Mobile App for Personal Use
A brief trailer showcasing the core features for mood logging, data visualization, and contextual note-taking.
Building wearable technology that monitors emotions to improve anxiety disorders treatment
I'm Luigi A. Moretti, PhD candidate at UWE Bristol, former healthtech founder, medical doctor. I'm developing emotion monitoring technology that helps patients understand themselves and clinicians provide better care. Using consumer wearables like the Google Pixel Watch 2, MEMoPAD bridges clinical research and real-world deployment.
What
Wearable emotion monitoring for anxiety disorders (GAD, PTSD, OCD, PD, SAD)
Technology
Google Pixel Watch 2 sensors (cEDA, PPG, skin temperature) + self-reported data on the mobile app
Funding
3.5y PhD scholarship and £7,500+ from NHS BNSSG ICB and UWE Bristol
How Emotion Monitoring Helps Anxiety Disorders
- Continuous tracking: Captures patterns missed by self-report questionnaires
- Real-world context: Links emotions to daily activities, triggers, and coping strategies
- Objective data: Reduces recall bias and provides physiological evidence for clinical conversations
- Early intervention: Improves self-awareness, supporting proactiveness and timely interventions
- Personalized insights: Recognizes individual patterns and multimodal data correlations
The Clinical Need
Previous attempts to integrate structured data in mental health have faced persistent challenges, including the complexity of multimodal data, the social stigma that delays help-seeking, and difficulties in data interpretation which can hinder user engagement. We believe focusing on emotion can mitigate these issues by providing a universally understood common ground.
Our Approach
Commercially available wearable devices feature increasingly sophisticated sensors at affordable prices, without the stigma associated with medical-looking hardware. Our approach focuses on data collected from consumer wearables and mobile phones to capture longitudinal and contextualized data through multimodal affective computing.
Our Vision
Our goal is to co-design a digital tool that provides clear, continuous, and interpretable emotion data. We aim to help users understand themselves better, foster proactive self-management, and facilitate deeper, more effective conversations with their clinicians, ultimately improving clinical pathways for anxiety care.
Our Unique Value Proposition
- Emotion as a Common Language: We use emotion as a foundational layer to make complex multimodal data more understandable and to avoid the stigma of clinical labels or to trigger more anxiety.
- Hybrid Data Fusion: We combine passive data from everyday wearables with active, user-provided content to create a richer, more complete picture of an individual's well-being and foster self-reflection.
- Semi-Structured Context: The tool allows users to add context to their data in a simple way, making the insights personally relevant and clinically valuable.
- Clinical Integration Focus: From the outset, the project is designed as a tool to be integrated directly into existing clinical pathways, supporting patients, clinicians, and carers.
Alignment with UK Health Priorities
The MEMoPAD project directly supports the UK's 10-Year Health Plan by developing the framework needed to integrate wearable devices and AI into clinical pathways. By empowering patients with their own data, we enable a more personalized approach to care. This focus on prevention addresses a key challenge identified by our co-design participants: supporting individuals in seeking help for the first time. By improving mental well-being, our work also contributes to the wider societal benefit of boosting workforce productivity and supporting people's mental health in daily as well as in challenging scenarios.
See MEMoPAD in Action
Explore our progress through the latest video trailer of the mobile app and the interactive prototype of our web app for clinicians.
Web App for Professionals
Navigate a clickable prototype of the clinician-facing dashboard. Click the image to explore.