The Mobile Sensor Lab Numbers
The Mobile Sensor Lab Numbers
Ada re-derives this chapter’s own numbers step by step, at full precision
ADA · CALCULATION AUDIT
The Mobile Sensor Lab Numbers
Samples, uncertainty gates, and outdoor gain, ~4 minutes
A phone’s Web Sensor APIs hand back not just a live reading but the numbers that say whether to trust it. A motion setup sampling every 40 ms collects about 75 readings in a 3-second window; a location fix reporting 40 m of accuracy cannot resolve which of two points 10 m apart you are near; and the same phone that reads about 150 m indoors tightens to about 4 m under clear sky. This audit re-derives those lab numbers to ask when a browser reading is real evidence rather than just a live value.
Companion to the chapter Lab: Mobile Web Sensor APIs — every number here comes from that chapter.
See the relationship before changing it
The figure reads from left to right. The blue card is motion event interval. The middle card applies this page's rule. The green card is samples in three seconds. Walk the arrows once: set the input, apply the rule, then read the result with its unit.
The retained audit below checks several chapter fixtures. This model keeps those stated values fixed and changes only motion event interval, so the numeric fixture does not switch without explanation.
Derive the baseline in four named moves
- 1
Name the input. The chapter baseline is 40 ms.
- 2
Name the relationship. samples = 3,000 ms / event interval
- 3
Substitute with units. 3,000 ms / 40 ms per sample = 75 samples
- 4
Read the result. Keep the unit beside the value. Use it only inside the technical boundary on this page.
Predict, then change motion event interval
Try Predict the direction of samples = 3,000 ms / event interval. Test another motion event interval, then compare samples in three seconds.
Observe A shorter measured event interval produces more readings in the same three-second observation window. Reset motion event interval to 40 and compare samples in three seconds.
Explain A shorter measured event interval produces more readings in the same three-second observation window.
Check yourself
What should you do before trusting a moved-control result?
What does this small model leave out?
Ready: use the stated baseline inputs, then compare each displayed result.
1. Count samples from the measured interval
The chapter’s motion example uses an event interval near 40 ms and a 3 second setup window:
samples = 3 s / 0.040 s = 75 readings
That is enough to check a bounded stillness or tilt condition, but only if the lab records how many readings passed and failed the angle threshold.
2. Reject location decisions whose question is smaller than the uncertainty radius
The examples compare the reported accuracy radius with the spatial decision:
40 m accuracy / 10 m point spacing = 4.0
Both ratios are greater than 1, so the uncertainty is larger than the thing being decided. The honest result is reject, request a better fix, or defer.
3. Compare indoor and outdoor accuracy without hiding the scale change
The under-the-hood example gives about 150 m indoors and about 4 m outdoors:
outdoor clear-sky radius is 37.5 times smaller
What the audit buys you: the lab can say exactly why a browser reading was accepted, rejected, or retested. The math stays small, but it prevents a live phone value from being mistaken for proof.
Every number above is taken from the chapter’s own mobile sensor lab examples and re-derived step by step.