Decreasing mental health due to loneliness is a prevalent issue worldwide, especially for people moving and transitioning to new environments, finding themselves unable to regularly rely on family and friends from home.
How can we emotionally support people who are unable to rely on their support systems?
Ami is a pocket companion mobile application that provides emotional support to users through AI emotion recognition and recommends activities to do to help maintain or improve mood.
Our team started off by doing research on who our target audience was and what their needs were. We found research on people who felt lonely, unsupported and what devices they were most comfortable using—social media and their phones.
The team conducted research on applications that aimed to help people with their mental health, such as self-help and therapy applications, and what unique approaches these applications took. We looked for what was missing in the market as well as the pain points that users experienced.
Through our research, we found that although mental health applications were able to give people the tools for mental success, it didn’t help with loneliness and/or relieving emotions at the moment. An interesting way that some applications helped users was through a chatbot. This could help users feel a personal connection to alleviate loneliness.
Although there are many people who are lonely around the world, we decided to target specifically youth and young adults going through life environmental life transitions such as a new school or job. With this target demographic and our market research in mind, we created a user persona.
We created a user flow of the application in Figma together to map out the features of the application. The main features were the AI chatbot, the calendar to track moods and a list of previously suggested activities. These activities include music, video, podcasts, physical activities or locations of interest. We created 2 user scenarios to help to describe the different situations a user may create in chat.
After figuring out the user flow, we got pencil and paper and sketched out what each page would look like. During this ideation, we had some trouble figuring out the information architecture of the recommendations feature, came up with multiple solutions and asked multiple mentors for their thoughts. After finalizing our sketches we went ahead and created wireframes and prototyped them in Figma.
Our solution to the research we conducted was Ami, an AI chat companion mobile application that provides an emotional support system to people feeling lonely and are unable to rely on someone at their time of need. Users can chat with Ami through voice-recognition or text throughout the day. Ami analyzes the user’s words to identify their mood and suggest activities to uplift or maintain their mood. After these conversations, Ami tracks moods and suggestions for the user to view later.
Ami prompts the Eden to specify what kind of recommendations they would like during their conversations. This includes various activities indoors and outdoors for the Eden the explore.
Eden opens up the app Ami and is prompted to tell Ami about his day. Through voice recognition, Eden tells Ami that he, unfortunately, had a bad day today. Ami identifies that Eden is currently stressed and anxious and gives him a recommendation that may help with his situation.
Ami is not only there for you when you’re feeling sad, but is also there for your happy moments. Eden here is telling Ami about his raise and how happy he is. Ami identities his joy and celebrates with him.
Ami offers filtering options for Eden to specifically choose what he would like to see. For instance, he can choose to look for recommendations for Joy or favourites.
Eden is also able to see his progression of emotions through the Calendar that Ami provides. Here he reviews an old conversation when he felt stressed and anxious.
This was my first time working at a hackathon filled with students and mentors from different disciplines. I loved being able to ideate with my group and immediately receiving feedback to validate our ideas by going to other hackathon participants and mentors. The environment was fast-paced and filled with energy that I really enjoyed working in.
It is one thing to think of an idea that you think solves problems for people, and another to find issues to solve. We had a general idea that we wanted to create an application that helped people with mental health but it was difficult to narrow down what exactly we should do. By doing user research into what specific issues people had in the real world, we were able to narrow down an idea and validate it using the people around us that matched these personas.