Appraisal of a Conversational Artefact and Its Utility in Remote Patient Monitoring

Black, Lesley-Ann; McTear, Michael F.; Black, Norman D.; Harper, Roy; Lemon, M.

doi:10.1109/cbms.2005.33

Cited by 21 publications

(41 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DI@L-log is a spoken conversational agent that acts as a voice logbook to collect home-monitored data from patients suffering from Type-2 diabetes (Black et al, 2005). The data collected by the system are the patient's weight, blood pressure (systolic and diastolic values), and sugar levels.…”

Section: Case Application: the Di@l-log Conversational Agentmentioning

confidence: 99%

“…We have applied our technique to explore dialog strategies for the DI@L-log conversational agent (Black et al, 2005), designed to collect monitored data from patients suffering from diabetes. In addition, a set of specific measures has been defined to evaluate the main characteristics of the acquired data and the new dialog strategy that can be learned from them.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Automatic Dialog Simulation Technique to Develop and Evaluate Interactive Conversational Agents

Griol

Carbó

Molina

2013

Applied Artificial Intelligence

View full text Add to dashboard Cite

During recent years, conversational agents have become a solution to provide straightforward and more natural ways of retrieving information in the digital domain. In this article, we present an agent based dialog simulation technique for learning new dialog strategies and evaluating con versational agents. Using this technique, the effort necessary to acquire data required to train the dialog model and then explore new dialog strategies is considerably reduced. A set of measures has also been defined to evaluate the dialog strategy that is automatically learned and to compare different dialog corpora. We have applied this technique to explore the space of possible dialog stra tegies and evaluate the dialogs acquired for a conversational agent that collects monitored data from patients suffering from diabetes. The results of the comparison of these measures for an initial corpus and a corpus acquired using the dialog simulation technique show that the conversational agent reduces the time needed to complete the dialogs and improve their quality, thereby allowing the conversational agent to tackle new situations and generate new coherent answers for the situations already present in an initial model.

show abstract

Section: Case Application: the Di@l-log Conversational Agentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatic Dialog Simulation Technique to Develop and Evaluate Interactive Conversational Agents

Griol

Carbó

Molina

2013

Applied Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…DI@L-log is a spoken dialog system which acts as a voice logbook to collect home monitored data from patients suffering from Type-2 diabetes [1]. The data collected by the system are the patient's weight, blood pressure (systolic and diastolic values) and sugar levels.…”

Section: The Di@l-log Systemmentioning

confidence: 99%

“…In this paper, we apply this approach to develop a conversational agent which acts as a voice logbook to collect home monitored data from patients suffering from Type-2 diabetes [1], integrated in a context-aware multiagent architecture [3]. In this architecture, a Context Manager in the conversational agents deals with loading the context information provided by the User and Positioning Agents, and communicates it to the different modules of the Conversational Agent during the interaction.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Conversational Agents Using POMDPs and Agenda-Based Simulation

Griol

Molina

2011

Advances in Intelligent and Soft Computing

View full text Add to dashboard Cite

Abstract. Context-aware systems in combination with mobile devices offer new opportunities in the areas of knowledge representation, natural language processing and intelligent information retrieval. Our vision is that natural spoken conversation with these devices can eventually become the preferred mode for managing their services by means of conversational agents. In this paper, we describe the application of POMDPs and agenda-based user simulation to learn optimal dialog policies for the dialog manager in a conversational agent. We have applied this approach to develop a statistical dialog manager for a conversational agent which acts as a voice logbook to collect home monitored data from patients suffering from diabetes.

show abstract

“…During the last two decades, these interfaces have been increasingly used in Ambient Assisted Living (AAL) providing services such as interviews [55,117], counseling [57,70], chronic symptoms monitoring [23,56,97,102], medication prescription assistance and adherence [5,22], changing dietary behavior [44], promoting physical activity [51,120], helping cigarette smokers quit [124], speech therapy [131], and prognosis and diagnosis using different techniques [60,88].…”

Section: Introductionmentioning

confidence: 99%

Modeling the user state for context-aware spoken interaction in ambient assisted living

2014

View full text Add to dashboard Cite

Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfil these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influenc of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.

show abstract

Appraisal of a Conversational Artefact and Its Utility in Remote Patient Monitoring

Cited by 21 publications

References 1 publication

An Automatic Dialog Simulation Technique to Develop and Evaluate Interactive Conversational Agents

An Automatic Dialog Simulation Technique to Develop and Evaluate Interactive Conversational Agents

Context-Aware Conversational Agents Using POMDPs and Agenda-Based Simulation

Modeling the user state for context-aware spoken interaction in ambient assisted living

Contact Info

Product

Resources

About