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Head-Tracking Pointing (Ballard & Stockman, 1995)

Measured by Hansen, Rajanna, MacKenzie & Bækgaard · COGAIN @ ETRA 2018 (2018)

Head Pointing 1995

Inputs

The measured or assumed values behind the calculations, each with its source.

TP = 2.472 bits/s
Fitts' law throughput for head pointing on a head-mounted display, computed per ISO 9241-9 (effective width). Same study, same task: eye-gaze pointing 2.127 bits/s, mouse 3.239 bits/s. The system date follows Ballard & Stockman's 1995 facial-aspect computer-control paper; the throughput is measured in the 2018 HMD study.
MT = 850 ms
Mean time to activate a target by head pointing (gaze was 812 ms, mouse 642 ms in the same study).
E = 0.26 %
Click-condition error rate for head pointing; gaze was 1.53% and mouse 0.06% in the same study. Effective width folds endpoint scatter into the reported throughput.

Strictest ITR

Each scoring method is an upper bound on the channel, so the headline is the strictest (smallest) one for this entry. Use the score selector on the home page to view any single method across entries.

Strictest Fitts' law Author-reported · reproduced
Fitts' law throughput, re-derived from the task conditions
2D pointing channel (apples-to-apples with the mouse)
2.47 bits/s
  1. Information per movement (index of difficulty)

    ID = log2(A/W + 1);  amplitudes (160, 260 px) × widths (50, 100 px) → ID = 1.4–2.6 bits (mean ≈ 2.0 bits/movement)

    Each head-pointing acquisition selects among endpoints set by the distance-to-width ratio; that ratio, in bits, is the information the movement carries.

  2. Accuracy folded in via effective width

    W → We per ISO 9241-9, from endpoint scatter; head effective target width = 51.707 px

    The head pointer had lower click-condition error than eye gaze in this study (0.26% vs 1.53%), and the effective-width correction is already reflected in the reported throughput.

  3. Throughput = information ÷ movement time

    mean MT = 850 ms → TP = 2.472 bits/s for head pointing (gaze 2.127, mouse 3.239 in the same study)

What counts as a bit depends on the action space. The number of distinguishable actions and how likely each one is are design choices of the task, not the sensing hardware. The same modality can present a fixed set of targets, a set pruned per step by a grammar or language model, or a continuous control space. Each of these changes how many actions are live and how the probability mass is spread, and therefore the information per selection. Read the action space below before comparing headline numbers across entries.

Action space

What the user can produce at each step, and how those options are distributed.

Structure
Continuous control space
Size
Continuous
Prior
Uniform: all actions assumed equally likely
Notes
Head pointing used as a 2D cursor-control channel: the headset's central forward projection acts as the pointer, and the user moves the head to acquire spatial targets. This is distinct from eye-gaze pointing in the same Hansen et al. experiment, which used the intersection of gaze vectors as the pointer. Fitts' throughput folds in speed and spatial accuracy, so this entry is comparable to mouse, trackball, stylus, tongue, eye-gaze and cursor-BCI pointing channels.

Comparability The strictest bound here is Fitts throughput: the index of difficulty, log₂(A/W + 1), per movement. Directly comparable to the other continuous-pointing entries (mouse, trackball, stylus, gaze and the cursor BCIs). Set against the text entries (keyboards, spellers, speech) it crosses methods: both report bits/s, but one measures movement difficulty and the other text information, so compare within the family first.

Source

Authors
Hansen, Rajanna, MacKenzie & Bækgaard
Publication
COGAIN @ ETRA 2018, 2018
Paper
10.1145/3206343.3206344
Reference
System date: Ballard & Stockman 1995 facial-aspect computer control
Reference
I. S. MacKenzie: paper page with full results