⚡ electrical engineering

Motion-resilient and rapid contactless heart rate monitoring by overcoming dual physical barriers

This paper presents a low-cost, single-channel 60 GHz radar system that achieves rapid and motion-resilient contactless heart rate monitoring by employing a novel maximum-energy-variance criterion and phase differencing techniques to overcome the dual challenges of respiration masking and the speed-resolution trade-off, demonstrating 95.1% accuracy across diverse motion scenarios within a 5-second window.

Original authors: Zengdi Bao, Ningning Wu, Jiahao Zhu, Zhenhao Yang, Yang Li, C'heng Hu, Yanhua Wang

Published 2026-06-30

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CC BY 4.0

Original authors: Zengdi Bao, Ningning Wu, Jiahao Zhu, Zhenhao Yang, Yang Li, C'heng Hu, Yanhua Wang

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to hear a tiny, delicate whisper (a heartbeat) in a room where a loud, rhythmic drum is being beaten (breathing), and someone is also shuffling their feet and swaying around (body movement). This is the exact challenge the researchers at the Beijing Institute of Technology faced when trying to build a radar system that can measure your heart rate without touching you.

Here is how they solved this problem, explained simply:

The Two Big Hurdles

The paper identifies two main "physical barriers" that usually stop this technology from working well:

The "Loud Drum" Problem: When a radar bounces off a person, the signal from their breathing is huge and loud. The signal from the heartbeat is tiny and weak. It's like trying to hear a mosquito buzzing while a jet engine is running next to it. Usually, the radar just gets confused and locks onto the loud breathing, ignoring the heartbeat.
The "Speed vs. Clarity" Problem: To hear a specific note clearly, you usually need to listen for a long time. But if you listen for too long, the person might move, and the sound gets ruined. If you listen for a split second to catch them before they move, the sound is too fuzzy to identify the note.

The Solution: A Three-Step "Magic Trick"

The team created a system using a standard, off-the-shelf 60 GHz radar (the kind you might find in a car or a smart home device) that overcomes these hurdles using clever math instead of expensive hardware.

Step 1: The "Freeze-Frame" Camera (Finding the Right Signal)

Instead of listening for a long time, the system looks at the person in tiny, split-second snapshots (less than one second).

The Analogy: Imagine taking a photo of a dancer. If you take a photo too slowly, the dancer's whole body blurs. But if you take a photo with a super-fast shutter speed, the dancer's body is frozen, but their heart is still beating fast enough to create a tiny, sharp "jitter" in the image.
How it works: The researchers realized that while breathing is smooth and slow, the heartbeat creates sharp, tiny mechanical "jitters" on the chest. Even in a tiny fraction of a second, the heartbeat's "jitter" creates more energy variance (chaos) than the smooth breathing.
The Result: The system uses a "Maximum-Energy-Variance" rule. It scans the radar data and says, "Ignore the smooth, loud breathing; find the spot with the most tiny, sharp jitters." This allows it to pick out the heartbeat signal even while the person is moving.

Step 2: The "Scissors and Glue" Technique (Fixing the Blur)

Now that they found the heartbeat in those tiny snapshots, they have a new problem: the snapshots are too short to hear the rhythm clearly.

The Analogy: Imagine you have 100 tiny, blurry 1-second video clips of a song. You can't hear the melody in just one clip. But, if you cut out the "noise" (the breathing and the shuffling feet) from each clip and then tape them all together in a row, you suddenly have a long, clear 5-second song.
How it works: The system takes those tiny, motion-free snapshots, removes the low-frequency "drum beat" of the breathing, and stitches them together into a longer 5-second signal. This gives the computer enough data to calculate the exact heart rate without the person having to stand perfectly still.

Step 3: The "Super-Listener" (The MUSIC Algorithm)

Finally, to make sure they don't mix up the heartbeat with any remaining background noise, they use a special math tool called the MUSIC algorithm.

The Analogy: A standard radio tuner (like an FFT) is like a person trying to find a station in a crowded room; they might hear two stations blending together. The MUSIC algorithm is like a super-listener who can separate two people talking right next to each other, even if one is whispering.
The Result: It isolates the heartbeat frequency with extreme precision, even when the breathing signal is much stronger.

The Results

The team tested this on 10 people in 12 different scenarios. They sat still, they swayed side-to-side, they lay down, and they changed angles.

The Outcome: The system achieved 95.1% accuracy in just 5 seconds of observation.
The Takeaway: They proved you don't need expensive, custom-built hardware to measure heart rates remotely. By shifting the "heavy lifting" from the hardware to the software (algorithms), they made a cheap, standard radar capable of tracking a heart rate even when the person is moving around.

In short, they taught a radar how to ignore the "loud drum" of breathing and the "shuffling feet" of movement to focus entirely on the "tiny whisper" of the heartbeat, all by taking super-fast snapshots and stitching them together.