Laboratory of Informatics of Grenoble Équipe Ingénierie de l'Interaction Humain-Machine

Équipe Ingénierie de l'Interaction
Humain-Machine

Analyse de la latence et de sa compensation pour l’interaction au toucher direct : aspects techniques et humains

pages 1-158. 2017.

Élie Cattan

Université Grenoble Alpes (Eds.)

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.

Our 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.

We 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.