OK, I have renamed time_t to sc_time_t so it doesn't conflict with linux time_t. Try pulling.
Thanks - can confirm compiles without error now.
Thanks to work on the harness - its great to have 2 algorithms to compare.
Are you going to write a brief for your students ?
Pointers I would give them would be:
Use 1 .c file and one .h file and a simple make file.
Do not over engineer the stages using fancy queues, buffers, message passing etc
Use the int_sqrt() function as that is what the embedded devices will mostly have to use.
avoid large arrays.
Add lots of comments to explain how the code works.
If you use some statistical symbol / concept make sure you reference to a site that explains it clearly - ie don't just say /* using black's algorithm */
Avoid fancy Computer Science terminology like 'single directed graph - instead use plain english like - tree structure'
Testing should prove good accuracy when walking for long periods (not just short bursts) and non stepping use cases such as sleeping. driving, sitting at a desk, watching TV - for an hour of such activities we want sub 10 steps to be logged.
As a general aim I think we should try to have something as good as an average fitness tracker.
Agree. I would like to get to a consistant 2% within control on walking and 200 steps across the day on non stepping activities. 200/10000 steps is 2%. Still a long way from the later.
I have a 1 hour train journey tomorrow so going to try an capture a log just sitting on a train - 0 steps.
In the end it depends on the use case.
I've had a few fitness bands / watches over the years. The most common use case for me and most people I know who use them is just monitoring that you are getting your 10,000 steps per day in. For training / running and anything that needs split second and distance accuracy you have to use GPS - I would see the Bangle Run app fulfilling that niche - if its made to work well.
There is a fundemental limit on what we can do with the Bangle with a 12.5hz sample time and 3hz low pass filter. The effectiveness of the LPF is constrained by the sampling rate.
Espruino is a JavaScript interpreter for low-power Microcontrollers. This site is both a support community for Espruino and a place to share what you are working on.
@user107850
Thanks - can confirm compiles without error now.
Thanks to work on the harness - its great to have 2 algorithms to compare.
Are you going to write a brief for your students ?
Pointers I would give them would be:
Agree. I would like to get to a consistant 2% within control on walking and 200 steps across the day on non stepping activities. 200/10000 steps is 2%. Still a long way from the later.
I have a 1 hour train journey tomorrow so going to try an capture a log just sitting on a train - 0 steps.
I've had a few fitness bands / watches over the years. The most common use case for me and most people I know who use them is just monitoring that you are getting your 10,000 steps per day in. For training / running and anything that needs split second and distance accuracy you have to use GPS - I would see the Bangle Run app fulfilling that niche - if its made to work well.
3hz is within slow running pace, approx 12 mins per mile.
See the table I produced for this. http://forum.espruino.com/conversations/359542/?offset=75
There is a fundemental limit on what we can do with the Bangle with a 12.5hz sample time and 3hz low pass filter. The effectiveness of the LPF is constrained by the sampling rate.