DIFFERENTIAL CAPACITIVE TOUCH POINTER
The differential capacitive touch pointer
comprises just four paper-thin copper pads and a small IC. The IC measures the
capacitive effect of a finger (or thumb) touching the pads. Any touch affects
all four pads but the touch position determines the strength of the effect. The
differential between left and right and top and bottom pads yields a resolution
of at least 1000 in both dimensions. Because, it is differential, the position
signal is immune to electric noise and background capacitance. This allows the
sensitivity to be very high without swamping the signal with noise.
Consequently, the device can be placed inside a thick plastic case with no
opening for the touch pads. The touch position is detected through the solid
case.
DISPLACEMENT ACCELERATION AND SUB-PERCEPTIVE GESTURE ANALYSIS
The high resolution is useful for analysis but no human has sufficient tactile
acuity to directly control 1000-point resolution over an 11mm range. Therefore,
the input can’t be mapped directly to the display but must be used in
displacement mode. All such devices change the mapping according to sliding
speed. Sliding faster expands the mapping to produce a larger displacement.
With such a small input area, mapping acceleration methods used for larger
devices are not very effective. I developed a new
acceleration method based on
control theory. This is instantaneously responsive without overreacting and is
continuously variable without the quantization seen in other methods. The
control terms include not only the usual, such as speed integral and
derivative, but also unique measures of user intent derived from
sub-perceptive gesture analysis.
FLICK SIGNAL PROCESSING: CROWD WISDOM
Also see flick algorithm development.
As with all capacitive touch devices, the finger’s X-Y position as it
approaches or withdraws from the device is not reliably indicated. Most touch
devices discard this ambiguous hover information. However, with a small device
like this, a natural and comfortable flick may comprise only approach and
withdrawal with little or no precise position information. To recognize the
user’s intent as the length, speed, and angle of a flick, I developed 25 unique
signal processing methods. These individually vary in reliability from 50% to
80%. Since no one method is good enough, I developed an adaptive voting scheme.
If the best ones agree, they are always right. Otherwise, more and more of the
less reliable ones are allowed to vote. These less reliable methods are not
simply inferior but ones selected for their reliability in resolving disputes
between the better methods. The result is nearly 100% reliability, which is to
say that most users think that the device has responded correctly. There are no
cases where the device reports incorrectly but a few where it cannot be sure
and reports this instead of an erroneous flick.
See Flick Processor.