Adaptable dialogue architecture and runtime engine (AdaRTE): A framework for rapid prototyping of health dialog systems

International audienceSpoken dialog systems have been increasingly employed to provide ubiquitous access via telephone to information and services for the non-Internet-connected public. They have been successfully applied in the health care context; however, speech technology requires a considerable development investment. The advent of VoiceXML reduced the proliferation of incompatible dialog formalisms, at the expense of adding even more complexity. This paper introduces a novel architecture for dialogue representation and interpretation, AdaRTE, which allows developers to lay out dialog interactions through a high-level formalism, offering both declarative and procedural features. AdaRTE's aim is to provide a ground for deploying complex and adaptable dialogs whilst allowing experimentation and incremental adoption of innovative speech technologies. It enhances augmented transition networks with dynamic behavior, and drives multiple back-end realizers, including VoiceXML. It has been especially targeted to the health care context, because of the great scale and the need for reducing the barrier to a widespread adoption of dialog systems

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

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 fulfill 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 influence of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02- 02, CAM CONTEXTS (S2009/TIC-1485

Conversational agents in healthcare: a systematic review.

Objective: Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods: We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement. Results: The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions: The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration: The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917

Heart rate monitoring, activity recognition, and recommendation for e-coaching

Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter

A Novel Approach to Improve the Planning of Adaptive and Interactive Sessions for the treatment of Major Depression

[EN] Human Computer Interaction (HCI) is a research field which aims to improve the relationship between users and interactive computer systems. A main objective of this research area is to make the user experience more pleasant and efficient, minimizing the barrier between the users' cognition of what they want to accomplish and the computer's understanding of the user's tasks, by means of userfriendly, useful and usable designs. A bad HCI design is one of the main reasons behind user rejection of computer-based applications, which in turn produces loss of productivity and economy in industrial environments. In the eHealth domain, user rejection of computer-based systems is a major barrier to exploiting the maximum benefit from those applications developed to support the treatment of diseases, and in the worst cases a poor design in these systems may cause deterioration in the clinical condition of the patient. Thus, a high level of personalisation of the system according to users' needs is extremely important, making it easy to use and contributing to the system's efficacy, which in turn facilitates the empowerment of the target users. Ideally, the content offered through the interactive sessions in these applications should be continuously assessed and adapted to the changing condition of the patient. A good HCI design and development can improve the acceptance of these applications and contribute to promoting better adherence levels to the treatment, preventing the patient from further relapses. In this work, we present a mechanism to provide personalised and adaptive daily interactive sessions focused on the treatment of patients with Major Depression. These sessions are able to automatically adapt the content and length of the sessions to obtain personalised and varied sessions in order to encourage the continuous and long-term use of the system. The tailored adaptation of session content is supported by decision-making processes based on: (i) clinical requirements; (ii) the patient's historical data; and (iii) current responses from the patient. We have evaluated our system through two different methodologies: the first one performing a set of simulations producing different sessions from changing input conditions, in order to assess different levels of adaptability and variability of the session content offered by the system. The second evaluation process involved a set of patients who used the system for 14 to 28 days and answered a questionnaire to provide feedback about the perceived level of adaptability and variability produced by the system. The obtained results in both evaluations indicated good levels of adaptability and variability in the content of the sessions according to the input conditions.E. Fuster Garcia acknowledges the financial support from the "Torres Quevedo" program (Spanish Ministry of Economy and Competitiveness) co-funded by the European Social Fund (PTQ-12-05693), and the financial support from the Universitat Politecnica de Valencia under the Grant "Ayudas Para la Contratacion de Doctores para el Acceso al Sistema Espanol de Ciencia, Tecnologia e Innovacion" (PAID-10-14).Bresó Guardado, A.; Martínez Miranda, JC.; Fuster García, E.; García Gómez, JM. (2016). A Novel Approach to Improve the Planning of Adaptive and Interactive Sessions for the treatment of Major Depression. International Journal of Human-Computer Studies. 87:80-91. https://doi.org/10.1016/j.ijhcs.2015.11.003S80918

Non-invasive vascular assessment using photoplethysmography

Photoplethysmography (PPG) has become widely accepted as a valuable clinical tool for performing non-invasive biomedical monitoring. The dominant clinical application of PPG has been pulse oximetry, which uses spectral analysis of the peripheral blood supply to establish haemoglobin saturation. PPG has also found success in screening for venous dysfunction, though to a limited degree. Arterial Disease (AD) is a condition where blood flow in the arteries of the body is reduced,a condition known as ischaernia. Ischaernia can result in pain in the affected areas, such as chest pain for an ischearnic heart, but does not always produce symptoms. The most common form of AD is arteriosclerosis, which affects around 5% of the population over 50 years old. Arteriosclerosis, more commonly known as 'hardening of the arteries' is a condition that results in a gradual thickening, hardening and loss of elasticity in the walls of the arteries, reducing overall blood flow. This thesis investigates the possibility of employing PPG to perform vascular assessment, specifically arterial assessment, in two ways. PPG based perfusion monitoring may allow identification of ischaernia in the periphery. To further investigate this premise, prospective experimental trials are performed, firstly to assess the viability of PPG based perfusion monitoring and culminating in the development of a more objective method for determining ABPI using PPG based vascular assessment. A complex interaction between the heart and the connective vasculature, detected at the measuring site, generates the PPG signal. The haemodynamic properties of the vasculature will affect the shape of the PPG waveform, characterising the PPG signal with the properties of the intermediary vasculature. This thesis investigates the feasibility of deriving quantitative vascular parameters from the PPG signal. A quantitative approach allows direct identification of pathology, simplifying vascular assessment. Both forward and inverse models are developed in order to investigate this topic. Application of the models in prospective experimental trials with both normal subjects and subjects suffering PVD have shown encouraging results. It is concluded that the PPG signal contains information on the connective vasculature of the subject. PPG may be used to perform vascular assessment using either perfusion based techniques, where the magnitude of the PPG signal is of interest, or by directly assessing the connective vasculature using PPG, where the shape of the PPG signal is of interest. it is argued that PPG perfusion based techniques for performing the ABPI diagnosis protocol can offer greater sensitivity to the onset of PAD, compared to more conventional methods. It is speculated that the PPG based ABPI diagnosis protocol could provide enhanced PAD diagnosis, detecting the onset of the disease and allowing a treatmenpt lan to be formed soonert han was possible previously. The determination of quantitative vascular parameters using PPG shape could allow direct vascular diagnosis, reducing subjectivity due to interpretation. The prospective trials investigating PPG shape analysis concentrated on PVD diagnosis, but it is speculated that quantitative PPG shaped based vascular assessment could be a powerful tool in the diagnosis of many vascular based pathological conditions

How to write health dialog for a talking computer

AbstractAutomated dialogue systems delivered over the telephone offer a promising approach to delivering health-related interventions to populations of individuals at low-cost. Over the past two decades, an automated telephone system called Telephone-Linked Care or TLC has been successfully designed and evaluated by the authors and their colleagues. This work has resulted in over twenty systems for various health-related conditions and lifestyle behaviors. This paper describes our approach to developing and writing dialogue for these automated telephone systems, including determining the program objectives, defining the target population, and selecting a theory of behavior change to guide the intervention. Both macro and micro issues are considered in constructing dialogue systems that are engaging for the target population, easy to use, and effective at promoting positive health behaviors and outcomes

Digital Health’s Impact on Integrated Care, Carer Empowerment and Patient-Centeredness for Persons Living With Dementia

E-health or digital health technologies endeavour to connect key stakeholders and thereby lay the foundation for better integrated as well as potentially more patient-centered care. However, despite the promise of empowerment, efficiency and value, digital health has yet to become part of the daily lives of the people who care for persons living with dementi

Systematic review of context-aware digital behavior change interventions to improve health

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013-2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users' health behaviors

2010 Abstract Booklet

Complete Schedule of Events for the 12th Annual Undergraduate Research Symposium at Minnesota State University, Mankato