Lexical Access in Individuals with Cerebral Palsy and Severe Speech and Physical Impairment

This study examined lexical access in adolescents and adults with cerebral palsy and severe speech and physical impairment (CP/SSPI) who have limited language production due to severe dysarthria or anarthria. To date, the impact of a severe speech production deficit on lexical activation and the organization of the mental lexicon has not been investigated. Such an investigation may support or refute these views of an articulatory basis of speech perception and lexical development and access. The hypothesis of this study is that spoken word recognition will be severely reduced or absent in this population although individual differences may result in variations. Method. Using the visual world eye tracking paradigm, in 16 adolescents and adults with CP/SSPI, the study examined resolution of phonological or semantic competition among referents for a spoken word as it unfolds over time. Eye gaze patterns were compared to published data as well as to a group of eight age-matched adults with no neurological impairment and to children with typical development (TD) from a previous study using the same stimuli and experimental design. Results. Participants with CP/SSPI revealed significant fixations to targets and to phonological onset competitors but not to semantic relatives beyond those directed to unrelated pictures while children with TD demonstrated significant phonological and semantic competition effects. The participants with CP/SSPI and higher PPVT-4 scores exhibited eye gaze patterns more similar to adults with no neurological impairment while those with lower scores were less efficient at resolving competition. Conclusion. Despite the presence of a severe speech impairment, individuals with CP/SSPI demonstrated varying levels of lexical activation, suggesting that theories relying on an articulatory bases of speech perception do not offer a complete explanation of lexical development and access in this population

SymbolDesign: A User-centered Method to Design Pen-based Interfaces and Extend the Functionality of Pointer Input Devices

A method called "SymbolDesign" is proposed that can be used to design user-centered interfaces for pen-based input devices. It can also extend the functionality of pointer input devices such as the traditional computer mouse or the Camera Mouse, a camera-based computer interface. Users can create their own interfaces by choosing single-stroke movement patterns that are convenient to draw with the selected input device and by mapping them to a desired set of commands. A pattern could be the trace of a moving finger detected with the Camera Mouse or a symbol drawn with an optical pen. The core of the SymbolDesign system is a dynamically created classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to the complexity of the classification task. In experiments, subjects used the SymbolDesign method to design and test the interfaces they created, for example, to browse the web. The experiments demonstrated good recognition accuracy and responsiveness of the user interfaces. The method provided an easily-designed and easily-used computer input mechanism for people without physical limitations, and, with some modifications, has the potential to become a computer access tool for people with severe paralysis.National Science Foundation (IIS-0093367, IIS-0308213, IIS-0329009, EIA-0202067

A Framework for Gamification of Human Joint Remote Rehabilitation, Incorporating Non-Invasive Sensors

Patients who have suffered soft tissue injuries or undergone surgery often experience reduced muscle strength, flexibility, and pain in the affected area, which can interfere with daily activities. Rehabilitation exercises are crucial in reducing symptoms and returning patients to normal activities. This research presents a framework for human joint rehabilitation that enables clinicians to set engaging gamified rehabilitation tasks for their patients utilising non-invasive sensors and machine learning algorithms