publications([{
   "lang": "en",
   "bibtype": "article",
   "doi": "https://doi.org/10.1007/s00221-018-5368-2",
   "uri": "http://iihm.imag.fr/publication/CPB+18a/",
   "title": "Adaptation to visual feedback delays on touchscreens with hand vision",
   "url": "http://iihm.imag.fr/publs/2018/EBR18_Cattan_AdaptDelay.pdf",
   "journal": "Experimental Brain Research",
   "year": 2018,
   "number": 12,
   "pages": "3191–3201",
   "volume": 236,
   "id": 826,
   "abbr": "CPB+18a",
   "authors": {
      "1": {
         "first_name": "Élie",
         "last_name": "Cattan"
      },
      "2": {
         "first_name": "Pascal",
         "last_name": "Perrier"
      },
      "3": {
         "first_name": "François",
         "last_name": "Bérard"
      },
      "4": {
         "first_name": "Silvain",
         "last_name": "Gerber"
      },
      "5": {
         "first_name": "Amélie",
         "last_name": "Rochet-Capellan"
      }
   },
   "date": "2018-09-06",
   "type": "Revues internationales avec comité de lecture",
   "abstract": "Direct touch finger interaction on a smartphone or a tablet is now ubiquitous. However, the latency inherent in digital computation produces an average feedback delay of ~75 ms between the action of the hand and its visible effect on digital content. This delay has been shown to affect users’ performance but it is unclear whether users adapt to this delay or whether it influences skill learning. Previous work studied adaptation to feedback delays but only for longer delays, with hidden hand or indirect devices. This paper addresses adaptation to touchscreen delay in two empirical studies involving the tracking of a target moving along an elliptical path. Participants were trained for the task either at the minimal delay the system allows (~9 ms) or at a longer delay equivalent to commercialized touch devices latencies (75 ms). After 10 training sessions over a minimum of two weeks (Experiment 1), participants adapt to the delay. They also display long-term retention seven weeks after the last training session. This adaptation generalized to a similar tracking path (e.g. infinity symbol). We also observed generalization of learning from the longer delay to the minimal delay condition (Experiment 2). The delay thus does not prevent the learning of tracking skill, which suggests that delay adaptation and tracking skill could be two separate components of learning.",
   "type_publi": "irevcomlec"
},
{
   "lang": "en",
   "type_publi": "icolcomlec",
   "doi": "http://dx.doi.org/10.1145/3025453.3025585",
   "title": "Does Practice Make Perfect? Learning to Deal with Latency in Direct-Touch Interaction",
   "abstract": "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.\r\n\r\nWe 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.",
   "authors": {
      "1": {
         "first_name": "Élie",
         "last_name": "Cattan"
      },
      "2": {
         "first_name": "Amélie",
         "last_name": "Rochet-Capellan"
      },
      "3": {
         "first_name": "Pascal",
         "last_name": "Perrier"
      },
      "4": {
         "first_name": "François",
         "last_name": "Bérard"
      }
   },
   "year": 2017,
   "uri": "http://iihm.imag.fr/publication/CRP+17a/",
   "pages": "5619-5629",
   "bibtype": "inproceedings",
   "id": 784,
   "abbr": "CRP+17a",
   "address": "Denver, USA",
   "date": "2017-05-30",
   "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet",
   "booktitle": "Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems"
},
{
   "lang": "fr",
   "type_publi": "these",
   "title": "Analyse de la latence et de sa compensation pour l’interaction au toucher direct : aspects techniques et humains",
   "abstract": "Latency, the delay between a user input on a system and the corresponding response from the system, is a major issue for the usability of interactive systems. In direct-touch interaction, latency is particularly perceivable and alters user performance even at levels in the order of ten milliseconds. Yet, current touch devices such as smartphones or tablet-pc exhibit in general latencies over 70~ms.\r\n\r\nOur goal is to improve the knowledge on latency (its causes, its effects) and to find strategies to compensate it or to decrease its negative effects. We present a review of the HCI literature on the topic, then we link this literature with the motor control research field that has studied human behaviour when facing visuomotor perturbations, and in particular the adaptation to feedback delay.\r\n\r\nWe then present our four contributions. We contribute both in a practical and a theoretical manner to the problem of latency in direct-touch interaction. Two of our contributions supplement the diagnosis of latency: the first one is a new latency measurement technique; the second one is a study of the impact of latency on bimanual interaction, which is important when interacting on large tactile surfaces. We show that bimanual interaction is as much affected by latency as a single hand interaction, suggesting that more complex tasks, suppose to increase the cognitive load, do not necessarily reduce the effect of latency. Our two other contributions address the reduction of the effects of latency. On one hand, we introduce a low latency system (25~ms) associated with a predictive software compensation, and we show that the system enables users to improve their performances as if they were using a system with 9~ms of latency. On the other hand we study users' ability to adapt to latency in order to improve their performance on a tracking task, and we show that the negative impact of latency is reduced with long-term training thanks to human adaptability.",
   "year": 2017,
   "uri": "http://iihm.imag.fr/publication/C17a/",
   "id": 803,
   "bibtype": "phdthesis",
   "editor": "Université Grenoble Alpes",
   "authors": {
      "1": {
         "first_name": "Élie",
         "last_name": "Cattan"
      }
   },
   "date": "2017-09-26",
   "document": "http://iihm.imag.fr/publs/2017/these_final_pdf_compressed.pdf",
   "type": "Thèses et habilitations",
   "pages": "1-158",
   "abbr": "C17a"
},
{
   "lang": "en",
   "type_publi": "icolcomlec",
   "doi": "https://doi.org/10.1145/2992154.2992160",
   "title": "Effect of Touch Latency on Elementary vs. Bimanual Composite Tasks",
   "abstract": "Touch latency has been shown to reduce users' performances but most studies focus on one-handed elementary tasks such as pointing or tracking a single object. The everyday use of touch devices is made, however, of more complex \"composite\" tasks combining several objects with potential bimanual interaction. Such a composite task may increase users' cognitive load which makes latency less perceivable. We thus expected that the impact of latency on users' performances should be smaller in composite tasks than in elementary tasks.\r\n\r\nWe tested this hypothesis by comparing the degradation effect of latency on users' performances in an elementary vs. a composite task. The elementary task consisted in positioning a single object. The composite task involved sorting and positioning objects with a two-handed interaction, inducing more complex planning and motor strategies that could be seen as an additional cognitive load. Contrary to expectations, the degradation effect was comparable in the two tasks. This study indicates that the substantial hindrance of latency, demonstrated on elementary tasks, also exists in more complex tasks that better represent the every day use of touch devices. This strengthens the motivation to question the interaction between the task properties and latency effect and to adapt commercial devices and applications accordingly.",
   "authors": {
      "1": {
         "first_name": "Élie",
         "last_name": "Cattan"
      },
      "2": {
         "first_name": "Amélie",
         "last_name": "Rochet-Capellan"
      },
      "3": {
         "first_name": "François",
         "last_name": "Bérard"
      }
   },
   "year": 2016,
   "uri": "http://iihm.imag.fr/publication/CRB16a/",
   "pages": "103-108",
   "bibtype": "inproceedings",
   "id": 773,
   "abbr": "CRB16a",
   "address": "Niagara Falls, Canada",
   "date": "2016-11-07",
   "document": "http://iihm.imag.fr/publs/2016/iss115-cattan.pdf",
   "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet",
   "booktitle": "Proceedings of the 2016 ACM International Conference on Interactive Surfaces and Spaces"
},
{
   "lang": "en",
   "type_publi": "icolcomlec",
   "doi": "https://doi.org/10.1145/2817721.2817736",
   "title": "Reducing Latency with a Continuous Prediction: Effects on Users' Performance in Direct-Touch Target Acquisitions",
   "abstract": "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.\r\n\r\nWe 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.",
   "authors": {
      "1": {
         "first_name": "Élie",
         "last_name": "Cattan"
      },
      "2": {
         "first_name": "Amélie",
         "last_name": "Rochet-Capellan"
      },
      "3": {
         "first_name": "Pascal",
         "last_name": "Perrier"
      },
      "4": {
         "first_name": "François",
         "last_name": "Bérard"
      }
   },
   "year": 2015,
   "uri": "http://iihm.imag.fr/publication/CRP+15a/",
   "pages": "205-214",
   "bibtype": "inproceedings",
   "id": 742,
   "abbr": "CRP+15a",
   "address": "Madeira, Portugal",
   "date": "2015-11-16",
   "document": "http://iihm.imag.fr/publs/2015/ITS15_prediction.pdf",
   "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet",
   "booktitle": "Proceedings of the 2015 ACM International Conference on Interactive Tabletops and Surfaces"
},
{
   "lang": "en",
   "type_publi": "icolcomlec",
   "doi": "https://doi.org/10.1145/2817721.2817747",
   "title": "A Predictive Approach for an End-to-End Touch-Latency Measurement",
   "abstract": "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.\r\n\r\nIn 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.",
   "authors": {
      "1": {
         "first_name": "Élie",
         "last_name": "Cattan"
      },
      "2": {
         "first_name": "Amélie",
         "last_name": "Rochet-Capellan"
      },
      "3": {
         "first_name": "François",
         "last_name": "Bérard"
      }
   },
   "year": 2015,
   "uri": "http://iihm.imag.fr/publication/CRB15a/",
   "pages": "215-218",
   "bibtype": "inproceedings",
   "id": 743,
   "abbr": "CRB15a",
   "address": "Madeira, Portugal",
   "date": "2015-11-16",
   "document": "http://iihm.imag.fr/publs/2015/ITS15_pred.pdf",
   "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet",
   "booktitle": "Proceedings of the 2015 ACM International Conference on Interactive Tabletops and Surfaces"
}]);