Emerging Trends 18 | SunMatrix Ramble

Sen.se: Mother and the Motion Cookies

Sensors are showing up everywhere, from wrist wearables like Jawbone UP and Fitbit to the first wave of “smart home” kits. The promise is always the same. Data that helps you understand your day, then nudges you when something matters.

Mother and the Motion Cookies, from connected-objects startup Sen.se, is positioned as a more flexible take on that idea. Instead of buying a single-purpose gadget for each habit, you get one “Mother” hub and a set of small sensor tags. The Motion Cookies. You decide what you want to track, attach a Cookie to the relevant object, and set alerts for the moments you care about.

Definition tightening: A Motion Cookie is a small sensor you can stick to an object. The “Mother” device is the home base that receives the signals and turns them into simple dashboards and notifications.

If you strip away the friendly character design, this is a configurable rules engine for everyday life. The sensors stay the same. The meaning changes based on what you attach them to and what you tell the app to watch for.

Watch the demo video for more.

A sensor kit that behaves like a toolkit

The smart move here is that the hardware is deliberately generic. One sensor type can be repurposed across dozens of “jobs”, depending on where you place it. Toothbrush, medication box, door, bag, water bottle. The product is less about owning the perfect device, and more about reassigning the same device as your priorities change.

In consumer IoT, products only survive if setup friction stays low and the data translates into a simple action.

Why the “Mother” framing makes the tech feel usable

Smart home products often fail at the handoff between capability and comprehension. Mother softens that gap by packaging sensing as caregiving. The real question is whether a sensor system can feel understandable enough that people actually try it. That emotional framing reduces the intimidation factor and makes experimentation feel normal.

Extractable takeaway: When your product is technically broad, give users a friendly mental model and a small first win, then let reconfiguration become the habit that unlocks the long tail of use cases.

What connected-product teams should copy

Design for reassignment, not perfection. People’s routines change. Your hardware should survive those changes.
Make “setup” the product. If a user cannot get to value in minutes, they will not get to value at all.
Translate sensing into verbs. “Brush”, “open”, “arrive”, “drink”, “take”. Verbs beat metrics.
Alert sparingly. The fastest way to kill trust is to spam people with “insights” they did not ask for.

A few fast answers before you act

What is Mother and the Motion Cookies?

It is a smart home kit with one central hub and multiple small sensor tags. You attach a sensor to an object, choose what you want to track, and get updates or alerts based on that behaviour.

What is the core idea compared to a single-purpose wearable?

Reconfigurability. The same sensors can be reassigned to different objects and routines, so the system adapts to what you want to measure this week, not what the device designer assumed forever.

What problem is it trying to solve?

Turning ambient behaviour into something actionable, without requiring you to buy a new gadget for every habit or household scenario.

Why does the “Mother” framing matter?

It makes a technically broad sensor system feel more understandable and less intimidating. That framing helps users see the product as practical support, not just instrumentation.

What makes this kind of product hard to sustain?

Reliance on companion apps and backend services, plus the challenge of keeping alerts useful rather than noisy. If the system becomes high-maintenance, it stops feeling like help.

Audi TT: Brochure Hack

When print becomes interface, not paper

The best product storytelling often happens before the test drive, when you can turn curiosity into comprehension with a tangible experience.

Transforming classic print surfaces into smart touch based surfaces through special conductive ink and sensors has been around for a while now. But I still thought the below execution from Audi was worth a mention, as it was a step forward for the technology since it surfaced in 2014.

The Audi TT was the first Audi model to feature a revolutionary Virtual Cockpit. So to inspire customers, Razorfish Germany used conductive ink, haptic feedback, Bluetooth and a custom smartphone app to transform the car’s conventional sales brochure into a smart surface which gave viewers an augmented touch based journey through the car’s Virtual Cockpit. Here, a “smart surface” means a printed object that detects touch and triggers connected digital content.

How the brochure hack worked as a connected demo

The mechanism was a touch-sensitive brochure plus a companion device.

Conductive ink turned parts of the brochure into touch-sensitive zones. The smartphone app acted as the compute layer and controller, meaning it handled the logic and displayed the content. Bluetooth linked the physical brochure to digital content, while haptic feedback reinforced the feeling that the brochure itself had become an interface.

Instead of reading about Virtual Cockpit, viewers navigated it through touch, guided by the brochure’s layout.

In European automotive showrooms and launch events, touch-sensitive print works best when it helps prospects understand a digital feature before they ever sit in the car.

Why touch and haptics made the feature feel real

Virtual Cockpit is a feature that can sound abstract on a spec sheet. Because viewers could explore it through touch and get immediate confirmation via haptics, the interaction reduced cognitive load and made the capability feel tangible. The real question is whether your pre-drive touchpoint helps people experience the feature, not just read about it.

Extractable takeaway: When a feature is hard to picture, pair a simple physical interaction with immediate feedback so people learn by doing instead of decoding specs.

The business intent behind turning a brochure into product experience

The intent was to make a new interface feature memorable and easy to understand before a customer ever sat in the car.

This is a strong pattern when the feature is hard to visualize and the physical object can reliably deliver the same guided interaction every time. It also reframed the brochure from disposable collateral into a premium object that people were more likely to keep, show, and talk about, extending attention time well beyond the first touchpoint.

Steal this for your next touch-enabled brochure demo

Use print to trigger behavior, not just convey information. If the product story is interactive, the media should be too.
Make the first interaction obvious. Touch zones should feel discoverable without instructions.
Pair physical feedback with digital content. Haptics and responsiveness make the experience feel credible.
Choose touch-enabled print when it explains something complex. The tech earns its cost when it makes a hard-to-grasp feature instantly understandable.

A few fast answers before you act

What was the Audi TT “Brochure Hack”?

It was a printed sales brochure turned into a touch-sensitive interface, designed to help people explore the Audi TT’s Virtual Cockpit concept through guided interaction.

How did the brochure become interactive?

Conductive ink and sensors created touch zones in the brochure. A custom smartphone app provided the compute layer and content. Bluetooth connected brochure and phone, and haptic feedback helped the interaction feel physical and “real”.

Why is “smart print” useful for explaining a feature like Virtual Cockpit?

Because the feature can feel abstract on a spec sheet. Touch interaction reduces cognitive load and makes the experience feel like discovery instead of marketing.

What is the key mechanism in one line?

Physical touch triggers digital explanation. The brochure guides the hand, the phone delivers the content, and feedback confirms the interaction.

What did this change versus a normal brochure or video?

It increased time spent and comprehension by turning passive reading into guided exploration, which is especially valuable in high-consideration categories like automotive.

When does this pattern make business sense?

It makes sense when you need to simplify a complex product story early in the funnel, and when the physical object is meant to be kept, shown, and revisited rather than discarded.

Microsoft: Big Data to Predict Traffic Jams

Big Data is increasingly being used to find solutions to problems around the world. In this latest example, Microsoft partnered with the Federal University of Minas Gerais, one of Brazil’s largest universities, to undertake research that helps predict traffic jams up to an hour in advance.

With access to traffic data, including historical numbers where available, road cameras, Bing traffic maps, and drivers’ social networks, Microsoft and the research team set out to establish patterns that help foresee traffic jams 15 to 60 minutes before they happen.

What “big data” means in this context

Here, “big data” is not a buzzword. It means combining multiple high-volume signals that each describe traffic from a different angle. Flow and speed data. Camera feeds. Map-layer congestion indicators. And sometimes social or incident signals that explain why conditions change.

How the prediction model is positioned

The mechanism is short-horizon forecasting. Aggregate live and historical traffic conditions. Detect repeating patterns and transitions. Then output a probability that a segment will shift from free-flowing to congested within the next 15 to 60 minutes. The goal is not perfect certainty. It is an early warning that is useful enough to reroute, rebalance signals, or advise drivers.

In urban mobility programs, 15 to 60 minute congestion prediction is a practical layer between raw telemetry and real-world operational decisions.

Why it lands

This works because it targets a time window people actually feel. Short-horizon forecasting matters because it aligns the prediction with the moment when routes, signals, and departures can still change. The real question is whether earlier warning is reliable enough to trigger better decisions before congestion locks in. Useful prediction beats perfect prediction in operational systems.

Extractable takeaway: When a prediction is delivered inside the decision window, it creates value even if it is not perfect. The win is earlier choices, not flawless foresight.

What to steal for traffic prediction

Design for actionability: pick a forecast horizon that matches real decisions, not academic elegance.
Blend signals carefully: combine steady signals, like flow data, with explanatory signals, like incidents or events, when available.
Communicate confidence: a probability and a time window often beats a single definitive “will happen” claim.
Validate across cities: portability matters, because traffic behaviors vary by road network and culture.
Measure the right outcome: accuracy matters, but reduced delay and better routing outcomes are the real business KPIs.

A few fast answers before you act

What is Microsoft trying to do here?

The project aims to predict traffic jams 15 to 60 minutes ahead by combining traffic flow data, map signals, cameras, and other contextual inputs to spot patterns before congestion forms.

Why is 15 to 60 minutes the useful range?

It is long enough to change routes, adjust signal timing, or delay a departure. It is short enough that conditions have not completely changed since the forecast was generated.

What data sources matter most?

Traffic flow and speed data usually provide the core signal. Cameras, incidents, events, and social signals can add context that improves timing and explains sudden changes.

What does “80% accuracy” actually mean?

It is typically reported as the share of correct predictions under a defined test setup. The real value depends on how accuracy is measured, what baseline is used, and how the prediction is turned into driver or city actions.

Where does this approach fit in a smart-city stack?

It sits between sensing and intervention. Sensors and maps detect current conditions. Prediction estimates near-future conditions. Then routing, signaling, and traveler information systems act on that forecast.