The headband reads brain activity while the user sits calm. The app has to turn that signal into meditation practices worth trying - in real time, and without lag.- Our reading of the brief handed to Milleniance at the start of the engagement
The headband measures the electrical activity of the brain while the user sits calm, and sends it to a phone over Bluetooth Low Energy. The app then has to recommend meditation practices that move the user toward a desired mental state. Because this is a real-time feedback product, delay is not a detail - sending every reading to the cloud and back could be a deal breaker.
The signal-processing logic also existed as Python scripts, which had to become native Swift and Java code. Around that core sat the usual weight of a consumer product: user accounts, content, device management, an admin portal - and a pre-order shop that needed to launch alongside it.
The first task was a requirements document drawn from the hardware's components and interfaces. From there the design was divided into subsystems, treated as features, with the aim of keeping interdependencies between them as small as possible. That split makes it clear early which skills the build needs, what the software architecture looks like and where the bottlenecks are likely to be.
Two questions had to be settled before the rest: where signal processing should run - on the device or in the cloud - and which cloud provider to use. The team profile followed from the answers: native Swift and Java, on-device machine-learning kits such as Core ML and TensorFlow Lite, and a working grasp of filtering, FFT and spectrography.
"If your product's value depends on a signal being interpreted in real time, decide where that processing lives before you design a single screen."
No sales deck, no discovery call with someone who hands you off afterwards. We look at your device, your data path and where the processing needs to happen. We respond within one business day.
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