Performance Software
On-Device Machine Learning
On-device machine learning runs trained models directly on the wearable that captured the data, interpreting movement where it is sensed so raw data never has to leave the device.
Overview
Trained models that interpret movement directly on the wearable that captured it, so raw data never has to leave the device. This protects privacy by default and keeps working without a network, at the price of fitting a model into kilobytes through quantization and pruning. It is a constraint that reshapes how movement models are designed from the start.
This profile is a starting point and will grow with technical detail, validation notes, and integration specifics. For now it summarizes what On-Device Machine Learning captures and how it connects, and points to related development topics, hardware, and platforms so you can place it within the wider landscape of movement technology.
What it captures
On-Device Machine Learning is typically a embedded inference that captures recognized activities and on device metrics. Its accuracy depends on placement, conditions, and how the raw signal is filtered and modeled before it reaches a usable metric, and it is best validated against a trusted reference under the conditions in which it will actually be used.
As with any measurement technology, the clean number it reports is the end of a chain of sensing, refinement, and interpretation. Reading that chain, knowing what was discarded and where accuracy holds or degrades, is part of using the technology well rather than being misled by a precise looking figure.
How it connects
Data generally leaves the technology over runs locally, syncs summaries when connected, and it commonly runs on or alongside Wearable and embedded hardware. Integration is results surfaced through device apis, which shapes how readily its data can be combined with other streams in a larger system.
Maturity and use
In terms of maturity this class of technology is advancing with dedicated inference chips. This material is informational only, describing general characteristics rather than endorsing any specific product, and details such as accuracy, connectivity, and supported standards can change as firmware and hardware evolve.
