Surface electromyographic control of a novel phonemic interface for speech synthesis

Optimized and Predictive Phonemic Interfaces for Augmentative and Alternative Communication

Purpose We empirically assessed the results of computational optimization and prediction in communication interfaces that were designed to allow individuals with severe motor speech disorders to select phonemes and generate speech output. Method Interface layouts were either random or optimized, in which phoneme targets that were likely to be selected together were located in proximity. Target sizes were either static or predictive, such that likely targets were dynamically enlarged following each selection. Communication interfaces were evaluated by 36 users without motor impairments using an alternate access method. Each user was assigned to 1 of 4 interfaces varying in layout and whether prediction was implemented (random/static, random/predictive, optimized/static, optimized/predictive) and participated in 12 sessions over a 3-week period. Six participants with severe motor impairments used both the optimized/static and optimized/predictive interfaces in 1-2 sessions. Results In individuals without motor impairments, prediction provided significantly faster communication rates during training (Sessions 1-9), as users were learning the interface target locations and the novel access method. After training, optimization acted to significantly increase communication rates. The optimization likely became relevant only after training when participants knew the target locations and moved directly to the targets. Participants with motor impairments could use the interfaces with alternate access methods and generally rated the interface with prediction as preferred. Conclusions Optimization and prediction led to increases in communication rates in users without motor impairments. Predictive interfaces were preferred by users with motor impairments. Future research is needed to translate these results into clinical practice. Supplemental Material https://doi.org/10.23641/asha.8636948.

Biological surface electromyographic switch and necklace-type button switch control as an augmentative and alternative communication input device: a feasibility study

Augmentative and alternative communication (AAC) is an approach used to supplement, improve, and support the communication of those with speech or language impairments. We developed an AAC device for diverse approaches, using an electromyographic (EMG) switch and a necklace-type button switch. The EMG switch comprised an EMG signal processor and a switch interface processor. EMG signals were processed using an electrode through the stages of signal acquisition, amplification, filtering, rectification, and smoothing. In the switch interface processor, the microprocessor determined the switch as ON or OFF in response to an input EMG signal and then converted the EMG signal into a keyboard signal, which was transmitted to a smart device via Bluetooth communication. A similar transmission process was used for the necklace-type button switch, and switch signals were input and processed with general-purpose input/output. The first and second feasibility tests for the EMG switch and button switch were conducted in a total of three test sessions. The result of the feasibility test indicated that the major inconvenience and desired improvement associated with the EMG switch were the intricacy of the AAC device settings. The major inconveniences and desired improvements for the necklace-type button switch involved device shifting, volume and weight, and inconvenience in fixing the switch in various directions. Thus, based on the first and second feasibility tests, we developed an additional device. Finally, the EMG switch and necklace-type button switch developed to remedy the inconveniencies had high feasibility.

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

Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce the sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task. The task was repeated for a range of sensor configurations to elicit a range of signal qualities. Signal features were extracted from the calibration of each configuration and examined via a principle component factor analysis in order to predict the HMI performance during subsequent tasks. Feature components most influenced by the energy and the complexity of the EMG signal and muscle activity between the sensors were significantly predictive of the HMI performance. However, configuration order had a greater effect on performance than the configurations, suggesting that non-experts can place sEMG sensors in the vicinity of usable muscle sites for computer access and healthy individuals will learn to efficiently control the HMI system.