Traditional topic-specific communication boards for Augmentative and Alternative Communication (AAC) require manual programming of relevant symbolic vocabulary, which is time-consuming and often impractical even for experienced Speech-Language Pathologists (SLPs). While recent research has demonstrated the potential to automatically generate these boards from photographs using artificial intelligence, there has been no exploration on how to design such tools to support the specific needs of AAC-based language instruction. This paper introduces QuickPic, a mobile AAC application co-designed with SLPs and special educators, aimed at enhancing language learning for non-speaking individuals, such as autistic children. Through a 17-month design process, we uncover the unique design features required to provide timely language support in therapy and special education contexts. We present emerging evidence on the overall satisfaction of SLPs using QuickPic, and on the advantages of large language model-based generation compared to the existing technique for automated vocabulary from photographs for AAC.

In this article, we present three studies involving WhatsHap, a mobile system designed to deliver speech as vibrations on the forearm with minimal hardware demands and practice time. After only 4.2 h of training on a 24-haptic phoneme vocabulary and on how to combine these to form words, participants were able to generalize their phoneme identification skills to the understanding of untrained English words, correctly identifying 65% of words in phrases rendered with a user-controlled interval between words, and up to 59% with a fixed interval. Ultimately, participants were able to complete 88% of simple communicative tasks that elicited spontaneous speech and semi-structured bidirectional conversation using the apparatus. We conclude by providing insights as to how such a system may ultimately be used for communication under more natural conditions.

As artificial intelligence (AI) makes significant headway in various arenas, the field of speech–language pathology is at the precipice of experiencing a transformative shift towards automation. This study introduces QuickPic AAC, an AI-driven application designed to generate topic-specific displays from photographs in a “just-in-time” manner. Using QuickPic AAC, this study aimed to (a) determine which of two AI algorithms (NLG-AAC and GPT-3.5) results in greater specificity of vocabulary (i.e., percentage of vocabulary kept/deleted by clinician relative to vocabulary generated by QuickPic AAC; percentage of vocabulary modified); and to (b) evaluate perceived usability of QuickPic AAC among practicing speech–language pathologists. Results revealed that the GPT-3.5 algorithm consistently resulted in greater specificity of vocabulary and that speech–language pathologists expressed high user satisfaction for the QuickPic AAC application. These results support continued study of the implementation of QuickPic AAC in clinical practice and demonstrate the possibility of utilizing topic-specific displays as just-in-time supports.

The tactile sense can be used as a channel for general communication, especially in contexts where the visual and auditory modalities are occupied with other tasks or compromised. We propose a new method for communicating generic words through the sense of touch that relies on delivering vibration patterns, representing the phonemes composing the words, to the user’s skin through two vibrotactile transducers worn on the forearm. The novelty of this technique is that vibration patterns are created from the audio of the corresponding English phoneme, resulting in vibration patterns that resemble physical characteristics when uttering the phoneme during normal speech. After 100 minutes of training, participants were able to recognize 50 words rendered haptically with an average accuracy of 94.4%. Results support the possibility of using the proposed apparatus in real-world applications.

Communication with an Alternative and Augmentative Communication (AAC) device tends to be extremely slow, mainly due to difficulties in navigating the extensive vocabulary needed for the generation of spontaneous utterances in a manner that allows the user to easily find and select appropriate words when communicating. This work aims to design, implement, and evaluate a context-adaptive AAC application that suggests the most appropriate words according to the user location and past vocabulary usage.

Given that the population is aging, it is crucial to develop technologies which will not only help the elderly to age in place, but also live in place with independent and healthy lifestyle. Ambient Assisted Living (AAL) environments can help the elderly and people with functional diversity by anticipating their needs in specific situations and acting proactively in order to properly assist them in performing their activities of daily living (ADLs). Since the users needs tend to be very diverse in regard to functioning and disability levels, it is crucial to have personalized services capable of providing tailored assistance to a user based on their unique preferences, requirements, and desires. This paper introduces the ontology named AATUM (Ambient Assistive Technology User Model), to be adopted by systems whose goal is to enhance user quality of life within ALL environments through service personalization. Its main feature is the use of The International Classification of Functioning, Disability and Health (ICF) to model the user’s functioning and disability levels in a consistent and internationally comparable way. The use of the proposed ontology is illustrated through its application in two different case studies.