# A Context-Aware Conversational Agent in the Rehabilitation Domain
## 2 Related Work  
Virtual agents roots are traced to legacy systems which used speech recognition to offer ground-breaking technologies for their time. VAL (Voice Activated Link) and Sphinx-II are such examples of systems that were ahead of their era and were later replaced by more advanced and voice-enabled with dialogue capabilities solutions [4]. Conversational agent's audience has been expanded ever since, and they have been proposed as a means to promote mental well-being [5] or even as an automated social skills trainer for people with autism spectrum disorders [6]. Modern agents encountered in everyday consumer products (Google's Assistant, Amazon's Alexa, Apple's Siri, and Microsoft's Cortana) are mostly an expected feature rather than an exception, and the practice is rapidly expanding to the health-care sector [7], where they are meant to be utilised as portals for medical services. Amazon has enjoyed a head start in providing virtual assistant services, with Alexa being a top-charting device sales-wise, already installed in millions of houses. In addition, outside of home settings, there have been numerous clinical environments embracing the technology recently, conducting pilot testing, and assessing its usefulness [8]. However, Google, Apple, and Microsoft plan to also integrate health-care-oriented services into their respective devices, with Google Home being on the brink of hospital trials as well. Moreover, specialised, medically focused products are being developed, assisting in (i) health-care organisation assistance (Merit ([[https://merit.ai/](https://merit.ai/)]([https://merit.ai/](https://merit.ai/)))) by providing automated appointment re/scheduling and sending outbound reminders; (ii) doctor support (Suki ([[https://www.suki.ai](https://www.suki.ai)]([https://www.suki.ai](https://www.suki.ai)))), Robin ([[https://www.robinhealthcare.com/](https://www.robinhealthcare.com/)]([https://www.robinhealthcare.com/](https://www.robinhealthcare.com/))) via the implementation of voice commands and generation of accurate notes; and (iii) patient and caregiver support alike (Aiva [[https://aivahealth.com](https://aivahealth.com)]([https://aivahealth.com](https://aivahealth.com)), based on the amazon echo platform) by offering a direct channel of communication between the two groups in the form of a multipurpose mobile app. In Reference [9], the authors examine under which conditions virtual assistants can provide support to elderly people and people with cognitive disabilities so they can live autonomously. The conversational importance of virtual assistants has been reported in Reference [10], where emphasis is placed on the role of chatbots beyond patient monitoring; it focuses on patient-doctor interaction over a chatbot-driven telemedicine platform after the former has been discharged from a clinical environment. Finally, a comprehensive review on existing conversational agents for health-related purposes can be found in Reference [11]; the characteristics, current applications, and evaluation measures are laid bare regarding agents with unconstrained natural language-input capabilities. Nevertheless, certain concerns have been expressed over potential safety risks when patients are using virtual assistants as the sole source for medical information [12]. Most dialogue-driven platforms share architectural common grounds, embracing a standardised approach comprised of components like a speech-to-text and text analysis layer, a dialogue manager one, a reasoning mechanism, and a language generation (LG) layer. Speech-to-text manages the transcription of any vocal user input addressed to the system. Robust speech recognition software ascertain that high-quality textual input reaches the text analysis process, tackled by powerful natural language processing (NLP) algorithms.