Synaptics

My first professional experience in UX was at Synaptics, a hardware company in San Jose that designs displays, touch, and biometric components integrated into PCs and mobile devices. I was the usability intern in the Biometric Division, working under two researchers.

My managers came from two different backgrounds — one in engineering, the other in biomechanics — and together introduced me to the facets of usability in hardware and tangible products. Working with them highlighted the unique challenges of building hardware, where the physical demands of both the electronics and the human body limit how interaction can happen.

Rather than setting aside specific projects, my managers let me contribute to whatever work came their way. Until this internship I had only known multi-year academic research in cognitive science, hunting for statistically significant trends; the studies here ran a few weeks each and focused on finding flaws in prototypes or preferences about a technology. By the end of the summer I was pleasantly surprised to have completed four major studies.

01 Facial Recognition as Secondary Authentication

My first study was a competitive analysis between several facial-recognition apps, focused on the efficiency of the technology and its use as a form of security.

Methods

We recorded the time required for enrollment and verification under indoor and outdoor lighting from four angles. Enrollment creates the original image stored as the "key" that each later verification is compared against; some apps had users rotate and make expressions to capture nuance. After enrolling, subjects tried to verify their faces while we recorded each app's accuracy and processing time, rotating to change the lighting angle and repeating under sunnier conditions. Subjects then ranked the four apps by preference, and I ran a short interview protocol on how they felt about the effectiveness and security of facial recognition.

Results

Many subjects came into the lab confident about facial recognition, but left dissuaded after seeing it in action.

All four apps struggled in outdoor lighting and even rejected some subjects during verification. Subjects reasoned that if the apps couldn't handle bright light, they'd struggle even more with changes to hair, makeup, glasses, or darker settings. Many also felt enrollment wasn't thorough enough, which fed concerns about security — they concluded facial recognition was best used as a secondary form of authentication, not the only measure for sensitive apps like banking or email.

02 Trackpad Competitive Analysis

The next study compared Synaptics' trackpad products against a competitor's, using three laptops — two with Synaptics trackpads and one with the competition. (Results stay out of this writeup, as they're specific to Synaptics products.)

Methods

Subjects sat at each laptop in randomized order and completed tasks testing three interaction types:

• Single-point contact — clicking a target point, testing precision on each trackpad
• Zooming / pinching — two-finger expand and pinch gestures, testing how well each trackpad processed simultaneous contacts
• Typing — typing a passage, testing how well each trackpad rejected stray contact from the bases of the thumbs near the space bar

03 Fingerprint Sensor Integration Patent granted

My last two studies focused on new Synaptics products in clients' devices, both addressing the usability of integrating fingerprint sensors beneath the glass of trackpads and phones. Invisible sensors give back real estate for larger UIs, but introduce a new challenge: users have to interact with something they can't see or feel. These studies looked for new ways to enable that interaction. I built the experimental UIs with JavaScript, HTML, CSS, and Android Studio.

Study one · SecurePad (HP)

The first study addressed Synaptics' SecurePad, a trackpad with an integrated fingerprint sensor. HP was using SecurePads in its laptops and wanted to embed additional LEDs that light and flicker to indicate the sensor's location and authentication status. We worked with a team there to test the most user-friendly organization and behavior for these LEDs.

Study two · Galaxy S8 (Samsung)

The second study focused on Samsung's then-upcoming Galaxy S8, and is part of work wrapped up in the IP process at Synaptics. With my managers I helped design the experimental protocol and the scripts for data analysis. We investigated how the UI visuals shown during fingerprint enrollment affected the types of contact users made — effective enrollment maximizes the finger area exposed to the sensor, which gets harder when the sensor is invisible. The goal was to identify visual cues that elicit contact from different regions of the finger. To model the contact subjects made, I used Python's SciPy library.