Signal Processing - March 2017 - 86
which represent the likelihood of each of the icons in D being
the unknown intended on-display end point D I ! D. This uses
the gesture tracker measurements Y1:k (and possibly other
sensory data), i.e., the available partial pointing finger track at
t k whose extraction might require simple data sorting and associating routines. Each observation Yk is assumed to be derived
from an underlying pointing finger true (perturbation-free)
latent state X t k , that can include its position, velocity, etc.
Within a Bayesian framework, we have
a select signal to/by the existing interface software module
with minimal display overheads.
After inferring p (D I = D i Y1: k) at time t k, the end point
t
D I (t k) ! D of a freehand pointing gesture can be estimated
(if needed) by minimizing the expected value of a cost function over all of the possible destinations in D. This can be
expressed by
Dt I (t k) = arg min
D* ! D
p (D I = D i Y1: k) ? p (Y1: k D I = D i) p (D I = D i),
N
/ C (D *, D I) p^D I = D i | Y1:kh,
(4)
i= 1
(3)
where C (D *, D I ) is the cost of deciding D * as the destinawhere the prior p (D I = D i) on the selectable items (indetion, given that D I is the true intended on-display icon. If
pendent of Y1:k or the current pointing task) can be attained
the binary decision criterion C (D *, D I ) = 1 if D * ! D I
from relevant semantic and contextual information, such as
and C (D *, D I ) = 0 otherwise is used, it can be easily seen
selection frequency, GUI design, user prothat (4) leads to the maximum a posteriori
file, etc. This makes the adopted formula(MAP) estimate; it implies that the most
in the area of object
tion particularly appealing as additional
probable end point is deemed to be the
tracking, knowing the
information, when available, can be easily
intended on-display selectable icon. Withdestination of a tracked
incorporated. For example, the priors in
in the Bayesian framework, more elaborate
object not only leads to
(3) can be gradually and dynamically
cost functions can be applied [23]; groups
t q 1 D rather than an individual icons
learned as the system is being used, startD
more accurate tracking
ing from uninformative ones. Therefore, it
may
also be considered for expansion or
results, but also offers
is an adaptable probabilistic (belieffading purposes.
vital information on intent,
based) approach.
While the intuitive MAP estimate can
revealing potential conflict be used to assess the suitability of the preA prediction is performed at the arrival
or threat.
of each (or a few) new sensor observation(s).
diction model, it can produce fast fluctuThe inference module can use a number of
ating decisions during the pointing task.
low complexity, computationally efficient probabilistic end-point
This can be detrimental to midair selection due to the resulpredictors that are amenable to real-time implementation, given
tant false positives. In such cases, a simple decision rule can
the limited computing resources and training data available in
stipulate that the probability of an icon p ^D I = D i Y1: k h,
vehicles. The linear models discussed in the next section lead to
specifically the one delivered by the MAP classifier, should
a Kalman-filter-type implementation, combining end-point preexceed a certain threshold for a given duration of time before
diction and filtering out of noise induced by road/driving conditriggering an autoselection action.
tions. For severe perturbations, a separate statistical filter can be
employed to remove the highly nonlinear gesture motion arising
Adaptable GUI and selection confirmation
from perturbations.
The displayed interface implements seamlessly, in real time,
the applied pointing facilitation scheme. If an on-screen item
is selected or autoselected, the user can substantially benefit
Facilitation scheme and decision
(i.e., in terms of reducing the visual workload) from a feedTo assist the selection task, the displayed interface may be
back confirming the selection action, e.g., an audible or haptic
modified at t k , e.g., icons can be expanded/shrunk, colored/
signal. For a predictive display with midair selection, the
faded, or other [11], [12], [14], as per their probabilities P (t k)
emerging ultrasonic midair haptic technology [24] presents
in (2). Such facilitation strategies can require major modificaitself as a suitable equivalent to the conventional on-screen
tions to legacy in-vehicle GUI designs and possibly the related
haptic feedback, which is used in standard smartphone devicsoftware-hardware architectures. Their impact on the user
es, with established benefits.
experience in a split attention scenario (driving and interacting) is nontrivial and can be advised by experimental studies.
For instance, unlike mouse pointing on a computer screen,
Additional sensory data
constantly changing the in-car interface can increase visual
The availability of additional vehicle sensory data, such as
demand to monitor the ongoing changes. A promising pointsuspensions travel data via the controller area network (CAN)
ing facilitation scheme is midair selection, where the system
bus or an on-board inertial measurement unit (IMU), can enautoselects the predicted intended on-screen item on behalf of
able the intelligent predictive display system to establish the
the user, who does not need to physically touch the display
operating conditions, for instance, allowing it to determine
surface. While midair selection can reduce the freehand
whether the user input is perturbed or even estimate the level
pointing gesture duration and thus effort (visual, cognitive,
of noise present. It can then modify the applied statistical
and manual), its implementation entails only sending/reading
model by adapting its parameters or performing preprocessing
86
IEEE SIgnal ProcESSIng MagazInE
|
March 2017
|
Table of Contents for the Digital Edition of Signal Processing - March 2017
Signal Processing - March 2017 - Cover1
Signal Processing - March 2017 - Cover2
Signal Processing - March 2017 - 1
Signal Processing - March 2017 - 2
Signal Processing - March 2017 - 3
Signal Processing - March 2017 - 4
Signal Processing - March 2017 - 5
Signal Processing - March 2017 - 6
Signal Processing - March 2017 - 7
Signal Processing - March 2017 - 8
Signal Processing - March 2017 - 9
Signal Processing - March 2017 - 10
Signal Processing - March 2017 - 11
Signal Processing - March 2017 - 12
Signal Processing - March 2017 - 13
Signal Processing - March 2017 - 14
Signal Processing - March 2017 - 15
Signal Processing - March 2017 - 16
Signal Processing - March 2017 - 17
Signal Processing - March 2017 - 18
Signal Processing - March 2017 - 19
Signal Processing - March 2017 - 20
Signal Processing - March 2017 - 21
Signal Processing - March 2017 - 22
Signal Processing - March 2017 - 23
Signal Processing - March 2017 - 24
Signal Processing - March 2017 - 25
Signal Processing - March 2017 - 26
Signal Processing - March 2017 - 27
Signal Processing - March 2017 - 28
Signal Processing - March 2017 - 29
Signal Processing - March 2017 - 30
Signal Processing - March 2017 - 31
Signal Processing - March 2017 - 32
Signal Processing - March 2017 - 33
Signal Processing - March 2017 - 34
Signal Processing - March 2017 - 35
Signal Processing - March 2017 - 36
Signal Processing - March 2017 - 37
Signal Processing - March 2017 - 38
Signal Processing - March 2017 - 39
Signal Processing - March 2017 - 40
Signal Processing - March 2017 - 41
Signal Processing - March 2017 - 42
Signal Processing - March 2017 - 43
Signal Processing - March 2017 - 44
Signal Processing - March 2017 - 45
Signal Processing - March 2017 - 46
Signal Processing - March 2017 - 47
Signal Processing - March 2017 - 48
Signal Processing - March 2017 - 49
Signal Processing - March 2017 - 50
Signal Processing - March 2017 - 51
Signal Processing - March 2017 - 52
Signal Processing - March 2017 - 53
Signal Processing - March 2017 - 54
Signal Processing - March 2017 - 55
Signal Processing - March 2017 - 56
Signal Processing - March 2017 - 57
Signal Processing - March 2017 - 58
Signal Processing - March 2017 - 59
Signal Processing - March 2017 - 60
Signal Processing - March 2017 - 61
Signal Processing - March 2017 - 62
Signal Processing - March 2017 - 63
Signal Processing - March 2017 - 64
Signal Processing - March 2017 - 65
Signal Processing - March 2017 - 66
Signal Processing - March 2017 - 67
Signal Processing - March 2017 - 68
Signal Processing - March 2017 - 69
Signal Processing - March 2017 - 70
Signal Processing - March 2017 - 71
Signal Processing - March 2017 - 72
Signal Processing - March 2017 - 73
Signal Processing - March 2017 - 74
Signal Processing - March 2017 - 75
Signal Processing - March 2017 - 76
Signal Processing - March 2017 - 77
Signal Processing - March 2017 - 78
Signal Processing - March 2017 - 79
Signal Processing - March 2017 - 80
Signal Processing - March 2017 - 81
Signal Processing - March 2017 - 82
Signal Processing - March 2017 - 83
Signal Processing - March 2017 - 84
Signal Processing - March 2017 - 85
Signal Processing - March 2017 - 86
Signal Processing - March 2017 - 87
Signal Processing - March 2017 - 88
Signal Processing - March 2017 - 89
Signal Processing - March 2017 - 90
Signal Processing - March 2017 - 91
Signal Processing - March 2017 - 92
Signal Processing - March 2017 - 93
Signal Processing - March 2017 - 94
Signal Processing - March 2017 - 95
Signal Processing - March 2017 - 96
Signal Processing - March 2017 - 97
Signal Processing - March 2017 - 98
Signal Processing - March 2017 - 99
Signal Processing - March 2017 - 100
Signal Processing - March 2017 - 101
Signal Processing - March 2017 - 102
Signal Processing - March 2017 - 103
Signal Processing - March 2017 - 104
Signal Processing - March 2017 - 105
Signal Processing - March 2017 - 106
Signal Processing - March 2017 - 107
Signal Processing - March 2017 - 108
Signal Processing - March 2017 - 109
Signal Processing - March 2017 - 110
Signal Processing - March 2017 - 111
Signal Processing - March 2017 - 112
Signal Processing - March 2017 - 113
Signal Processing - March 2017 - 114
Signal Processing - March 2017 - 115
Signal Processing - March 2017 - 116
Signal Processing - March 2017 - 117
Signal Processing - March 2017 - 118
Signal Processing - March 2017 - 119
Signal Processing - March 2017 - 120
Signal Processing - March 2017 - 121
Signal Processing - March 2017 - 122
Signal Processing - March 2017 - 123
Signal Processing - March 2017 - 124
Signal Processing - March 2017 - Cover3
Signal Processing - March 2017 - Cover4
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