Craig Ulmer

Remotely Monitoring Human Vital Signs

1996-02-07 dsp

In early 1996 the Wall Street Journal did a piece on a class project that I'd worked on with four other students. They didn't name us, but it was pretty fun to be 23 and talking to a journalist from the WSJ:

The project was for a senior-level Real-Time DSP class I took in the Fall. My project team worked on modernizing a system that GTRI had developed to detect a person's heartbeat and respiration rate using a radar device. It was a fun project that highlighted how signal processing is often an art as much as it is a science.

An Idea for the Olympics

I worked for Dr. Schafer and Dr. McClellan as an undergrad and developed a few Matlab programs they could use in lab exercises for the intro to discrete signal process (DSP) class. One day after meeting with Dr. Schafer he told me that there was a unique opportunity coming up that I could be a part of if I signed up for a senior-level DSP class that was being offered in the Fall. The 1996 Summer Olympics in Atlanta were only a half year away, and the whole city was buzzing with Olympic thoughts. One of the DSP professors saw a news story about how world-class archer are so focused on stability when they're aiming that they time the release of an arrow with their heartbeat so that their aim isn't disturbed by the heart's movements. The DSP professors thought it would be interesting to see if we could rig up a device that could monitor an archer's heartbeat to test out the story, and do so without attaching anything to the archer that would disturb him or her.

As it turned out, someone at GTRI had previously built an analog system for detecting human vital signs as part of a military contract. The idea was that the device would be attached to a military truck so that soldiers could drive around a battle field, point the device at bodies in the field, and use it to detect which people were alive and could be rescued. The GTRI device was completely analog and used a radar device to provide the input signal to an analog filter that looked for a heartbeat signature in the frequency domain. The GTRI people told us the system had been successful enough to fulfill the contact, but hadn't made it out of the prototype stage for a number of reasons (size, analog reliability, and latency). They were happy to let us use the radar section of the project though, provided we worked with them if anything came out of the project.

Class Project

I signed up for the real-time DSP class and met four other students that were picked to work on the vital signs project. The class covered a number of practical issues to building real-time systems using current (TI) DSP chips. The professor was a founder of a company that built DSP products for others, and loaned each project team an EISA DSP card and software that could be used to implement the projects. In addition to having good hardware (modern TI DSP, memory, ADC, and DAC), the board came with several software examples that demonstrated how to continuously move data from ADC to DSP to DAC. As someone who was used to waiting on Matlab, it was thrilling to watch the board do things in real time. Testing was also interesting. The way you debugged an FFT was by hooking up a frequency generator to the input and an oscilloscope to the output. Seeing the stem move around as you adjusted the frequency was pretty exciting.

The project team started off by going over to GTRI and recording some initial radar data. We had one of our guys sit in a chair and breathe heavily for thirty seconds while the rest of aimed a radar horn at him and collected data on a PC. The team split up into two teams. The analysis team worked through the signal processing and dug around the data in Matlab. The implementation team focused on practical concerns of getting algorithms running on the hardware. I worked on the implementation team, as the hardware was the greater unknown. While there was a lot of TI reference code we could use, it was all in assembly and took a good bit of tinkering to glue together correctly.

It was difficult for us to detect respiration (chest moving in and out) and a heartbeat because the physical movements were small (precision issues) and at a low frequency (latency issues). A big part of our work involved a massive amount of (filtered) downsampling to get to the frequencies we needed. It seemed pretty crazy to me that we were recursively gathering thousands of samples only to distill it down to a singe value.

Mixed Results

The signal processing team struggled to find meaning in the data we captured, even after we went back and made additional runs. Respiration could be seen, but signals in the heartbeat frequencies were extremely faint. I believe the analysis team did have some better luck when they focused on harmonics, but none of us felt that our initial signal had enough fidelity for us to feel confident in the result. The implementation team got a design together that did all the downsampling and detection in real time. However, we were all pretty disappointed that we couldn't find a smoking gun.

Even though the results weren't what we wanted, the project was a good experience for me because we took an idea and built a practical implementation in a limited amount of time using the resource we had available. I enjoyed getting my hands dirty with real hardware, even though it meant wrestling with a lot of low-level software that wasn't in my scope. A few years after we did this work, someone else from Georgia Tech picked up the work and finished the effort. In retrospect it would have made a nice starting point for a masters thesis, but by the Olympics I was doing more in CompE than DSP at school.