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Motor Performance Assessment and Its Implication for Display and Control Systems
Published in Mustapha Mouloua, Peter A. Hancock, James Ferraro, Human Performance in Automated and Autonomous Systems, 2019
Daniel S. McConnell, Michael A. Rupp
Subsequently, Mackenzie (1992) lobbied for the standardized use of Fitts’ law to evaluate input devices. This can be achieved by calculating an index of performance (IP), or throughput, for each device. From Fitts’ original work, IP = ID/MT (units of bits/s) and is interpreted as the bandwidth of the visuomotor system. Mackenzie (1992) also slightly modified Fitts’ law by revisiting information theory and arguing that the ID should more closely resemble Shannon's formula for the signal-to-noise ratio. Thus, Mackenzie wrote that ID = log2(D + W)/W, which is a formulation that remains popular among HCI researchers. The use of Fitts’ law as a standard for evaluating input devices was adopted as an ISO in 2002 (ISO9241-9) and was revised twice, most recently in 2012 (ISO/TS9241-411).
Make buttons large
Published in Michael Wiklund, Kimmy Ansems, Rachel Aronchick, Cory Costantino, Alix Dorfman, Brenda van Geel, Jonathan Kendler, Valerie Ng, Ruben Post, Jon Tilliss, Designing for Safe Use, 2019
Michael Wiklund, Kimmy Ansems, Rachel Aronchick, Cory Costantino, Alix Dorfman, Brenda van Geel, Jonathan Kendler, Valerie Ng, Ruben Post, Jon Tilliss
Fitts’s Law is a model related to “pointing” movement (i.e., accessing a target with a finger or a computer pointer) that describes the relationship between (1) the distance to the target, (2) the size of the target, and (3) the amount of time it takes to reach the target. The model states that the shorter the distance to the target and the larger the target itself, the faster a user’s hand or pointer can reach the target accurately.3 As you might expect, incorporating Fitts’s Law into button design for safety-critical tasks can enable efficient use and yield potential safety benefits. For example, an “emergency stop” button should be sufficiently large and relatively close to the work area (although not vulnerably placed) to enable a user to access it quickly and accurately in the event of an emergency.
Psychology of pointing: factors affecting the use of mice and trackballs on graphical user interfaces
Published in Don Harris, Engineering Psychology and Cognitive Ergonomics, 2017
Fitts’ law predicts that movement time will increase logarithmically with an increase in the ratio of distance from start point to target to target width. The assumption underlying this law was that highly skilled, i.e., well learned, fast and accurate, performance would be limited by the capacity of the human motor system to process information. In order for Fitts’ law to be viable as an explanation of human movement it is necessary to make to make the following assumptions (Walker et al., 1993): positioning movements can be decomposed into one or more discrete submovements;each submovement travels a constant proportion of the remaining distance to the target;each sub-movement has a constant duration;sensory feedback is used to guide sub-movements;sub-movement sequences terminate as soon as the positioning movement reaches the target
Comparison of Eye-Based and Controller-Based Selection in Virtual Reality
Published in International Journal of Human–Computer Interaction, 2021
Eye-tracking techniques have been widely used in medicine, psychology, marketing, and human factors. It is also used as a human–computer interface for applications such as gaze-based typing in a 2D environment (Majaranta & Räihä, 2002). An eye-based interaction using eye-tracking techniques has been paid attention to since around the early 1990s (Frey et al., 1990; Ware & Mikaelian, 1987). The development of eye tracker has made it possible to carry out an actual human-computer interaction (HCI) task using eye-based interaction. Target selection, or target acquisition, is a typical HCI task to measure the performance of different interaction modality, which often combines with Fitts’ law to predict and evaluate the performance of the input technique. Fitts’ law has served as one of the prominent quantitative law for human–computer interaction research and design.
Applying ergonomics within the multi-modelling paradigm with an example from multiple UAV control
Published in Ergonomics, 2020
David Golightly, Carl Gamble, Roberto Palacin, Ken Pierce
At the next level of rigour are those models offering predictive functions and algebraic relationships or ‘engineering models’. A classic instance of this is Fitts’ Law which predicts time taken to reach a target is a ratio of the distance to the target and size of the target. This can be expressed algebraically, with a number of variants (MacKenzie 1992). There is also the power law of learning, which expresses improved performance through practice as a logarithmic relationship (Siebel 1963; Ritter and Schooler 2001). Other examples include Teal and Rudnicky (1992), who use a psychologically motivated model of response time processing to predict the impact on the operator of system response delay. Engineering models also apply to physiological aspects and applications, such as models of the human thermoregulatory system (Hwang and Konz 1977).
Effects of Quantity and Size of Buttons of In-Vehicle Touch Screen on Drivers’ Eye Glance Behavior
Published in International Journal of Human–Computer Interaction, 2018
Fred Feng, Yili Liu, Yifan Chen
The size of buttons on a touch screen is also an important design parameter. Fitts’s Law (Fitts, 1954) implies that the time required to rapidly move to a target region increases with decreasing target width. Several recommendations have been given regarding the minimum button size on a touch screen (e.g., 19 mm (3/4 inch) by Monterey Technologies Inc. (1996) and 22 mm by Greenstein (1997)). The basic idea was that the button should be at least as big as the size of an adult human fingertip (typically 16–20 mm in diameter, Dandekar, Raju, & Srinivasan, 2003). Jin, Plocher, and Kiff (2007) studied the effect of touch screen button sizes, spacing, and manual dexterity on the reaction time, accuracy, and subjective preferences of older adults. They found that longer reaction times and lower accuracy were elicited with small buttons, but the increase of accuracy plateaued with larger button sizes.