Tag: cycling

As a geek, the added bonus of exercise is the fun that you can have with the data you’ve generated. A recent conversation on Twitter about the accuracy of wrist-based HRMs got me thinking… how does a wrist-based HRM compare with a traditional chest-strap HRM? Conventional wisdom says that the chest-strap is more accurate, but my own experience of chest-strap HRMs is that they are a bit unreliable. Time to put it to the test.

I have a Garmin Fēnix 5 which records wrist-based HR and I have a Garmin chest-strap which uses ANT+ to transmit. I could pick up the ANT+ signal with a Garmin Edge 800 so that I could record both datasets simultaneously, on the same bike ride. Both the Fēnix and Edge can record GPS and Time (obviously) allowing accurate registration of the data. I also set both devices to receive cadence data via ANT+ from the same cadence/speed sensor so that I could control for (or at least look at) variability in recordings. I rode for a ~1 hr ~32 km to capture enough data. Obviously this is just one trial but the data gives a feel for the accuracy of the two HRMs. Biking, I figured was a fair activity since upper body and wrist movement is minimal, meaning that the contacts for both HRMs are more likely to stay in place than if I was running.

I’ll get to heart rate last. First, you can see that the GPS recording is virtually identical between the two units – so that’s a good sign. Second, elevation is pretty similar too. There’s a bit of divergence at the beginning and end of the ride, since those parts are over the same stretch of road, neither device looks totally accurate. I’d be inclined to trust the Fēnix here since has a newer altimeter in it. Third, cadence looks accurate. Remember this data is coming off the same sensor and so any divergence is in how it’s being logged. Finally, heart rate. You can see that at the beginning of the ride, both wrist-based and chest-strap HRMs struggle. Obviously I have no reference here to know which trace is correct, but the chest-strap recording looks like it has an erroneous low reading for several minutes but is otherwise normal. The wrist-based HRM looks like it is reading 120ish bpm values from the start and then snaps into reading the right thing after a while. The chest-strap makes best contact when some perspiration has built up, which might explain its readings. The comparisons are shown below in grey. The correlation is OK but not great. Compared to cadence, the data diverge a lot more which rules out simple logging differences as a cause.

I found a different story in Smart recording mode on the Fēnix. This is a lower frequency recording mode, which is recommended to preserve battery life for long activities.

So what can we see here? Well, the data from the Fēnix are more patchy but even so, the data are pretty similar except for heart rate. The Fēnix performs badly here. Again, you can see the drop out of the chest strap HRM for a few minutes at the start, but otherwise it seems pretty reliable.  The comparison graph for heart rate shows how poorly the wrist-based HRM measures heart rate, in this mode.

OK, this is just two rides, for one person – not exactly conclusive but it gives some idea about the data that are captured with each HRM.

Conclusions

Wrist-based HRM is pretty good (at the higher sampling rate only) especially considering how the technology works, plus chest-strap HRMs can be uncomfortable to wear, so wrist-based HRM may be all you need. If you are training to heart rate zones, or want the best data, chest-strap HRM is more reliable than wrist-based HRM generally. Neither are very good for very short activities (<15 min).

For nerds only

Comparisons like these are quite easy to do in desktop packages like Rubitrack (which I love) or Ascent or others. They tend to mask things like missing data points, they smooth out the data and getting the plots the way you want is not straightforward. So, I took the original FIT files from each unit and used these for processing. There’s a great package for R called cycleRtools. This worked great except for the smart recording data which was sampled irregularly and it turns out and the package requires monotonic sampling. I generated a gpx file and parsed the data for this activity in R using XML. I found this snippet on the web (modified slightly).

library(XML)
library(plyr)
filename <- "myfile.gpx"
gpx.raw <- xmlTreeParse(filename, useInternalNodes = TRUE)
rootNode <- xmlRoot(gpx.raw)
gpx.rawlist <- xmlToList(rootNode)$trk
gpx.list <- unlist(gpx.rawlist[names(gpx.rawlist) == "trkseg"], recursive = FALSE)
gpx <- do.call(rbind.fill, lapply(gpx.list, function(x) as.data.frame(t(unlist(x)), stringsAsFactors=F)))
names(gpx) <- c("ele", "time", "temp", "hr", "cad", "lat", "lon")

Otherwise:

library(cycleRtools)
edge <- as.data.frame(read_fit(file = file.choose(), format = TRUE, CP = NULL, sRPE = NULL))
write.csv(edge, file = "edge.csv", row.names = F)

The resulting data frames could be saved out as csv and read into Igor to make the plots. I wrote a quick function in Igor to resample all datasets at 1 Hz. The plots and layout were generated by hand.

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The post title comes from “Turn That Heartbeat Over Again” by Steely Dan from Can’t Buy A Thrill