facial electromyography has been demonstrated as a robust input modality for assistive technology devices [1–10]. Using surface electromyography (sEMG), the electrical activity of face and neck musculature can be detected by electrodes placed on the surface of the skin.…”

“…Using surface electromyography (sEMG), the electrical activity of face and neck musculature can be detected by electrodes placed on the surface of the skin. Individuals can then volitionally activate certain muscles to control a human-machine interface (HMI), such as a computer pointing device [1, 2, 4–7, 11] or even a power wheelchair [9–11]. Unlike the use of eye-tracking systems for computer access, sEMG is insensitive to lighting conditions and is suitable for all types of skin with proper skin preparation [12–14].…”

“…Most notably, direct control via facial sEMG has been shown to enable 360° cursor movements with four direction-based sEMG sources [2], while pattern recognition techniques would only enable cursor movement in the four cardinal directions with the same number of sensors. Yet, previous studies using sEMG cursor control (e.g., [1–3, 5, 7, 8]) have not evaluated small adjustments in sensor configuration, but instead relied on trained operators and trial-and-error to arrive at an “optimal” sensor configuration. Therefore, we investigated quantitative sEMG signal features extracted from a short calibration process in order to determine if user control performance within an HMI could be quickly and accurately predicted.…”