Saving power with accelerometers.
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@ -19,13 +19,15 @@ Now that I've recorded more than 100 GPX files, I have become quite fascinated w
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![](gpx_4.png)
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![](gpx_4.png)
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In March 2023, I became seriously interested in the idea of recording GPX 24/7. I started to think about how awesome these pink rivers and spiderwebs would look if every single path were lit up. Until now, I had primarily used Trackbook to record deliberate outings and special trips. And that's great, but it meant that my track collection mostly showed little disconnected bubbles of places I've been to only once or twice because I didn't typically record day-to-day events. Also, if I was [driving](/writing/not_just_bikes) to a location to hike at, I'd intentionally wait until I got there to start recording because the drive would dominate the statistics like distance travelled and average speed. What's worse is that I would sometimes forget to start recording at all. I knew that micromanaging the start and stop time of each recording for the purpose of statistics was silly, and that it would be better to collect as many trackpoints as possible and then make sense of it in post.
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In March 2023, I became seriously interested in the idea of recording GPX 24/7. I started to think about how awesome these pink rivers and spiderwebs would look if every single path were lit up. Until now, I had primarily used Trackbook to record deliberate outings and special trips. And that's great, but it meant that my track collection mostly showed little disconnected bubbles of places I've been to only once or twice because I didn't typically record day-to-day events. Also, if I was [driving](/writing/not_just_bikes) to a location to hike at, I'd intentionally wait until I got there to start recording because the drive would dominate the statistics like distance travelled and average speed. What's worse is that I would sometimes forget to start recording at all, and I'd miss the whole day. I knew that micromanaging the start and stop time of each recording for the purpose of statistics was silly, and that it would be better to collect as many trackpoints as possible and then make sense of it in post.
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Initially, I was concerned that if I recorded too many tracks, the boring commutes would drown out the interesting trips, but it turns out that boring tracks are actually interesting too, given time. Seeing how easily JOSM handled the quantity of points I loaded into it gave me confidence that I should proceed. It is probably better to regret having too much data than to regret not having it.
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Initially, I was concerned that if I recorded too many tracks, the boring commutes would drown out the interesting trips, but it turns out that boring tracks are actually interesting too, given time. Seeing how easily JOSM handled the quantity of points I loaded into it gave me confidence that I should proceed. It is probably better to regret having too much data than to regret not having it.
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However, the data structure and user interface of Trackbook are not geared toward 24/7 recording. It is designed for recording outings with a distinct start and end, and this presented me with some [friction](/writing/friction) in using it the way I imagined.
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However, the data structure and user interface of Trackbook are not geared toward 24/7 recording. It is designed for recording outings with a distinct start and end, and this presented me with some [friction](/writing/friction) in using it the way I imagined.
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I sat on the problem for a very long time, and then I finally decided to make a fork of Trackbook called [trkpt](https://github.com/voussoir/trkpt) that takes the data model in a direction more suited to producing long, meandering recordings that can be cut into separate GPX files after the fact. This is my first time forking someone else's project and republishing it with a new name. It makes me feel somewhat guilty, or rude, but it's ok by the license and I'm enjoying the freedom to make this work just how I want it.
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<p style="text-align:center;"><a style="display:inline-block;" href="https://github.com/voussoir/trkpt"><img src="trkpt_squircle_128x128.png"/></a></p>
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I sat on the problem for a very long time, and then I finally decided to make a fork of Trackbook called [trkpt](https://github.com/voussoir/trkpt) that takes the data model in a direction more suited to my goal. This is my first time forking someone else's project and republishing it with a new name. It makes me feel somewhat guilty, or rude, but it's ok by the license and I'm enjoying the freedom to make this work just how I want it.
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Basically, I swapped out the JSON-based file storage with an [SQLite](/writing/sqlite_what_a_hunk) database, and removed the entire concept of "Tracks" as a stored object. Instead, the database of trackpoints is queried on the fly to produce tracks with any start and end time you want, which you can export as GPX files. This also opens the door to JOSM-style megarenders and geospatial queries like "what day was I here?". I even made it pink.
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Basically, I swapped out the JSON-based file storage with an [SQLite](/writing/sqlite_what_a_hunk) database, and removed the entire concept of "Tracks" as a stored object. Instead, the database of trackpoints is queried on the fly to produce tracks with any start and end time you want, which you can export as GPX files. This also opens the door to JOSM-style megarenders and geospatial queries like "what day was I here?". I even made it pink.
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@ -33,10 +35,16 @@ Basically, I swapped out the JSON-based file storage with an [SQLite](/writing/s
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I also added "homepoints", which are points you can put on the map around your home or workplace so that trkpt does not record any trackpoints there. Due to GPS's natural inaccuracy and drift, which is especially bad when you are indoors, you tend to get large clouds of points around buildings if you leave the recording on, so homepoints eliminate that.
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I also added "homepoints", which are points you can put on the map around your home or workplace so that trkpt does not record any trackpoints there. Due to GPS's natural inaccuracy and drift, which is especially bad when you are indoors, you tend to get large clouds of points around buildings if you leave the recording on, so homepoints eliminate that.
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I do not literally run trkpt 24/7 just yet, because my instinct for optimization doesn't like the thought of leaving the GPS at full power all night long and at work, capturing tens of thousands of fixes that are immediately thrown away; or worse, struggling to capture a fix at all. It is hard to shake the somewhat superstitious feeling that it would have a negative effect on my phone's long-term battery lifespan [footnote_link]. Maybe I will find a balance that reduces power consumption near homepoints without sleeping the GPS so much that it is late to capture departures. These mental blocks are the only thing in the way of me running trkpt all the time, so the code changes themselves have been successful, I think. I am still starting trkpt when I wake up in the morning and turning it off at work. But for day trips on weekends I record the whole day with no stress, thanks to the flexibility of the new data model.
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In the first draft of this article, I wrote:
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> I do not literally run trkpt 24/7 just yet, because my instinct for optimization doesn't like the thought of leaving the GPS at full power all night long and at work, capturing tens of thousands of fixes that are immediately thrown away; or worse, struggling to capture a fix at all. It is hard to shake the somewhat superstitious feeling that it would have a negative effect on my phone's long-term battery lifespan. Maybe I will find a balance that reduces power consumption near homepoints without sleeping the GPS so much that it is late to capture departures. These mental blocks are the only thing in the way of me running trkpt all the time, so the code changes themselves have been successful, I think. I am still starting trkpt when I wake up in the morning and turning it off at work. But for day trips on weekends I record the whole day with no stress, thanks to the flexibility of the new data model.
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But, I sent an email to y20k to share my work with him, and he suggested using the device's accelerometers to improve the sleep/wake cycle and conserve battery. This really helps!
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Android provides a [significant motion sensor](https://source.android.com/docs/core/interaction/sensors/sensor-types#significant_motion) which, while implementation surely depends on the manufacturer, is meant to be used in low power scenarios where a latency of several seconds is acceptable. When we are near a homepoint, we can reduce the polling frequency of the GPS to once per minute, or perhaps five minutes. As soon as motion is detected, we can wake it back up to full power, so that by the time your shoes are tied, your fix is hot again.
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When I am at work, my device struggles to acquire any GPS fixes at all, so trkpt can't even know if I'm near the homepoint. In this case, there is no benefit in reducing the polling frequency of the GPS. If you ask Android for one location per minute and it has to struggle for several minutes to get a single fix, then you're not saving anything. The GPS burns a lot of energy in this state and it's better to turn it fully off. This means we are placing a lot of trust in the motion sensor to restart the GPS, so I will see how it goes and continue experimenting with the balance between all these features.
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Anyway, the code is the easy part. Now I have to do the hard part which is actually going out to interesting places more often and turning the world pink.
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Anyway, the code is the easy part. Now I have to do the hard part which is actually going out to interesting places more often and turning the world pink.
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Thank you y20k for making Trackbook!
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Thank you y20k for making Trackbook!
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[footnote_text] perhaps someday we'll [invent](/writing/the_stuff_of_fiction) a standardized interchangeable battery for pocket computers.
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