Edge Impulse puts the power of ML into real products, across every industry.
Education and Research
Health
OURA: Health Monitoring
Sleep analysis, perfected.. Sourcing and processing health vitals for high-quality ML training.
Education and Research
Health
Wearables
Human interfaces
Slate Safety: Predicting Heat Exhaustion
A solution to keep first responders safe when they're most at risk, leveraging on-device real-time analytics.
Education and Research
Agriculture
AI-Managed Crops Irrigation
Maximize crops yield and minimize soil degradation and water waste using the Arduino Edge Control and Edge Impulse.
Education and Research
Environment
Infrastructure
Predictive maintenance
Remote Grid Monitoring
Meet RAM-1, the world’s smartest powerline monitoring system.
Education and Research
Health
Early Blindness & Diabetic Retinopathy Detection
A portable medical device that can accurately detect different stages of diabetic retinopathy, without the need for an internet connection, ample energy or computational power.
Education and Research
Appliances
Buildings
Industry
IoT Gets an ML Boost, From Edge to Cloud
Today, it’s easy to run Edge Impulse machine learning on any OS like the Linux-powered Raspberry Pi and on every cloud, including Microsoft’s Azure IoT.
Education and Research
Industry
AI-Automated Hard-Hat Detection
Enhancing health and safety in industrial environments with embedded machine learning.
Education and Research
Infrastructure
Predictive maintenance
Safer Roads, Powered by Machine Learning
Automatic wheel lug nut detection using machine learning at the edge.
Education and Research
Agriculture
ML-Powered Agricultural Robotics
Record crop trials and provide precise irrigation levels to quantify the best crop-growing conditions with Edge Impulse.
Education and Research
Asset tracking & monitoring
Smart Cold Chain
Gain real-time insights in the product lifecycle to ensure the product has not been damaged or compromised during shipment.
Education and Research
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Academia
Edge Impulse partnered with Harvard SEAS to coauthor a paper detailing how Edge Impulse provides solutions to many of the challenges faced by the edge and embedded machine learning communities, including research and classroom activities.
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