HumanLink: Your Open-Source Anthropometric Database
My Role: UX Designer; Project Manager; Stakeholder Communications
Tools: Miro; FigJam; Lovable
Context: Group project for Human Factors in Product Design & Development Class, brought to us by stakeholders at Intuitive & Google
Tools: Miro; FigJam; Lovable
Minimal Viable Product
At the request of our stakeholder, we used AI to create our MVP rather than build it ourselves on Figma. We decided to use Lovable.AI, and fed it all of our previous materials (interview notes, wireframes, and our information architecture), along with various prompts and tweaks to generate our MVP. Since we don’t have any actual anthropometric data to populate the database with, this serves as a reference point for our stakeholders as they continue to develop this product in the future. Below is an embedded version of our MVP. Please feel free to explore the interface!
My Role
As project manager, I was responsible for all communication between our team and our stakeholders. I organized and led weekly meetings with these stakeholders, as well as delegating tasks within the team and creating timelines for us to stay on track.
My Role
As project manager, I was responsible for all communication between our team and our stakeholders. I organized and led weekly meetings with these stakeholders, as well as delegating tasks within the team and creating timelines for us to stay on track.
Our Process…
Exploratory Interviews
Our stakeholders came to us (a team of four) with a general idea of what they had wanted based on their experiences as Human Factors Engineers in the industry. In order to learn more about what other professionals and students experience, we conducted one-on-one interviews with human factors engineers across various disciplines, biomedical engineers, students, and artists—all potential key user groups for HumanLink. From these interviews, we learned more about the pain points that they all face with current anthropometric databases, what features would help them, and the kind of information that they would be looking for in a database. All of this helped inform our information architecture. The goal of our interviews was to learn more about pain points with existing systems, learn about what features people are looking for, and to get a general idea of whether or not there is a need for this product in the market.
List of user objectives and requirements (left) and user needs & requirements (right) gathered from exploratory interviews.
Information Architecture
Using the information gathered from our interviews, we created our IA using FigJam. Displayed below is the an interactive display of our final draft. It includes features mentioned during our exploratory research, AI tools to aid users with less experience navigating anthropometric databases, and filters that allow users to get exactly what it is they’re looking for.
Information architecture as developed on FigJam
Wireframes
We built our wireframes on Miro. We focused on the filtering system, the home page, and the results pages. The gallery below will take you through a walkthrough of the wireframe, starting at the home page, moving to the results page, and followed by an individual body part page. The body part pages have expandable information, allow users to save the part to a project, or add it to a relationship. It also recommends similar pages based on proximity to the part and how often another part is paired with it by other users.
Exploratory embed of our MVP developed on Lovable. Feel free to take a look around!
Minimal Viable Product
At the request of our stakeholder, we used AI to create our MVP rather than build it ourselves on Figma. We decided to use Lovable.AI, and fed it all of our previous materials (interview notes, wireframes, and our information architecture), along with various prompts and tweaks to generate our MVP. Since we don’t have any actual anthropometric data to populate the database with, this serves as a reference point for our stakeholders as they continue to develop this product in the future. Below is an embedded version of our MVP. Please feel free to explore the interface!
Minimal Viable Product
At the request of our stakeholder, we used AI to create our MVP rather than build it ourselves on Figma. We decided to use Lovable.AI, and fed it all of our previous materials (interview notes, wireframes, and our information architecture), along with various prompts and tweaks to generate our MVP. Since we don’t have any actual anthropometric data to populate the database with, this serves as a reference point for our stakeholders as they continue to develop this product in the future. Below is an embedded version of our MVP. Please feel free to explore the interface!
Project Scope:
HumanLink is an early-stage UX project that aims to create an open-source database where users can easily access and upload anthropometric data, making the worldwide UX design space more inclusive and accessible for students and professionals alike. When the project came to us, it was still in ideation phases.
Key Decisions:
1) Interviewing human factors engineers from a wide variety of disciplines to learn more about existing databases and pain points to give us an idea of where to bring the project.
2) Focusing on two calls to action:
⟶ Allowing users to easily search for and download anthropometric data
⟶ Allowing users to upload their own data reliably onto the database
3) Creating an interactive human body diagram for users to search through, allowing users to find information for body parts they may not know the formal names of.
4) Using Lovable AI to create our MVP by feeding it our wireframes and information architecture, as well as troubleshooting. This allowed us to have a prototype more quickly and interactive than we would have using a traditional tool like Figma.
Minimal Viable Product
At the request of our stakeholder, we used AI to create our MVP rather than build it ourselves on Figma. We decided to use Lovable.AI, and fed it all of our previous materials (interview notes, wireframes, and our information architecture), along with various prompts and tweaks to generate our MVP. Since we don’t have any actual anthropometric data to populate the database with, this serves as a reference point for our stakeholders as they continue to develop this product in the future. Below is an embedded version of our MVP. Please feel free to explore the interface!
My Role
As project manager, I was responsible for all communication between our team and our stakeholders. I organized and led weekly meetings with these stakeholders, as well as delegating tasks within the team and creating timelines for us to stay on track.
My Role
As project manager, I was responsible for all communication between our team and our stakeholders. I organized and led weekly meetings with these stakeholders, as well as delegating tasks within the team and creating timelines for us to stay on track.
Minimal Viable Product
At the request of our stakeholder, we used AI to create our MVP rather than build it ourselves on Figma. We decided to use Lovable.AI, and fed it all of our previous materials (interview notes, wireframes, and our information architecture), along with various prompts and tweaks to generate our MVP. Since we don’t have any actual anthropometric data to populate the database with, this serves as a reference point for our stakeholders as they continue to develop this product in the future. Below is an embedded version of our MVP. Please feel free to explore the interface!
Our Process…
List of user objectives and requirements (left) and user needs & requirements (right) gathered from exploratory interviews.
Information architecture as developed on FigJam
Wireframes
We built our wireframes on Miro. We focused on the filtering system, the home page, and the results pages. The gallery below will take you through a walkthrough of the wireframe, starting at the home page, moving to the results page, and followed by an individual body part page. The body part pages have expandable information, allow users to save the part to a project, or add it to a relationship. It also recommends similar pages based on proximity to the part and how often another part is paired with it by other users.
Wireframes
We built our wireframes on Miro. We focused on the filtering system, the home page, and the results pages. The gallery below will take you through a walkthrough of the wireframe, starting at the home page, moving to the results page, and followed by an individual body part page. The body part pages have expandable information, allow users to save the part to a project, or add it to a relationship. It also recommends similar pages based on proximity to the part and how often another part is paired with it by other users.
Wireframes
We built our wireframes on Miro. We focused on the filtering system, the home page, and the results pages. The gallery below will take you through a walkthrough of the wireframe, starting at the home page, moving to the results page, and followed by an individual body part page. The body part pages have expandable information, allow users to save the part to a project, or add it to a relationship. It also recommends similar pages based on proximity to the part and how often another part is paired with it by other users.
Exploratory Interviews
Our stakeholders came to us (a team of four) with a general idea of what they had wanted based on their experiences as Human Factors Engineers in the industry. In order to learn more about what other professionals and students experience, we conducted one-on-one interviews with human factors engineers across various disciplines, biomedical engineers, students, and artists—all potential key user groups for HumanLink. From these interviews, we learned more about the pain points that they all face with current anthropometric databases, what features would help them, and the kind of information that they would be looking for in a database. All of this helped inform our information architecture. The goal of our interviews was to learn more about pain points with existing systems, learn about what features people are looking for, and to get a general idea of whether or not there is a need for this product in the market.
List of user objectives and requirements (left) and user needs & requirements (right) gathered from exploratory interviews.
Information Architecture
Using the information gathered from our interviews, we created our IA using FigJam. Displayed below is the an interactive display of our final draft. It includes features mentioned during our exploratory research, AI tools to aid users with less experience navigating anthropometric databases, and filters that allow users to get exactly what it is they’re looking for.
Information architecture as developed on FigJam
Wireframes
We built our wireframes on Miro. We focused on the filtering system, the home page, and the results pages. The gallery below will take you through a walkthrough of the wireframe, starting at the home page, moving to the results page, and followed by an individual body part page. The body part pages have expandable information, allow users to save the part to a project, or add it to a relationship. It also recommends similar pages based on proximity to the part and how often another part is paired with it by other users.
Exploratory embed of our MVP developed on Lovable. Feel free to take a look around!