Orbit

VISUAL DESIGN, DESIGN SYSTEMS, UX DESIGN, PHYSICAL PROTOTYPING, MULTIMODAL DESIGN, WEARABLES, MOBILE APP DESIGN

Orbit is a speculative wearable device, synced with your mobile phone, that detects AI-generated voice scams and alerts users and their trusted networks.

My role: Lead product designer, researcher, product manager

Timeline: 10 weeks

Tools used: Figma, Arduino, Photoshop

What is Orbit?

Orbit combats AI voice cloning scams by detecting voice anomalies, flagging suspicious activity, and alerting trusted contacts. It ensures digital security with customizable settings and incident reviews.

  • Phishing scam detection wearable device that syncs with mobile phones

  • AI-powered using a model such as Ircam Amplify’s AI Speech Detector Model

  • Target users: Retirees and their families, particularly adult children

Designing for the Near Future

Context and Assumptions

Our assumptions about the nature of AI and of phishing scammers’ technology puts us, in our estimation, about 5-10 years into the future. These types of AI and phishing scam technologies don’t exist yet, but we think they could in the relatively near future.

  • We expect that AI will evolve to the point where scammers can use it to fake phone numbers or even display names and video calls, making scams harder to detect at a glance.

  • We assume that we will be able to integrate an existing AI model, such as Ircam Amplify’s AI Speech Detector Model, in order to detect voice cloning scams.

Research & Insights

Primary Research

My role in this research phase was to lead our user testing sessions with the younger generation, and I also led one of our generative IDIs with our older generation participants.

Goals

  • Understand how both older and younger adults navigate technology to detect phishing scams, addressing a growing concern in cybersecurity

Key Insights

Target Users

  • Younger adults (millennials): Does the user understand our design concept and does it address major pain points? How do younger generations interact with it?

  • Adults 65 and up: Iterated design based on feedback from the younger generation. How do older generations interact with it?

Building Trust Through Familiarity

Scammers leverage the principle of “familiarity” to establish a sense of trust, making their scams appear more credible and convincing to the victim.

Creating Panic Through Urgency

Scammers exploit “urgency” to create a sense of panic, pressuring victims to act quickly without questioning the legitimacy of the situation.

Exploiting Low Tech Literacy

Scammers take advantage of lower tech literacy to confuse and mislead victims, making it harder for them to recognize and avoid scams.

Methods

  • 7 semi-structured in-depth interviews

  • Social media polling

Concept Development & Ideation

During the concept development phase, I led our group through a series of brainstorming and sketching exercises to generate and explore a wide range of ideas.

I helped facilitate decision-making and guided the team in narrowing our concepts down to three key directions, which we then presented for feedback. This set us up to identify our core design challenge.

Design Challenge

How might we leverage trust within families, across varied levels of tech literacy, to create a shared learning experience that helps everyone recognize and avoid AI voice cloning phishing scams?

Building the Prototype

The key flows we wanted to implement in our prototype were:

  • Detecting AI voice cloning scams

  • Sharing scam information with family

  • Reviewing scam details and checking in with family

I built the physical shell for our device, while others on my team programmed the LED functioning.

I also wrote the user testing protocol for our next round of research, in preparation for our visit to a retirement community to interview folks and get feedback on our prototype.

We built a couple versions of a testable prototype: a bracelet with 3 different LED colors that signify whether or not an AI scam is present.

  • Scanning call in progress = yellow

  • Safe call = green

  • AI detected = red + vibration

Evaluative Research & Insights

“I might have trouble distinguishing physical [haptic] signals. Whereas the lights are are very easy.” -P6

What did we learn?

“I could have really gotten caught up in that if that was an AI message and it was one of my kids, [or] grandkids” -P6

Users want to understand how to report scams to the proper authorities.

A key element of feeling protected against scams includes open communication and sharing information with family and friends.

Wearing a smart bracelet helps users detect scams even when they aren’t looking at their phone.

The key flows we implemented in our prototype were:

  • Detecting AI voice cloning scams

  • Sharing scam information with family

  • Reviewing scam details and checking in with family

We completed multiple sets of user testing sessions, 4 of them with younger adults, and after adjusting our prototype based on feedback from those tests, we tested the updated version with 6 adults over the age of 65.

“I'm not into tech very much, but I think I could handle this without a problem.” -P10

Final Design

A Multimodal Experience

To make scam detection intuitive and non-intrusive, we designed a multi-touchpoint system that included a wearable bracelet, mobile interfaces, and family notifications.

Scam Review in the Mobile App

When the bracelet detects suspicious activity, the user is guided to a review screen on their phone. The interface is designed for clarity and quick comprehension:

  • Contextual summary of the suspicious activity

  • Clear “Proceed” or “Report” options

  • Accessible language tailored for older adults

Visual Design

Throughout prototyping and testing, the design system matured to meet both functional and emotional needs. I developed the brand identity, visual templates, and presentation materials, all of which evolved alongside user feedback.

  • Polished, user-friendly interfaces designed with accessibility in mind

  • A cohesive visual narrative used for stakeholder presentations

  • Bracelet prototypes that evolved based on user feedback and testing

Color-Coded Alerts

The wearable bracelet communicates scam risk using an easy-to-understand color system:

Yellow: Potential risk detected. User is prompted to slow down and review.

Green: Safe! No action needed.

Red: High probability of an AI scam. Alerts are triggered, and a review is prompted.

Alert Family Members

If a high-risk interaction is detected, a designated family member receives a real-time notification with options to follow up or offer support. For example, if Sam encounters a phishing call, his son receives an alert with suggested next steps.

Reflection & Next Steps

Successes

  • Cross-generational testing helped us collect a range of opinions on our device

  • Physical prototype engagement helped us conduct effective user tests

  • Our excellent group dynamic allowed for effective team collaboration

Challenges

  • Our ability to implement this tech right now is limited by existing AI capabilities, and by the fact that we have to speculate about future states of phishing scams

  • The success of this device relies heavily on healthy relationships and communication between family members

Looking ahead

  • Broaden scam detection beyond voice cloning; we’re curious to explore how our product can evolve and address broader challenges in the future

  • Evaluate haptic feedback and incident reporting in more depth

Outcomes

  • The team received positive feedback from industry professionals at our final showcase exhibition, where we demo-ed our prototype

  • The project allowed me to further develop my end-to-end design skills, leadership, and visual storytelling

  • Our team won an award for excellent digital fabrication skills!