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Conversational AI for People, For Their Benefit.

admin — Wed, 01 Feb 2023 23:29:21 +0000

These days we hear a lot about conversations between machines and humans and how powerful they could be if they brought benefits to people.

The Horizon 2020 project COADAPT was an ambitious project and brought together an Italian-Belgian-Finnish-Greek network of eleven partners. COADAPT’s mission was to support aging citizens to adapt to changing conditions in the workplace and personal life with diverse type of enabling technologies and systems.

The research group led by Prof. Giuseppe Riccardi at the Department of Engineering & Information Science ( University of Trento , Italy) lead the design and training of human-machine dialogue systems for mental health. While there is a lot of interest in the mental health domain for applying AI, most attempts have resorted to Eliza-style interactions devoid of natural language processing and conversation abilities.

The research group designed and piloted a novel conversational artificial intelligence system in the mental health domain. One of the most important novel feature is the ability to manage longitudinal conversations while engaging individuals for a long period of time. The system can understand and decode the context of the user’s behavior and provide personalized therapeutic support and manage user-specific conversations. Last but not least the AI systems interact with psychotherapist to provide an integrated human-in-the-loop experience.

In the research journey quite a few novel concepts were developed and validated. A novel methodology for eliciting and collecting dialogue data in the mental health domain [1], the concept of “Dialogue Follow-Ups” for conversational artificial intelligence systems. The team also improved several state-of-the-art models for understanding user emotions from text [2] and introduced the new concept of “Emotion Carriers” to explain emotions in natural language processing [3,4]. It was developed a model to construct the Personal Space of the users throughout the dialogue in order to model each user by his/her real-life events and participants [5]. The mentioned innovative models and ideas were further integrated in a personalized conversational model named “TEO Therapy Empowerment Opportunity” [6]. TEO was deployed in the field using the latest low-latency human-in-the-loop AI framework and helped both the patients and the therapists to achieve better interaction and therapy outcomes. TEO is the first conversational agent based on the latest natural language processing and machine learning achievements to be evaluated in a registered Randomized Control Trials (RCT) [6,7]. The research team demonstrated that participants who received traditional CBT treatment with the support of the m-health application were likely to report better satisfaction and more stable trend of improvement limited to the individual perception of stress related symptoms [6].

PUBLICATIONS

[1] Seyed Mahed Mousavi, Alessandra Cervone, Morena Danieli, and Giuseppe Riccardi. 2021. Would you like to tell me more? Generating a corpus of psychotherapy dialogues. In Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, pages 1–9, Online. Association for Computational Linguistics.

[2] Gabriel Roccabruna, Steve Azzolin, and Giuseppe Riccardi. 2022. Multi-source Multi-domain Sentiment Analysis with BERT-based Models. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 581–589, Marseille, France. European Language Resources Association.

[3] Seyed Mahed Mousavi, Gabriel Roccabruna, Aniruddha Tammewar, Steve Azzolin, and Giuseppe Riccardi. 2022. Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 62–70, Dublin, Ireland. Association for Computational Linguistics.

[4] Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner, and Giuseppe Riccardi. 2020. Annotation of Emotion Carriers in Personal Narratives. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1517–1525, Marseille, France. European Language Resources Association.

[5] Mousavi, Seyed Mahed, Roberto Negro, and Giuseppe Riccardi. “An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain.” CLiC-it. 2021.

[6] Danieli M, Ciulli T, Mousavi SM, Silvestri G, Barbato S, Di Natale L, Riccardi G. Assessing the Impact of Conversational Artificial Intelligence in the Treatment of Stress and Anxiety in Aging Adults: Randomized Controlled Trial. JMIR Mental Health. 2022 Sep 23;9(9):e38067.

[7] Danieli M, Ciulli T, Mousavi SM, Riccardi G. A Conversational Artificial Intelligence Agent for a Mental Health Care App: Evaluation Study of Its Participatory Design. JMIR Formative Research. 2021 Dec 1;5(12):e30053.

Digital Health: Which Roles for Patients, Professionals and Machines?

admin — Thu, 07 Apr 2022 08:36:00 +0000

The objective of the panel is to have a discussion on the challenges and opportunities of digital health. In the past century, health has been the domain where inventions and innovations have greatly impacted society. More recently the pace of innovation has increased in the technology sector. Advances in technology include novel devices, sensors and of course the whole gamma of AI algorithms and systems. These new systems can greatly help to reach a high level of performance in healthcare and it has been shown in a variety of use cases. However, we have witnessed many failures and/or slow acceptance from the user end. The user groups in healthcare cover the patients, the healthcare professionals, the healthcare providers and the policy makers. There are many open questions on the design of technology that needs to fit processes that cover the whole patient journey. How technological systems can help motivate individuals or entire groups ( e.g. citizens ) to get better healthcare or join new therapeutic paths that include digital health ? Is there a human vs machine decision dilemma and what is that about ? These are some of the questions we will address with the help of a panel of international experts from academia and industry.

Autism and emotions: artificial intelligence reveals their neural encodings

admin — Wed, 09 Jun 2021 21:23:13 +0000

A study shows that facial emotions are encoded in the brains of people with autism (ASD). Published in the journal Biological Psychiatry, the joint University of Trento-Stony Brook University study dismantles some beliefs about brain functioning in people with ASD and opens up new scenarios to improve their relational life. With machine learning, a representation of the neural models that each brain applies to decode emotions has been created. Riccardi (University of Trento): “An interdisciplinary approach is essential” Emotions are a universal language and can usually be recognized easily and naturally. This is not the case for people with Autism Spectrum Disorder (ASD) for whom this simple activity is very limited at best. The reason for this difficulty has for years been the focus of scientific studies that try to shed light on the functioning of the brain in individuals affected by these disorders. A study by the University of Trento and Stony Brook University of New York published a few days ago in pre-print version in the journal Biological Psychiatry: Cognitive Neuroscience and Neuroimaging questions many beliefs and opens up new scenarios to improve living conditions and the social relationships of people with ASD. Reading facial expressions and decoding emotions is actually difficult for those with autism spectrum disorders. But the reason lies not in the brain’s ability to encode neural signals – as has always been thought – but rather in problems in the translation of information. A problem that in this period is also exacerbated by the containment measures of the pandemic. “Particularly now with the constant use of protective masks – explains Matthew D. Lerner, co-author of the study and professor of Psychology, Psychiatry and Pediatrics at Stony Brook University – limits the expressiveness of the face and this leads to less availability of information on our emotions. This is why it is important to understand how, when and for whom comprehension difficulties arise, what are the mechanisms underlying the misunderstanding ». The study’s conclusions are the result of a long analytical work that used machine learning techniques and could be useful for reviewing the approach with which people with ASD are helped to read the emotions of others. «At the moment there is a tendency to use prostheses for the recognition of emotions that help the visual perception of biological movement. Our results suggest that we should instead focus on how to help the brain transmit an intact encoding of the message that conveys the correctly perceived emotion “. Reading emotions with machine learning The study was conducted jointly by a group of researchers from Stony Brook University in New York and the University of Trento (Department of Engineering and Information Sciences) on 192 people of different ages with and without autism spectrum disorders. Their neural signals were recorded while displaying many facial emotions and subsequently analyzed. To do this, the research team employed a new facial emotion classification system that leverages machine learning, called Deep Convolutional Neural Networks. This “machine learning” approach includes an algorithm that allows you to analyze and classify the activity of the brain while observing faces, detected by electroencephalography (EEG). The result is a very accurate map of the neural patterns that each person’s brain applies to decode emotions. “Technologies derived from machine learning are generally considered to be an engine of innovation in processes and products in all industrial sectors”, comments Giuseppe Riccardi, co-author of the study and professor of Information Processing Systems at the University of Trento (Department of Engineering and Information Science). “And it is also evident in this case. Machine learning techniques can help us interpret brain signals in the context of emotions. First of all, they can be decisive in supporting the early stages of basic scientific research. But they can also be used directly for clinical interventions. The study we conducted shows how much a strong integration of interdisciplinary skills is necessary for artificial intelligence to have a measurable impact on people’s lives “.

Conversational Artificial Intelligence for Healthy Ageing

admin — Thu, 13 Dec 2018 12:14:26 +0000

Trento, December 12 2018 – Almost four million in funding to create an ambitious project: developing conversational artificial intelligence to support aging and active population. It is the overall budget of “Coadapt”, the international project funded by the European Commission under the Horizon 2020 program, in which the University of Trento will lead the research and development of conversational artificial intelligence prototypes. A large part of the funding (approx 700 thousand euros) will be allocated to the research group lead by the Prof. Giuseppe Riccardi with the Department of Engineering e Information Science (DISI). The conversational and intelligent system will be supported by wearable and sensing technology, such as wrist or arm band or rings. Ageing workers can count on support from remote to face-to-face interactions with psychotherapist to manage various sources of stress ( stressors ) in the workplace and in their personal life.

The University of Trento has been chosen to host the kick off meeting, the meeting that officially starts the international project. The representatives of the partners met today in the Le Albere district for the first meeting and to discuss their workplan which will see them engaged for the next five years. The Italian-Belgian-Finnish network includes for Finland the University of Helsinki (coordinator), the University of Aalto, the Finnish Institute of Occupational Health (FIOH) and the company Etsimo Healthcare Ltd; for Belgium the company Innovation Sprint Sprl, for Italy the universities of Trento and Padua and the companies Idego Srl, BNP Srl and Electrolux Italia spa. The goal is the design, training and evaluation of intelligent systems to support healthcare professionals, patients and people in managing change in the workplace and in their personal life. «This integrated system will allow the doctor to develop personalized therapies or elaborate recommendations based on specific data collected on various touch points »explains Professor Giuseppe Riccardi, coordinator of the DISI research group. “The personal healthcare agents will be able to decode the surrounding environment, interpret the social behavior of the person and, at the same time, to detect the operating proxy data from human organs. For example, conversational agent will be able to listen to a voice, read a text, interpret a state of mind, a meaning, a basic emotion but also a complex emotional state. Sensors signals and analytics will be collected in real time, interpreted, summarized and made available to the healthcare professionals for an integrated intervention.”

What we are doing at UNITN

Personal Healthcare Agents (PHA)

admin — Fri, 13 May 2016 20:35:30 +0000

Personal Healthcare Agents (PHA) will change people’s lives and revolutionize the way they manage their wellbeing and health. They will be able to sense the environment , the personal and social behavior , as well as the human organ systems. They will be elaborating, interpreting, summarizing and making sense of these signals and share it with you as well as your caregivers. They will be playing a key role in providing evidence for personalized therapies and in the doctors’ decision-making processes. PHAs will be supporting and motivating people to stir their habits towards healthy lifestyles. PHAs will be engaging patients to behave according to doctors’ recommendations and prescriptions.

Prototypes & Demos

admin — Fri, 28 Nov 2014 21:25:08 +0000

Conversational Agents, Language Understanding Systems, Affective Computing and more. The SIS Lab realizes intelligent and conversational machine prototypes. In this selection we give an overview of the research challenges and applications.