Prevent Heat Exhaustion with an Edge ML-Powered Wearable Device

Case Study Overview

Cloud-based systems that monitor vital signs can keep first responders and industrial workers from getting injured or even dying on the job. But what happens when people “go dark,” and can’t be reached by the cloud? SlateSafety wondered if their award-winning BioTrac Band could deliver real-time alerts even without connectivity. In just 10 days, working with the vast amount of biometric data generated from the field, SlateSafety and the Edge Impulse team were able to zero in on key parameters and create an accurate, compact algorithm for predicting heat exhaustion. The new algorithm, developed on Edge Impulse's industry-leading edge ML development platform to run directly on existing hardware, can give the wearer real-time feedback on the risk of heat exhaustion, and enhances existing products by making safety at the edge a working reality.

In this case study, read about the results:

• An accurate and efficient ML algorithm, ready to run on SlateSafety’s existing Nordic-based device
• A 10-day development timeline, enabled by Edge Impulse’s Solutions Support Team and Edge ML platform
• Real-time situational awareness, to avoid overexertion injuries and fatalities, even in areas without connectivity