The Next Byte Podcast · 14 Jan 2025 · 16m
The hosts discuss a Penn State research team that developed a wearable blood pressure monitor inspired by a 3,000-year-old Traditional Chinese Medicine pulse-checking method. They explain how the team used a 3D-printed array of three pressure sensors at different depths on the radial artery, combined with machine learning, to infer systolic and diastolic blood pressure more accurately than current photoplethysmography (PPG) sensors. The conversation also covers the broader context of wearable battery technology, specifically the potential of solid-state batteries to improve device form factors, and highlights BYD's growing presence in the EV market.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Engineering & simulation
A machine learning algorithm processes a 3D pulse map from pressure sensors to infer systolic, diastolic, and average blood pressure values.
“they add a layer on top of that, which with a machine learning algorithm. Using this pulse map, this 30 map of how blood is pulsing through this artery over time, they're able to infer the blood pressure.”
Speaker 1 · Hear it at 7:49
early-stage companies
Other shows on the same ground, closest first.
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Dr. Gregory Charvat, CTO of Humatics Corporation, discusses his three startups: Butterfly Network, Hyperfine, and Humatics. He details the pivot at Butterfly Network from high-cost ultrasound tomography to a handheld ultrasound on a chip using MEMS technology. He also describes Humatics' development of a short-range, high-precision indoor GPS system for factory automation, which achieved centimeter and millimeter accuracy through time-of-flight measurements.
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