I have been using a Basis B1 and then a Basis Peak for the last 2 years.
Using R, here is what I learnt from these data.
You can use my R script to do the same basic analyze I have done:
> source('basisData.R') > basisData('bodymetrics.csv')
> head(data) date calories gsr heart.rate skin.temp steps 1 2013-07-28 00:00Z 1.206 3.4058700 57 93.2000 0 2 2013-07-28 00:01Z 1.200 3.6132000 53 93.2000 0 3 2013-07-28 00:02Z 1.200 3.6085500 55 93.2000 0 4 2013-07-28 00:03Z 1.381 4.3840100 57 93.2375 0 5 2013-07-28 00:04Z 1.264 4.0713400 55 93.2000 0 6 2013-07-28 00:05Z 1.200 3.2947900 55 93.0125 0 7 2013-07-28 00:06Z 1.591 5.0413300 58 92.9750 0 8 2013-07-28 00:07Z 1.456 5.6096600 65 93.2000 0 9 2013-07-28 00:08Z 1.442 10.7239000 56 93.2000 0 10 2013-07-28 00:09Z 1.200 12.0046000 52 93.2188 0
The export contains one line per minute, and for each minute a value for our Calories, Galvanic Skin Response (GSR), Heart rate, Skin temperature (ºF) and Steps.
I looked at my data between
2013-07-28 00:00 and
2015-07-27 23:59. This is 730 days or 1,051,199 minutes.
However, my CSV dump contains 1,010,286 lines that being ~701 days.
I suppose this means that for ~29 days (40,913 minutes) my watch was out of battery and not recording anything - that’s an acceptable 3.9% of down time.
Then on the 96.1% of the time my tracker was working, I had to filter
NA values, as I have not been wearing the watch while taking my shower, doing water activities, charging the battery and surely some hours where I forgot to put it back on my wrist.
For my heart pulse, I have 864,335 measures (82%), that’s 600 days. My average value for this period is 66.15 beats per minute. The min value is 30 bpm (seems quite low) and max value 206 bpm.
The sum of all my steps I have recorded for the period is 4,946,531 steps. This is an average of 4.8 steps per minute over 701 days or 7,056 steps per day.
Visualization 1 - Steps per minute
This is the first visualization of my data. On the
x axis the time (24 hours) with minute granularity. On the
y axis steps per minute.
Each minute of the day are assigned with a number of steps per minute and a color, over 701 days. As the multiple days overlap with each others it creates density areas.
Using mono-color and alpha transparency, the different clusters and areas appear more clearly:
Clusters detached at the top, around 170 steps per minute are my running sessions, more often happening in the afternoon than in the morning.
On the morning around 8:30, we have a very dense zone which is my morning commute. My evening commute is more spread, but still around 19:00.
Visualization 2 - Steps per day
x axis the days (2 years). On the
y axis steps per day.
Each days of the week are assigned with a color. The goal was to visually find patterns. We can see that Saturdays, in green, generally have peaks: shopping and running day :). By April 2015, Wednesdays are also trending as I have been doing some sport regularly on this day.
The feeling about the Saturdays is confirmed by the following visualization, showing the average steps per days of the week, busy Saturday / lazy Sunday.
This is a very small analyze due to the lack of API of retrieve of the data a Basis tracker is recording (sleep, running, ...). I hope Basis will release an API to let us dive into more data.