François Bérard

Did Minority Report Get It Wrong? Superiority of the Mouse over 3D Input Devices in a 3D Placement Task

Numerous devices have been invented with three or more degrees of freedom (DoF) to compensate for the assumed limitations of the 2 DoF mouse in the execution of 3D tasks. Nevertheless, the mouse remains the dominant input device in desktop 3D applications, which leads us to pose the following question: is the dominance of the mouse due simply to its widespread availability and long-term user habituation, or is the mouse, in fact, more suitable than dedicated 3D input devices to an important subset of 3D tasks? In the two studies reported in this paper, we measured performance efficiency of a group of subjects in accomplishing a 3D placement task and also observed physiological indicators through biosignal measurements. Subjects used both a standard 2D mouse and three other 3 DoF input devices. Much to our surprise, the standard 2D mouse outperformed the 3D input devices in both studies.

On the limits of the human motor control precision: the search for a device's human resolution

Input devices are often evaluated in terms of their throughput, as measured by Fitts Law, and by their resolution. However, little effort has been made to understand the limit of resolution that is controllable or usable by the human using the device. What is the point of a 5000 dpi computer mouse if the human motor control system is far from being able to achieve this level of precision? This paper introduces the concept of a Devices Human Resolution (DHR): the smallest target size that users can acquire with an ordinary amount of effort using one particular device. We report on our attempt to find the DHR through a target acquisition experiment involving very small target sizes. Three devices were tested: a gaming mouse (5700 dpi), a PHANTOM (450 dpi), and a free-space device (85 dpi). The results indicate a decrease in target acquisition performance that is not predicted by Fitts Law when target sizes become smaller than certain levels. In addition, the experiment shows that the actual achievable resolution varies greatly depending on the input device used, hence the need to include the device in the definition of DHR.

Reducing Latency with a Continuous Prediction: Effects on Users' Performance in Direct-Touch Target Acquisitions

Latency in direct-touch systems creates a spatial gap between the finger and the digital object when dragging. This breaks the illusion of presence, and has a negative effect on users performances in common tasks such as target acquisitions. Latency can be reduced with faster hardware, but reaching imperceptible levels of latency with a hardware-only approach is a difficult challenge and an energy inefficient solution. We studied the use of a continuous prediction of the touch location as an alternative to the hardware only approach to reduce the latency gap. We implemented a low latency touch surface and experimented with a constant speed linear prediction with various system latencies in the range [25ms-75ms]. We ran a user experiment to objectively assess the benefits of the prediction on users performances in target acquisition tasks. Our study reveals that the prediction length is strongly constrained by the nature of target acquisition tasks, but that the approach can be successfully applied to counteract a large part of the negative effect of latency on users performances.

Two touch system latency estimators: high accuracy and low overhead

The end-to-end latency of interactive systems is well known to degrade users performance. Touch systems exhibit notable amount of latencies, but it is seldom characterized, probably because latency estimation is a difficult and time consuming undertaking. In this paper, we introduce two novel approaches to estimate the latency of touch systems. Both approaches require an operator to slide a finger on the touch surface, and provide automatic processing of the recorded data. The High Accuracy (HA) approach requires an external camera and careful calibration, but provides a large sample set of accurate latency estimations. The Low Overhead (LO) approach, while not offering as much accuracy as the HA approach, does not require any additional equipment and is implemented in a few lines of code. In a set of experiments, we show that the HA approach can generate a highly detailed picture of the latency distribution of the system, and that the LO approach provides average latency estimates no further than 4 ms from the HA estimate.

The Transfer of Learning as HCI Similarity: Towards an Objective Assessment of the Sensory-Motor Basis of Naturalness

Human-computer interaction should be natural. However, the notion of natural is questioned due to a lack of theoretical background and methods to objectively measure the naturalness of a HCI. A frequently cited aspect of natural HCIs is their ability to benefit from knowledge and skills that users develop in their interaction with the real (non-digital) world. Among these skills, sensory-motor abilities are essential to operate many HCIs. This suggests that the transfer of these abilities between physical and digital interactions could be used as an experimental tool to assess the sensory-motor similarity between interactions, and could be considered as an objective measurement of the sensory-motor grounding of naturalness. In this framework, we introduce a new experimental paradigm inspired by motor learning research to assess sensory-motor similarity, as revealed by the transfer of learning. We tested this paradigm in an empirical study to question the naturalness of three HCIs: direct-touch, mouse pointing and absolute indirect-touch. The study revealed how skill learning transfers from these three digital interactions towards an equivalent physical interaction. We observed strong transfer of skill between direct-touch and physical interaction, but no transfer from the other two interactions. This work provides a first objective assessment of the sensory-motor basis of direct-touch naturalness, and a new empirical path to question HCI similarity and naturalness.

A Predictive Approach for an End-to-End Touch-Latency Measurement

With direct-touch interaction, users are sensitive to very low levels of latency, in the order of a few milliseconds. Assessing the end-to-end latency of a system is thus becoming an important part of touch-devices evaluation, and this must be precise and accurate. However, current latency estimation techniques are either imprecise, or they require complex setups involving external devices such as high-speed cameras. In this paper, we introduce and evaluate a novel method that does not require any external equipment and can be implemented with minimal efforts. The method is based on short-term prediction of the finger movement. The latency estimation is obtained on the basis of user calibration of the prediction to fully compensate the lag. In a user study, we show that the technique is more precise than a similar low overhead approach that was recently presented.

Two-finger 3D rotations for novice users: surjective and integral interactions

Now that 3D interaction is available on tablets and smart phones, it becomes critical to provide efficient 3D interaction techniques for novice users. This paper investigates interaction techniques for 3D rotation with two fingers of a single hand, on multitouch mobile devices.n We introduce two new rotation techniques that allow integral control of the 3 axes of rotation. These techniques also satisfy a new criterion that we introduce: surjection. We ran a study to compare the new techniques with two widely used rotation techniques from the literature. Results indicate that surjection and integration lead to a performance improvement of a group of participants who had no prior experience in 3D interaction. Qualitative results also indicate participants preference for the new interaction techniques.

Measuring the linear and rotational user precision in touch pointing

This paper addresses the limit of user precision in pointing to a target when the finger is already in contact with a touch surface. User precision was measured for linear and rotational pointing. We developed a novel experimental protocol that improves the estimation of users precision as compare to previous protocols. Our protocol depends on high-resolution measurements of finger motions. This was achieved by the means of two optical finger trackers specially developed for this study. The trackers provide stable and precise measurements of finger translations and rotations. We used them in two user experiments that revealed that (a) users precision for linear pointing is about 150dpi or 0.17mm, and (b) user can reliably point at sectors as narrow as 2.76 degrees in 2s in rotational pointing. Our results provide new information for the optimization of interactions and sensing devices that involve finger pointing on a surface.

Single user multitouch on the DiamondTouch: from 2 x 1D to 2D

The DiamondTouch is a widely used multi-touch surface that offers high quality touch detection and user identification. But its underlying detection mechanism relies on two 1D projections (x and y) of the 2D surface. This creates ambiguous responses when a single user exercises multiple contacts on the surface and limits the ability of the DiamondTouch to provide full support of common multi-touch interactions such as the unconstrained translation, rotation and scaling of objects with two fingers. This paper presents our solution to reduce this limitation. Our approach is based on a precise modeling, using mixtures of Gaussians, of the touch responses on each array of antennas. This greatly reduces the shadowing of the touch locations when two or more fingers align with each other. We use these accurate touch detections to implement two 1D touch trackers and a global 2D tracker. The evaluation of our system shows that, in many situations, it can provide the complete 2D locations of at least two contacts points from the same user.

Does Practice Make Perfect

Touch latency has been shown to deteriorate users performances at levels as low as 25 ms, but this was tested only in short experimental sessions. Real life usage of touchscreens covers much longer periods. It provides training which could lead to reduce the impact of latency. We investigate users ability to compensate for touch latency with training. Two groups of participants were trained on a tracking task during ten different days over two weeks with either high or low latency. The gap of performances between the two groups, observed at the beginning of the experiment, was reduced by 54 % after training. Users can thus compensate for latency, at least partially. These results nuance the negative effects of touch latency reported in previous work. They suggest that long-term studies could provide better insights on users behaviors when dealing with touch latency.