EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots
Abstract
:1. Introduction
- Multi-scenario agent-based chatbot framework: In EREBOTS, it is possible to combine several context-dependent behaviors that can be encapsulated in dedicate story lines, which can be modeled as isolated or interconnected scenarios. These behaviors are enacted by a network of user agents, doctor agents, and orchestrated through gateway agents.
- User personalization: User agents build a model of the user profile, his/her preferences, history, goals, and aggregated information. With this model, the user agents are able to tailor behaviors and provide a personalized experience.
- Healthcare personnel control and monitoring: Medical doctors and healthcare providers have the possibility of defining possible goals, configure self-assessment interactions, or customize the types of activities proposed to patients/participants. Moreover, they can monitor users’ profiles with detailed analytic describing their behaviors and aggregated trends.
- Privacy and ethics compliance: In EREBOTS, all the sensitive/personal information are solely under the control of the user, who can make any decisions concerning storage and sharing of her information. Through the Pryv. platform [12] integrated into EREBOTS, users may configure fine-grained access control or even entirely remove their data if they decide so.
- Multi-campaign implementation and testing: EREBOTS has been employed and tested in scenarios such as smoking cessation and balance enhancement exercises (physical rehabilitation) for older adults during social confinement (due to COVID-19 restrictions).
2. State of the Art
2.1. HMI and Chatbots
- Amazon Lex: it supports the development of chatbots providing natural language understanding and automatic speech recognition [13].
- Dialogflow: it provides a framework aiming at understanding human conversations relying on Google’s machine learning techniques [14].
- Microsoft Bot Framework: it is a tool-set including APIs for text and speech analysis [15].
- SAP Conversational AI: based on SAP’s technology platform, it enables users to build and monitor intelligent chatbots, as well as to automate tasks and workflow [16].
- Rasa Open Source: it is a machine learning framework that allows the automation of text and voice-based chatbot assistants [17].
2.2. Quality of Experience
- (i)
- human factors such as user personality, expertise, health condition (visual acuity, auditory capacity, etc.);
- (ii)
- context factors such as the context in which a user is consuming a given service (e.g., alone, with friends, on the way to work, etc.); and
- (iii)
- system factors such as a system’s features characterizing the service provided (e.g., video resolution, sound quality, response rate, natural language processing quality, etc.).
2.3. Multi-Agent Systems & Chatbots
2.4. Chatbots in Assistive and eHealth Scenarios
2.5. Opportunities and Open Challenges
- C1
- Social A2A (Agent-to-Agent): While chatbots have been mainly employed in social campaigns, the social capabilities among the bots (i.e., to relate/extend/complete information) have yet to be fully exploited.
- C2
- Run-time healthcare supervision: Mental and physical wellness and nutritional and metabolic disorders are areas that can vastly benefit from employing chatbots to attain behavior change. Nevertheless, physicians consider unsafe to release unsupervised autonomous chatbots operating in safety-critical scenarios [61].
- C3
- Evolving models and behaviors: Chatbots can model the users quite comprehensively. However, the sociological dynamics and implications can quickly change, and current solutions cannot model nor properly embed evolving behaviors in the complex dynamics of current frameworks.
- C4
- Multi-stakeholder personalization: Chatbots are pervading increasingly complex healthcare applications. However, current solutions do not provide sufficient personalization for the diverse stakeholders’ roles (i.e., caregivers, physicians, or relatives [37]).
- C5
- Users’ QoE: The user is central in chatbot applications. Nevertheless, mechanisms to periodically collect, elaborate, and understand users’ feedback on their experience are missing [62].
- C6
- Dynamic update mechanisms: The repetitiveness of the solutions and/or functionalities suggested by the chatbots (usually due to static state machines and the lack of run-time updating mechanisms) can cause users to relapse and abandon the application.
- C7
- Semantics and Terminology: Often, the messages sent by the chatbot are predefined. However, due to the diversity of the stakeholders in healthcare scenarios, the terminology and related sentence formulation should be formulated dynamically (i.e., standardization vs. explanation).
- C8
- Delegation: Chatbots can replace humans in dealing with automated and repetitive tasks. However, the criteria for delegating a task (computation- and interaction-wise) to a chatbot need to be defined [63].
- C9
- Privacy compliance: While the chatbots’ interactions are mostly visible to the user, what occurs in the back-end is usually not as clear/transparent. In the best-case scenario, data management and visibility are described in human-made informative documentation, where the actual match with the system dynamics cannot be verified.
3. The EREBOTS Framework
- The Database component encloses two different databases: (i) MongoDB, used as centralized storage only for non-personal data. In particular, it stores the user’s messenger service chat ID (e.g., Telegram) and the user-specific endpoint token for the personal data store. (ii) Pryv (Available online: https://www.pryv.com/ (accessed on 5 March 2021)), which is a platform enabling privacy regulation-compliant, stream-based personal data collection, and privacy management. Once a user has registered an account, the user can provide consent to external applications, which then can access and store specified data. EREBOTS uses an instance of Pryv to persist the user’s chat history and all personal data (e.g., age, name, and scenario-specific data). Employing Pryv, users gain exclusive control of their data, thus being able to revoke the consent at any point, disabling EREBOT access to it, and, if necessary, fully removing any stored piece of information.
- The Communication server acts as message space for the inter-agent communication within the MAS. It uses a Prosody (Available online: https://prosody.im/ (accessed on 5 March 2021)) XMPP server instance where each agent embodies a registered user. An agent can broadcast messages to all agents (in the form of a multi-user chat) or directly message a specific agent (in the form of peer-to-peer sessions).
- The Back-end relies on the SPADE framework [65] to instantiate and interconnect virtual agents. In particular, it endows the doctor agent, which serves the campaign-related functionalities and bridges them with the underlying system’s dynamics. Moreover, the doctor agent exposes a web application allowing the medical personnel in charge of the campaign to manage storylines (general or personalized therapies) and overview user treatments adherence/results.
- The Front-end component is in charge of managing the users’ connections and their messages from the chat platform(s). Although extensible to other messaging systems, the framework currently supports the following communication interfaces. (i) Telegram (Available online: https://telegram.org/ (accessed on 5 March 2021)): a widely used free messaging application for mobile phones released in 2013, offering desktop applications for PC, Mac, and Linux. Since 2015, Telegram has enabled the development of chatbots with a dedicated bot API. (ii) HemerApp: a dedicated front-end based on Flutter (Available online: https://flutter.dev/ (accessed on 5 March 2021)), a framework for native multi-platform development. Therefore, the HemerApp can be used on iOS, Android, or web.While HemerApp allows a direct connection with the MAS (i.e., SPADES), all messages using Telegram have to pass through dedicated Telegram APIs. This requires the realization of a gateway agent. Moreover, such an agent handles the initial user communication (i.e., registration and user agent creation) for both interfaces. As of today, the two interfaces can coexist, although only one is allowed within a given campaign.
3.1. Scenario, Functionalities, Dynamics, and Behaviors
- SC1
- Preventive physical conditioning: it profiles the user according to a basic motor-balance assessment and his/her preferences and provides tailored exercises according to the user experience/profile both reactively and proactively.
- SC2
- Smoking cessations: it consists of a 2-phase campaign. In phase 1, the bot determines the severity of the addiction (i.e., daily consumption, nicotine dependency) while recording the user’s smoking habits. In phase 2, the bot assists the user during the craving episodes providing personalized mood boosters, health tips, behavioral tracking, feedback/reporting support, and adherence/efficacy evaluation.
- SC3
- Brest cancer survivors: The bot provides informational content and advice according to the type of cancer, demographics, stage, physical condition, etc. The bot may counsel exercise sets targeting regaining/maintaining muscular strength and minimum physical activity levels.
3.1.1. Scenario SC1
3.1.2. Functionalities
- DF1:
- Create, modify, and delete objectives, exercises, and relationships among them.
- DF2:
- Visualize a single user and her aggregated information.
- UF1:
- Register a new profile.
- UF2:
- Manage his/her profile and settings (i.e., language (As of today, SC1 supports English, Italian, French, and German), user goals, and ability re-evaluation).
- UF3:
- Ask for exercises (matching the user’s level).
- UF4:
- Visualize personal statistics and performance.
- UF5:
- Get detailed information about the system functionalities and data usage, visibility, and storage.
- (i)
- Define the user goals, such as the desired level of balance to be attained.
- (ii)
- Define the self-assessment questions, i.e., the set of questions to be asked to the user to determine her current situation with respect to the desired goals.
- (iii)
- Associate the questions to a specific difficulty level.
- (iv)
- Relate the questions to each other, defining the overall physical activity plan.
- (v)
- Define the exercises to be suggested, including their instructions, and related multimedia (see Figure 5).
- (vi)
- Assign the exercises to each difficulty level.
4. Experimentation
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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# | Question |
---|---|
1 | How difficult is it for you to keep your balance when you stand in a quiet environment? |
2 | How difficult is it for you to keep your balance when you walk around in the apartment? |
3 | How difficult is it for you to keep your balance when you climb up a stair? |
4 | How difficult is it for you to keep your balance when you reach for an object that is on the table far in front of you? |
5 | How difficult is it for you to keep your balance when you pick something up off the ground? |
6 | How difficult is it for you to keep your balance when you stand on tiptoe to get a cup from the cupboard? |
7 | How difficult is it for you to keep your balance when you are being pushed by your pet or by someone or when you stumble over something? |
8 | How difficult is it for you to keep your balance when you carry a package to the apartment? |
9 | How difficult is it for you to keep your balance when you step down a stair? |
10 | How difficult is it for you to keep your balance when you walk and look back? |
11 | How difficult is it for you to keep your balance when you walk across the wet bathroom floor? |
Individual | Difficulty Entry-Level | Description |
---|---|---|
7 | Difficult to keep balance being pushed. | |
4 | Difficult to reach objects far on a table. | |
5 | Difficult to pick something from the ground. | |
8 | Difficult to keep the balance while carrying a medium/big package. | |
8 | Difficult to keep the balance while carrying a medium/big package. | |
5 | Difficult to pick something from the ground. | |
9 | Difficult to keep balance stepping downstairs. | |
2 | Difficult to constantly keep balance when walking. | |
7 | Difficult to keep balance being pushed. | |
3 | Difficult to keep balance when climbing stairs. | |
4 | Difficult to reach objects far on a table. | |
10 | Difficult to keep balance when walking while looking back. | |
10 | Difficult to keep balance when walking while looking back. |
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Calvaresi, D.; Calbimonte, J.-P.; Siboni, E.; Eggenschwiler, S.; Manzo, G.; Hilfiker, R.; Schumacher, M. EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots. Electronics 2021, 10, 666. https://doi.org/10.3390/electronics10060666
Calvaresi D, Calbimonte J-P, Siboni E, Eggenschwiler S, Manzo G, Hilfiker R, Schumacher M. EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots. Electronics. 2021; 10(6):666. https://doi.org/10.3390/electronics10060666
Chicago/Turabian StyleCalvaresi, Davide, Jean-Paul Calbimonte, Enrico Siboni, Stefan Eggenschwiler, Gaetano Manzo, Roger Hilfiker, and Michael Schumacher. 2021. "EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots" Electronics 10, no. 6: 666. https://doi.org/10.3390/electronics10060666
APA StyleCalvaresi, D., Calbimonte, J.-P., Siboni, E., Eggenschwiler, S., Manzo, G., Hilfiker, R., & Schumacher, M. (2021). EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots. Electronics, 10(6), 666. https://doi.org/10.3390/electronics10060666