Connected Home / MFA Thesis
SENSORY
A smart home system that sees heat, not pixels - designed around the people most homes forget.
Most smart-home products were designed to make routine tasks easier for healthy, able-bodied adults. I started from the opposite edge: an older person living alone, a blind resident trying to locate an object, and a wheelchair user navigating a changing home.
SENSORY is a camera-light home system built around thermal heat maps. A distributed sensor network reads presence, movement, and unusual heat without turning the home into a collection of recorded faces. The same signal can support accessibility, appliance safety, health awareness, and security.
This was my MFA thesis at the University of Illinois Urbana-Champaign. I owned the research, system architecture, industrial design, physical prototype, UI, icon system, and visual identity.

01 / the insight
The smartest sensor is the one that does not need a face.
The concept began with a simple distinction: occupancy data does not have to be surveillance data. Thermal sensing can describe where warmth is changing without creating a recognizable portrait of the person creating it.
Research from Aalto University showed that people consistently associate different emotions with different patterns of bodily sensation. I treated that work as an inspiration, not a diagnostic claim. It opened a larger design question: what could a home understand from heat, and where should it deliberately stop?
That boundary shaped the architecture. Thermal input could prompt a check-in, surface an appliance anomaly, or reveal a fall-like change in posture. It could not label a resident's emotion as fact or replace a medical professional.



A heat map does not need to identify a person to make a home more responsive.
02 / system design
One signal source, seven jobs, and one shared mental model.
The hard part was not inventing more features. It was proving that each capability belonged to the same system. I mapped the home as a continuous thermal environment, then organized actions around four human needs: understand, assist, protect, and respond.
The platform covered room occupancy, appliance heat, safety alerts, health-oriented check-ins, accessibility assistance, security anomalies, and environmental controls. Each feature used the same floor-plan model so users did not have to relearn the house every time they changed tasks.
- 01Sense locally
- 02Map the room
- 03Explain the change
- 04Offer an action


03 / industrial design
The form follows the person who needs it most.
I explored a folding display, a compact round control, a solar-powered panel, and a wall-mounted screen paired with detachable sensors. The final direction separated sensing from control: small modules could be placed where coverage mattered, while the larger display stayed at a reachable, predictable location.
A folding hinge added failure points. A small round screen hid too much information. Solar charging made a safety system weather-dependent. The wall-mounted panel offered the clearest tradeoff between reach, legibility, repairability, and room-scale context.



04 / interface
A dashboard for every body in the room.
The UI uses the floor plan as its anchor. Rooms, people, appliances, and alerts all resolve to a location instead of becoming separate app destinations. That reduces navigation and makes unusual changes easier to interpret.
Accessibility is a primary mode, not a settings appendix. Large touch targets, direct emergency access, navigation assistance, and seated-use reach were considered in the first layout. Thermal views stay visible as evidence, while plain-language labels explain what the system believes is happening.



A four-generation home does not need four apps. It needs one system that respects every body.
05 / reflection
What I would rebuild with today's AI.
The original concept separated health, safety, accessibility, and environment into different feature lines. Today I would use a shared contextual model, but keep the dashboard as a transparency layer: what was sensed, what changed, what is uncertain, and what action is proposed.
I would also move more inference on-device, reduce retained data, and give every resident a visible privacy state. Better models make the concept more feasible, but they do not change the design principle: the most vulnerable resident is the primary user, not an edge case.




AI can make the system more capable. It cannot decide whose needs matter most.