Explainable AI (XAI) for Pain and Anxiety Detection: Developed ensemble and deep-learning models, trained on dementia-related datasets, to detect pain, anxiety and emotion. Focused on XAI to ensure clinical interpretability. Utilized multimodal datasets (Wearable Stress and Affect Detection (WESAD), Psychophysiology of Positive and Negative Emotions (POPANE), Fudan Sleep, Lifesnaps Fitbit, Pain E-motion Faces) for emotion, stress, pain, and sleep analysis. Selected the lightweight Polar 360 wearable for real-world dementia care data collection.
Multilingual service to extract medical concepts from unstructured text: Initial pipeline being integrated into Patient Sensing Network. The service extracts PROBLEM, TEST, or TREATMENT concepts from an input string, not limited to clinical semantics, and is being mapped to Fast Healthcare Interoperability Resources (FHIR) Composition and augmented with Logical Observation Identifiers Names and Codes (LOINC) methodology.
Pipelines to extract observable cues of pain and distress from diary recordings: The end-to-end pipeline serves as a comprehensive system for automated pain and distress detection from patient diary recordings. The initial pipeline for symptoms of mental distress was adapted from the SMILE project and integrated into the AI4HOPE framework. The work carried out in the context of AI4HOPE includes adaptation of the observable to the context of PwD and implementation of models to extract pain digital biomarkers-(Activity continues).
Conversational agents' framework for patient engagement: Implemented a conversational framework using RASA (open-source conversational AI platform) based chatbots for collecting patient-reported outcomes and assessing distress/pain risks, with large language model-based assistants for personalized education and information delivery on dementia progression and QoL management. The operational RASA chatbot and initial prototype AI assistant will undergo patient-expert validation in the next sprint.
Multisensory Patient Sensing Network: Designed a multisensory emotion recognition and pain/distress assessment framework, comprising individual analytical pipelines and integrated services.
The Dementia Journey Companion (DJC): A digital assistant empowering PwD by documenting personal preferences and routines, preserving life stories, and providing tailored education through AI.
User Stories & Use Cases: These outline the initial processes and findings from stakeholder engagement and requirements gathering efforts within the AI4HOPE project. It details the methodologies adopted, including co-creation workshops, focus groups, and consultations with public involvement groups, emphasizing the integration of value-sensitive design within an agile development framework. It shows how the insights derived from the WP timeline are integrated into the project's evaluation strategy. We also define five personas, patients (mild–moderate dementia), informal carers (family/friends), formal carers (professional carers/health aides), nurses, and clinicians/healthcare professionals, and two pilot-aligned use cases: (1) Assessment & Monitoring (Pilot 1); (2) Living with Dementia, Journey & ACP (Pilot 2). Over 120 user stories span system components, capturing diverse desires, interactions, and requirements to guide the development and evaluation of AI-driven solutions for dementia care.
Feasibility testing: Two pilots (n=150 each) in mild PwD across Ireland, Germany, Portugal, Slovenia, Spain and the UK will assess feasibility and acceptability of the digital intervention and the DJC, measuring perceived benefits/burdens and QoL impact.
