ActiVibe was designed as a set of vibrotactile icons to represent progress.
As activity performance is generally evaluated as a percentage or as a value on a scale, we created vibrations corresponding to the values 1 to 10, intending to represent 10\% increments.
-Because of our choice of using a single basic vibration actuator, we encode the vibrations using the duration and rhythm parameters only.
+%Because of our choice of using a single basic vibration actuator, we encode the vibrations using the duration and rhythm parameters only.
Since there was no prior encoding of discrete numbers found in the literature using duration and rhythm only, we first had to determine the best encoding pattern for ActiVibe.
%We designed a total of six patterns (\reffig{fig:activibesets}) and in our subsequent evaluations used the pattern with the highest accuracy rate for ActiVibe.
Introducing a scale enables the positioning of a value relative to the beginning and the end of a progression.
Sets C-E represent both the value and the scale of a progression in several ways.
Either the current value is represented by a series of short vibrations and the scale by filling the sequence with a long vibration, either before or after the value (\reffig{fig:activibesets}, sets C \& E), or the current value is represented by a long vibration and the scale is represented by filling the sequence with a series of short vibrations (\reffig{fig:activibesets}, set D).
-Set F was defined using the results from the first laboratory study as a combination of short pulses and long vibrations.
+Set F was defined using the results from the first laboratory study described in \refsec{sec:evalactivibe} as a combination of short pulses and long vibrations.
\begin{table}[htb]
\centering
Finally, the pre-vibration was long vibrations of \qty{700}{\ms}.
\subsubsection{Evaluation}
+\label{sec:evalactivibe}
I will not give the details about the evaluations in this study.
I will rather discuss the main findings with hindsight.
\paragraph{Input systems}
-Input systems observe our movements with sensors that measure directly or indirectly parts of our movements.
+Input systems capture our movements with sensors that measure directly or indirectly parts of our movements.
%\defword{Direct input} consists in measuring the movement of our body, typically fingers and arms.
%\defwork{Indirect input} systems measure the movements of an object manipulated by the user.
There are several families of technologies for this.
%mapping between DOFs and objects manipulated
For example, in \refsec{sec:interactionvr} we present two interaction techniques for immersive virtual environments.
The first one is a selection technique for distant objects.
-The second one enable susers to select facial expressions for their avatar without the need to perform the same expression with their own face.
+The second one enables users to select facial expressions for their avatar without the need to perform the same expression with their own face.
%Raycursor, facial expressions
\subsubsection{Haptic Direct Manipulation}
\label{sec:hapticdm}
-We discussed the concept of direct manipulation inf \refsec{sec:systemarch}~\cite{schneiderman83}.
+We discussed the concept of direct manipulation in \refsec{sec:systemarch}~\cite{schneiderman83}.
It is one of the most important concepts of GUIs.
Its properties provide valuable usability benefits that highly contributed to the success of GUIs over command line interfaces.
Yet, this paradigm was tailored for visual interfaces.
Targets are represented with a \qty{250}{\hertz} frequency and the background with \qty{100}{\hertz}.
Not only it is an easily distinguishable vibration, but the \qty{100}{\hertz} is subtle and avoids or at least reduces numbness.
The inputs use a multitouch smartwatch.
-Up and down swypes move the cursor in either direction.
+Up and down swipes move the cursor in either direction.
The tracking states trigger with one contact point and the dragging state triggers with two contact points.
The cursor is not felt in the idle state, to avoid numbness.
The details about feedback and states machines are presented in the paper~\cite{gupta16}.
This simple example shows the rigidity of algorithmic behavior.
All information about the problem must be known in advance, the computing process is precisely defined, and the output is specified by the inputs~\cite{knuth68}.
Wegner and Goldin describe interaction as a more general model in which the machine is connected to input streams, that provide unpredictable data~\cite{goldin08}.
-They discuss interaction between systems, but Beaudouin-Lafon explains how their concept also applies to HCI~\cite{mbl08}.
+They discuss interaction between systems, and Beaudouin-Lafon explains how their concept also applies to HCI~\cite{mbl08}.
They model interaction with co-induction as in the example in Listing~\ref{lst:coinduction}.
This example defines a stream of numbers, which is a non-finite structure, and a function that returns another stream in which numbers from the input stream are multiplied by \num{2}.
The co-inductive definition of the stream only has one constructor, identical to the second constructor of lists.
% So, adding haptic feedback to gestural interaction does not bring something new, but restore something that should have been there right at the beginning.
% My focus on the sensorimotor loop is also motivated by my impression that this is the most underrated mechanism of interactive systems.
-The interaction models I describe mostly describe current good practices.
+The interaction models I described mostly correspond to current good practices.
Therefore I tend to claim they have descriptive properties.
But at this stage, I am still uncertain of their generative properties.
These are the results of years of thinking about what started as an attempt to make a bridge between HCI and theoretical computing.
The rigidity of the algorithmic approach is today compensated by the promising flexibility of machine learning mechanisms.
The idea here is not to replace humans, or even perform activities on their behalf as in the computer-as-a-partner paradigm.
The idea here is to leverage mechanisms such as reinforcement learning to break the rigidity of traditional computer programs.
-I belive it will enable them to probe their environment, try, evolve to avoid making repeatedly the same mistakes, or even avoid Gödel's incompleteness phenomenon by developing new features and capabilities by themselves~\cite{godel31}.
+I believe it will enable them to probe their environment, try, evolve to avoid making repeatedly the same mistakes, or even avoid Gödel's incompleteness phenomenon by developing new features and capabilities by themselves~\cite{godel31}.
I believe we can leverage a proper combination of rational and empirical methods to improve interaction.
So far, my research did not focus on any category of users, technology, or interaction context.
projects / hdr.git / commitdiff