A field team faces an unresolved physical question: Can a 50 Hz clock catch a 20 ms fall impact? They must answer it before changing sample rate on the real device. Predict the direction first.
See the relationship before changing it
The figure reads from left to right. The blue card is sample rate. The middle card applies this page's relationship. The green card is 3g displacement. 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 added model holds every other chapter fixture fixed, so the numeric fixture does not switch without explanation.
Derive the baseline in four named moves
- 1
Name the input. The chapter baseline for sample rate is 50.
- 2
Name the relationship. x3g=3x9.81/(2πx5500)²=24.6 nm x16g=130.5 nm q=32g/4096=7.8125 mg/code 3g/q=384 codes; 2g/q=256 codes fpulse≈1/0.020=50 Hz fs,min=2x50=100 Hz; 50x0.020=1 sample
- 3
Substitute the chapter fixture. Set sample rate to 50. The page ledger gives 3g displacement as 24.644 nm.
- 4
Read the result. Keep nm beside the value. Use it only inside the technical boundary on this page.
Predict, then change sample rate
Try Predict the direction of 3g displacement. Move one control, calculate, then check your prediction.
Observe Quantisation divides amplitude; sampling divides time. Passing one does not repair failure in the other. Reset the control to 50 and compare 3g displacement.
Explain Only sample rate moves here. The other chapter fixtures remain fixed.
Check yourself
What should you do before trusting a moved-control result?
What does this small model leave out?
1. Start with the physical story
A MEMS accelerometer reads the motion of a tiny spring-mounted mass. Its converter can resolve many amplitude steps, yet a slow clock can still sample before and after a brief impact. Good codes cannot recover a peak that was never sampled.
2. Name every algebra move
Cancel the massSubstitute k=m(2πfn)² into x=ma/k.
Find displacementDivide acceleration by squared resonance angular frequency.
Find one codeDivide the full 32g span by 2¹².
Convert pulse to frequencyUse approximately one over 20 ms.
Apply NyquistDouble the fastest feature frequency.
Count opportunitiesMultiply sample rate by pulse duration.
3. Reproduce the chapter case
x16g=130.5 nm
q=32g/4096=7.8125 mg/code
3g/q=384 codes; 2g/q=256 codes
fpulse≈1/0.020=50 Hz
fs,min=2×50=100 Hz; 50×0.020=1 sample
The ADC has ample nominal code resolution for the thresholds, while 50 Hz offers only one sample opportunity in the illustrative impact window.
4. Try one real input
TryRaise sample rate and predict the sample period and opportunities inside 20 ms.
ObserveAt 50 Hz the sample period equals the pulse duration. At 100 Hz there are two nominal opportunities.
ExplainQuantisation divides amplitude; sampling divides time. Passing one does not repair failure in the other.
This is a quasi-static mass-spring and pulse-width screen.
- MEMS
- Damping, bandwidth, axis alignment, analogue filtering, noise, saturation, and device calibration shape the reading.
- Falls
- Impact duration, body location, surface, activity, and person vary; 20 ms is illustrative.
- Algorithm
- Threshold timing alone does not establish a fall, urgency, or clinical outcome.
Correct, not complete: this calculation does not validate a fall detector or care pathway.
5. Use the result in the design
Choose rate and anti-alias filtering from measured impact spectra, then test phase, body location, activities of daily living, cancellation, and escalation with the intended population.
6. Record the evidence state
Keep sensor model, range, rate, filter, axis, mounting, calibration, timestamp, raw trace, threshold, impact width, activity label, user confirmation, alert delivery, and outcome.
7. Check yourself
Why does the proof mass move only nanometres?
Why are 384 codes not enough evidence?
Does 100 Hz validate fall detection?
The arithmetic reproduces the chapter's 3g and 50 Hz case with catalog-style sensor assumptions.
- MEMS
- Damping, bandwidth, axis alignment, analogue filtering, noise, saturation, and device calibration shape the reading.
- Falls
- Impact duration, body location, surface, activity, and person vary; 20 ms is illustrative.
- Algorithm
- Threshold timing alone does not establish a fall, urgency, or clinical outcome.
Correct, not complete: this calculation does not validate a fall detector or care pathway.
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