On-device activity recognition
Compact classifiers detect walking, running, cycling, swimming and strength sessions directly on the wearable, auto-starting workouts and logging them without needing a phone connection.
Edge AI & IoTAI for Wearables
AI in wearables that lasts all day.
We build AI in wearables that turns accelerometer, PPG and temperature signals into insights people actually use. Our models are compressed to run on tight power budgets, sync cleanly with companion apps and keep sensitive body data as private as possible.
- On-device inference
- Sensor fusion pipelines
- Battery-aware models
01 Overview
Turning noisy sensor streams into meaningful wearable features
Wearable products live or die on the quality of their signal processing. A ring, watch or patch collects motion, heart rate, skin temperature and sometimes ECG, but those signals are noisy, intermittent and shaped by how the device sits on the body. Our wearable AI development starts with the raw data: labeling sessions, cleaning artifacts and building models that hold up across skin tones, wrist sizes and activity levels.
Applying ai in wearables also means respecting hardware limits. We quantize and prune models to fit microcontrollers and low-power NPUs, decide which inference belongs on the device versus the phone versus the cloud, and design firmware update paths so models can improve after launch. The result is a feature set that feels instant, protects battery life and earns long-term daily wear.
02 Challenges
What AI solves in wearables.
The recurring problems we hear from wearables teams — and where AI makes the biggest difference.
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CH-01
Motion artifacts corrupt readings
Optical heart-rate and SpO2 signals degrade during movement or loose fit. Learned denoising and multi-sensor fusion recover cleaner estimates and mark low-confidence windows instead of reporting bad data.
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CH-02
Tight power and memory budgets
Small batteries and microcontrollers cannot run large models. Quantization, pruning and duty-cycled inference let useful AI run on-device for days between charges.
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CH-03
Generic models miss individual differences
Population-level thresholds misread athletes, older adults and people with darker skin. Personal baselines and fairness testing across diverse cohorts keep insights accurate for every wearer.
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CH-04
Data overload without meaning
Users see dozens of charts but rarely know what to do next. Generative summaries translate trends into short, specific guidance while avoiding medical claims the product cannot support.
03 Use cases
AI use cases for wearables.
Proven applications we design, build and run in production — each delivered through one of our core services.
Sleep staging from motion and PPG
Models estimate light, deep and REM sleep from wrist or finger sensors, then pair results with bedtime consistency trends so users understand what changed and why.
ML DevelopmentIrregular rhythm and anomaly alerts
Signal models flag readings that fall outside a wearer's baseline, prompt a confirmatory measurement and suggest consulting a clinician, with clear wording about what the device can and cannot detect.
Predictive AnalyticsVoice assistant on the wrist
Low-latency wake-word detection and speech recognition let users log notes, set timers and query their stats hands-free, with the wake word handled locally on the device.
Voice AI & SpeechPersonalized insight summaries
A language model turns weekly heart-rate variability, recovery and step trends into short, readable summaries inside the companion app, grounded strictly in the user's own data.
Generative AI DevelopmentFleet telemetry and model monitoring
Pipelines collect anonymized performance metrics from devices in the field, detect accuracy drift by firmware version or hardware revision and schedule over-the-air model updates.
MLOps Services04 Services
AI services for wearables.
Edge AI & IoT
On-device AI for cameras, sensors and embedded hardware.
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ML Development
Custom machine learning models for prediction and classification.
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Predictive Analytics
Forecasting, churn, demand and risk models.
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Data Engineering for AI
Pipelines, vector stores and feature stores that feed AI.
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Voice AI & Speech
Speech recognition, voice agents and text-to-speech.
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MLOps Services
CI/CD, monitoring and retraining pipelines for ML in production.
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05 How we deliver
From use case to production.
Every wearables AI project follows the same five stages, so you validate value early and scale with confidence.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
06 Built for your sector
Designed for wearables realities.
Data, regulation and the systems you already run shape every architecture decision we make.
Biometric data privacy
Body data is highly personal. We minimize what leaves the device, use explicit opt-in consent, support deletion requests and design data flows around GDPR, state consumer health data laws and HIPAA requirements where applicable.
Device, app and cloud integration
We work with BLE protocols, Apple HealthKit, Google Health Connect and your own sync services so models, firmware and companion apps share consistent data formats and versioning.
Hardware variation in the field
Sensor tolerances, firmware revisions and fit vary across units. We test models across hardware batches and real-world wear conditions, and build fallbacks for when signal quality drops.
Can AI models really run on a smartwatch or fitness ring?
Yes, when they are designed for it. We use compact architectures, int8 quantization and frameworks such as TensorFlow Lite Micro or vendor NPU toolchains so inference fits within kilobytes of memory and small power budgets. Heavier analysis can move to the phone or cloud when it does not need to be instant.
How do you handle sensor data quality from wearables?
We profile signal quality across activities and fit conditions, build artifact detection into the pipeline and train models to output confidence scores. When readings are unreliable, the product can skip, smooth or flag them rather than presenting misleading numbers to the user.
Is wearable health data covered by privacy regulations?
Often, yes. Depending on your market and claims, GDPR, state consumer health data laws and sometimes HIPAA can apply. We design consent flows, data minimization and retention policies around those requirements and recommend legal review for the specific claims your product makes.
What data do you need to build wearable AI features?
Ideally, raw sensor recordings paired with ground-truth labels, such as reference devices, annotated activity sessions or clinically measured values. If you lack labeled data, we can help design a collection study, recruit diverse participants through partners and build labeling tools to speed annotation.
Can you add generative AI insights to an existing wearable app?
Yes. We connect a language model to the metrics you already compute, constrain it to your data and approved wording, and add guardrails so it avoids diagnosis. Summaries can run in the cloud or, for some features, on newer phones with on-device language models.