Meet Method's FINE
We’ve all heard that response, “yeah, I’m fine.” It’s something we say when we’re fine and something we say when we are anything but fine.
According to recent studies on mental health, one in ten children is affected by a serious psychological problem, with future projections showing an alarming increase in this trend. That’s why we have taken the initiative to explore new models of engagement and investigate the potential use of empathy applied to human-machine interaction.
In mental health everybody is different, so it is important that every voice is heard.
Challenge
During a 5 month deep-dive, we gained first-hand insight into non-intrusive mechanisms for the prevention of depression in childhood and adolescence, collaborating with key experts in the field, children, and parents through an open and co-creative process.
Achievements
By bringing together design thinking, Artificial Intelligence and the principles of crowdsourcing, FINE (Feeling Insecure, Negative, Emotional) enables a digital friend to react empathetically to a child’s emotional state.
A machine learning ‘empathetic’ model has been trained to read and react to emotions appropriately, with a corresponding family hub displaying the child’s and family member’s collective mood over time, acting as a central trigger to the habit-forming routine of talking about emotions at home, and encouraging the kind of positive behavior change that leads to a preventative and collective caretaking of how one feels.
Awards for FINE:
- Fast Company’s Innovation by Design: Finalist, Health Category
- Fast Company’s World Changing Ideas, Nominee: App Category
Service
- Service & Product Innovation
Duration
5 Months
Capabilities
- Brand Design
- Experience Design
- Product Design
- Prototyping
- Research & Insights
- Service Design
- Software Engineering
Location
- London
“I’ve been amazed how working with Method, the team has been able to move so quickly from learning about our professional field to being able to contribute so perceptively as if they had been working with us for years.”
Dr. Emilios Lemoniatis, Consultant Child and Adolescent Psychiatrist The Tavistock and Portman, NHS Foundation Trust
Making children the experts
An open and non-stigmatizing approach was key in our research process. With co-creation sessions, we invited children and parents to share their experiences and create low-fi prototypes of future concepts. This had a strong influence on our design concepts, which received positive praise in a series of subsequent validation exercises.
Can we make an empathetic AI?
As part of our research, we found that no dataset currently exists that would teach a machine learning model how to be empathetic. As a result, we designed and created an application that captures empathetic responses to emotional stimuli. Through a crowdsourcing initiative, we generated enough data to train a model over time on how to respond to different moods detected by analyzing the human face. This showcases the potential use cases of new forms of human-machine interaction; one we can experience right now.
