CONVOY is a wearable device and an app for women to feel safe walking at night
Product Design, Wearable Device Design, UX Research, UX/UI Design, Hardware UX
August 2019 - December 2019
Figma, Rhinoceros, KeyShot, Photoshop, Illustrator, 3D Printing, Arduino
Anjali Devakumar, Morgan Chin and Yujin Xue
In the United States, 45% of women report they do not feel safe walking alone at night
An app and a wearable device that suggests safe routes according to crime data of an area.
The wearable device guides users to the desired destination by using voice and haptic interaction. Users are able to get information about neighborhoods and receive the latest crime and news stories.
Literature Review and Competitive Analysis methods selected to explore the problem space.
Socioeconomic status, ethnicity, and age can increase the levels of anxiety experienced when walking alone at night.
Women tend to arrange their plans to occur in daylight, walk different routes, and do not attend events at night to stay safe.
They feel the urge to let their friends and family know when they arrived at the desired location safely.
Despite the high statistics and adverse effects of this problem, a robust product or a system does not currently exist. 3 crucial issues with these products defined:
Lack of feedback, state of the product, and communication.
The interaction is either takes a lot of time or the language is too complex to understand.
Once the emergency state is activated it can't be canceled.
02. User Research
In order to understand our user group better, we have conducted various user research methods such as surveys and semi-structured interviews + follow-up questionnaire.
~ 15 mins
To understand the trend of what our users make them feel safe, we distributed a survey. This helped us to categorize certain behaviors and perceptions towards walking alone as women.
9 female participants
~ 1 hour
20 defined questions
Statements of our participants bolstered the information about not feeling safe walking at night.
Quality of the environment (maintenance, lumination, etc.) that provides safety changes according to the individual.
In contrast to our assumptions, users don't want to carry items or weapons in their hands and don't prefer to be distracted by their phones.
Participants's own handwritings about what makes them safe or not
03. Understanding Data
To understand our data, we created an Affinity Map with the data that be obtained from user research activities.
We found 19 major pain points that all were unique in their situation, contexts, and relevant to the original problem. We decided that there were six insights that we wanted to concentrate on moving forward.
Being unfamiliar with an area makes women unsafe
Being able to connect with other people helps our users to feel safe
People don't reach out to the police when they are uncertain that they are in danger
Users want to reach authentic crime information of a neighborhood
Users want to know about the different qualities of an area which makes them feel safe (ex. illumination)
People prefer to be closer to the areas that are more crowded
04. Understanding Users
By creating user personas, empathy maps, journey maps, and task analysis we aimed to go deeper into their experiences, thoughts, and emotions by giving our users "a voice".
To come up with a wide range of ideas we engaged in an informed brainstorming session and creativity/feasibility chart keeping our pain points in mind.
After our sessions, we selected three possible ideas then created the low-fi paper prototypes and tested them with our users.
Transportation App & Key Fob
SOLO is an application that comes with a corresponding key fob that has two modes: SOLO and DUO. By choosing SOLO, users can find a bicycle, scooter, or car that they can unlock and drive. By choosing DUO, users can call a rideshare service to pick them up.
Familiarity with other apps.
The phone is distracting.
Not for an emergency situation.
Can't select the route and driver.
This application shows other people nearby that are walking to a similar destination. Users can join trips and gain a group of walking buddies to accompany them to their destination.
Familiarity with location based apps.
Potential security concerns.
Not for an emergency situation.
Crime Data & Wearable
This application enables users to reach crime data in an area and according to that, it provides safe routes. Wearable technology guides them without distracting them.
No need to look at the phone.
Using existing data.
Potential privacy & security concerns.
An app and a wearable device that suggests safe routes according to crime data.
The wearable device guides users to the desired destination by using voice and haptic interaction.
Users will also be able to search for information about neighborhoods and join communities in order to receive that community's latest crime and news stories.
06. Iteration 1
For iteration-1, we created digital low-fi wireframes of our final idea and tested with our users by using think-a-loud and cognitive walk-through methods.
For the wearable, a participatory design session was conducted, and in light of the outcomes, the 3D model was created and tested with users.
The idea was tested by using low-fi wireframes. Cognitive walkthrough and think-a-loud methods used.
Knowing crime data and real-time news of neighborhoods.
Onboarding screens were helpful and fun.
Using existing data.
Needed more information about neighborhoods.
Neighborhood information and news in the routes view.
Several wordings should be revised.
Connection with the wearable should be communicated clearly.
Wearable Design & Testing
To test explore the suitable places and forms of the wearable we engaged in participatory design sessions with our users.
Fingers, wrists, and forearms are the best places to wear the device.
Users designed rings, bracelets, and earrings considering the context and features.
Different statutes and alerts of the wearable indicated by small lights rather than a detailed screen.
According to the outcomes of the session, we designed the model and 3D printed it to test the device.
We asked our users to perform tasks and we recorded any hesitations and confusions they expressed. At the end of the session, we administered the After Scenario Questionnaire and questions probing users about the comfort, understandability, and learnability of the wearable. Task completion success noted.
Red and yellow lights are easy to understand.
Turning motion is intuitive.
The battery indication is not clear.
Grasping is hard.
Users don't know the consequences of their actions
A tutorial is needed.
07. Iteration 2
For iteration-2, we revised our design according to the feedback received and created digital high-fi wireframes.
For the wearable, various shapes and designs modeled and the final design is selected. Haptic interaction tests conducted to test the feedback method.
By selecting the neighborhoods users can reach their real-time news, crime data, and the overall score.
Users can select qualities that are important for them to evaluate the safety of an area (ex. light, maintenance)
Users can import the contacts that they want to reach in an emergency situation.
Various information can be found on each neighborhood page such as overall score, street view, and news.
The majority of the "overall score" is composed of police reports and a minor percentage is provided from our user ratings.
The crime graphs designed to be easy to understand and under each table source is provided to increase the credibility
Real Time News
Users can see different stories of the neighborhoods.
Furthermore, emergency situations and ongoing instances' progress are provided to our users
Convoy provides multiple safe routes that users can choose from. Each route has different qualities and ratings considering the overall score of an area.
When convoy + is paired with the app it gives haptic feedback to users to navigate safely.