Optimized and Predictive Phonemic Interfaces for Augmentative and Alternative Communication

Ability-Based Methods for Personalized Keyboard Generation

This study introduces an ability-based method for personalized keyboard generation, wherein an individual's own movement and human-computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user's movement abilities through capturing directional constraints and preferences. We evaluated our approach in a study involving 16 participants using inertial sensing and facial electromyography as an access method, resulting in significantly increased communication rates using the personalized keyboard (52.0 bits/min) when compared to a generically optimized keyboard (47.9 bits/min). Our results demonstrate the ability to effectively characterize an individual's movement abilities to design a personalized keyboard for improved communication. This work underscores the importance of integrating a user's motor abilities when designing virtual interfaces.

Ability-based Keyboards for Augmentative and Alternative Communication: Understanding How Individuals’ Movement Patterns Translate to More Efficient Keyboards

This study presents the evaluation of ability-based methods extended to keyboard generation for alternative communication in people with dexterity impairments due to motor disabilities. Our approach characterizes user-specific cursor control abilities from a multidirectional point-select task to configure letters on a virtual keyboard based on estimated time, distance, and direction of movement. These methods were evaluated in three individuals with motor disabilities against a generically optimized keyboard and the ubiquitous QWERTY keyboard. We highlight key observations relating to the heterogeneity of the manifestation of motor disabilities, perceived importance of communication technology, and quantitative improvements in communication performance when characterizing an individual’s movement abilities to design personalized AAC interfaces.

The Effects of Channel Number on Classification Performance for sEMG-based Speech Recognition

Speech recognition based on surface electromyography (sEMG) signals is an important research direction with potential applications in life, work and clinical. The number and placement of sEMG electrodes play a critical role in capturing the underlying sEMG activities and in turn, accurately classifying the speaking tasks. The aim of this work was to investigate the effect of the number of channels in speech recognition based on high-density (HD) sEMG. 8 healthy subjects were recruited to perform 11 English speech tasks with sEMG signals were detected from 120 electrodes covering almost the whole neck and face. The classification accuracy was evaluated in the context of a linear discriminant analysis (LDA) with different sets of EMG electrodes. By comparing the classification accuracy, the sequential forward search (SFS) algorithm was adopted to figure out the optimal combination of electrodes which realized the highest classification level. The results showed that smaller number of channels obtained by the SFS method could achieve the classification accuracy of 80%, and another few electrodes were needed to record detail information to achieve the classification accuracy of 85%, 90% and 95%. The numbers were rather smaller than 120. Considering the computation time and reliable accuracy, it is concluded that the SFS method might be helpful for standardizing the number and position of electrodes in speech recognition.