A Context-Aware Conversational Agent in the Rehabilitation Domain

Abstract

Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.

A Chatbot System to Support Mine Safety Procedures during Natural Disasters

“…Vaidyam et al [18] noted that the mental health field could adopt chatbots in psychiatric treatment with proper approaches. Mavropoulos et al [19] presented a context-aware system framework by combining monitoring and chatbots to benefit ailing patients and assist clinical experts to retrieve information about patients. Griol et al [20] presented a multimodal conversational coach for physical activity training with sensors to provide meaningful coaching and feedback during sessions.…”

Voice assistants in private households: a conceptual framework for future research in an interdisciplinary field

“…Cluster 5 contains predominantly CS research papers investigating and proposing solutions for technical challenges in VA application development. Recent work focuses on extensions and improvements for the technologically relatively mature mass-market VAs (e.g., Liciotti et al, 2014 ; Azmandian et al, 2019 ; Jabbar et al, 2019 ; Mavropoulos et al, 2019 ). Some research investigates ways to overcome the technical challenges of VAs in household environments: For example, King et al ( 2017 ) work on more robust speech recognition, and Ito ( 2019 ) proposes an audio watermarking technique to avoid the misdetection of utterances from other VAs in the same room.…”

Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions

“…Context-aware conversational agent to support patients with movement disability [30]; Transcription of patient charts at the emergency department [31] Community based screening and risk stratification Screening of cataracts [32] and glaucoma [33] highly image-driven specialty, there still exist large amounts of natural language in day-to-day work, for which NLP is a potential use case to help improve the quality and delivery of ophthalmic care. With population greying and lifestyle conditions such as cardiovascular disease dominating the demographic trend worldwide, ophthalmic conditions such as glaucoma, cataracts, and diabetic retinopathy have likewise increased in prevalence.…”

“…In the recent decade, assistive technologies have risen in popularity to support these rehabilitative goals, as well as to provide continuity of care in between clinician visits. For example, a context-aware conversational agent has been developed, using techniques from speech recognition and computer vision, to support patients with movement disabilities, as well as communicate with ailing patients in the hospital and at home [30]. Other applications of speech recognition in healthcare include transcription for radiological reports, and recording of patient charts at the emergency department [31].…”