Fitts's Law Target Acquisition for Assistive Access

Target Layout

Target width W 90 px

Target distance D 380 px

Access Impairment Model

Motor noise (tremor) σ 8 px

Pointer smoothing filter 20%

Click the glowing target to begin.

Live Readouts

Index of difficulty

0.00 bit

Predicted MT (Fitts)

— ms

Actual avg MT

— ms

Throughput

— bit/s

Error rate

Trials

#	ID (bit)	MT (ms)	Result

How it works

This is the ISO 9241-9 reciprocal tapping test used to evaluate switch, eye-gaze, and AAC (augmentative & alternative communication) devices in special-education assistive technology. You tap alternately between two targets of width W separated by distance D. Fitts's Law (Shannon formulation) predicts how long that takes:

ID = log2(D / W + 1)         "index of difficulty", in bits
MT = a + b · ID               predicted movement time
Throughput = ID / MT          bits acquired per second

Here a ≈ 150 ms and b ≈ 120 ms/bit (typical values for a well-tuned pointing device). The simulator fits your own recent taps into the same model so you can compare your actual average movement time against the prediction.

Target width / distance — a bigger W or a shorter D lowers ID, which is exactly why AT designers enlarge on-screen buttons and AAC grid cells for motor-impaired users.
Motor noise σ — models hand tremor or limited fine-motor control: your visible cursor drifts randomly around your intended aim point, and a small target is missed far more often at high σ.
Pointer smoothing filter — a low-pass filter (as used in real eye-gaze and tremor-compensation software) damps that jitter at the cost of added lag, trading precision for responsiveness.

The result you should see: raising σ or shrinking W drives up the error rate and actual MT, while raising the smoothing filter can recover accuracy but only up to a point — the same trade-off assistive-technology evaluators tune for every real switch, eye-tracker, or AAC device.

Tap the glowing target, then the other one, back and forth.

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