Quantifying Human Coordination in HCI

Maurice R. Masliah

Ergonomics in Teleoperation and Control (ETC) Lab

Department of Mechanical and Industrial Engineering

University of Toronto, Ontario Canada, M5S 3G8

+1 416 978 3776

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This poster provides a conceptual contribution to the understanding of how to evaluate performance for computer applications that require users to simultaneously control multiple degrees of freedom (dof). Whereas previous metrics from the literature, efficiency and integrality, are measurements in the space and time domains respectively, coordination should be measured in both dimensions. This poster proposes a definition of coordination and hypothesizes as to what will be found.


coordination, performance, integrality, efficiency, manual control, multiple degrees of freedom, input devices


As computer power continues to grow, high-end computer applications such as computer-aided design, scientific data visualization, computer graphics animation, telerobotics, virtual reality, and 3D video games are becoming increasingly common. To be effective, these applications require the user to continuously control multiple degrees of freedom (dof), a highly coordinated task. This is a very different type of interaction than the discrete single-key-press interactions of word processing and spreadsheet applications. Not only are the tasks more complex, but so too are the devices with which operators interact.


In its report [1] the National Research Councilís Committee on Virtual Reality Research and Development stressed the need for ways of evaluating manual interfaces and stated that the current technology has not been adequately assessed. New products which allow users to manually interact with computers are regularly appearing on the market, yet little is being done to quantify the usability of these devices. Clearly, as human-computer interactions becomes more complex the need for evaluating multiple dof devices will become even more important.

Previous Metric: Integrality

Jacob et al. [2], discussed the degree to which input devices with multiple dof can be characterized as integral based on whether it is easy or even possible to move diagonally across dofs. In Figure 1, Device A is showing evidence of "city-block", or stair-step, movement between the x and y dof. Device B exhibits what Jacob et al. termed "Euclidean" movement, movement that cuts diagonally across the dof.

Integrality is measured by first segmenting into equal time units the trajectory of each dof [2]. Each segment is then checked for presence or absence of movement above an arbitrarily chosen threshold. For each time segment, trajectory movement in all dof classifies that segment as Euclidean. The end result is a ratio of Euclidean to city-block movements for a given task. This ratio is a measure of the integrality of a given input device, which can then be compared to the integrality ratio of other input devices for the same task.

Integrality, however, does not distinguish between movements which contribute towards the goal and movements which do not.

Previous Metric: Efficiency

A coordinated movement is generally recognized as being an efficient movement. One proposed measure of efficiency is the length of a trajectory taken by a user, where the shortest possible trajectory is considered to be the most efficient. Zhai & Milgram [3] have proposed equating coordination to efficiency in this manner. If there exists an ideal or an optimal trajectory, the userís actual trajectory can be compared to the optimal. The ratio of the userís trajectory to the optimal trajectory is used as an inverse measure of coordination.


Essentially, integrality is a measure of the simultaneity of motion among multiple dof. Integrality is measurement in the time domain only, and says nothing about magnitude or the direction of the movement. Efficiency, on the other hand, is measurement in the spatial domain only, and says nothing about the time course of the movements made. This poster argues that a more complete metric of coordination must take into account measurement in both time and space.

Figure 2. A taxonomy of time-space manual control tasks


In a multiple dof continuous movement task, it is possible to have two movements with equal performance scores, but with very different time-space trajectories. The purpose of a coordination metric is to provide a measure that "tells us something" about the trajectory taken by a user.

Coordination and Performance

Figure 2 presents a taxonomy of different manual control tasks where what is measured is a function of the type of task. Like other performance measures, a measure of coordination should vary with the task. For coordination, a measure is desired which quantifies a personís ability to simultaneously manipulate multiple dof in a task-specific way. The following definition is therefore proposed:

Coordinated movement is the simultaneous control along multiple degrees of freedom, which results in an efficient trajectory.




The trajectory data from a single 6 dof tracking trial from one subject using an isometric spaceball in rate control mode is graphed in Figure 3. See [4] for more on the experimental protocol. By grouping data into translation and rotation subsets, patterns of coordination are observed. Such data corroborate with first-hand observation of subjects switching control between the subsets in order to reduce the complexity of the task.

Incorporating both time and space into a two dimensional measure of coordination is currently under developement.


It is hypothesized that novices will find it difficult to simultaneously coordinate a high number of dof. To reduce the complexity of the task it is predicted that subjects will control subsets of the dof, with the translation and rotation subset being the natural groupings. Higher coordination scores will therefore be measured higher within translational and rotation dof, than between translational and rotational dof.

It also remains to be seen whether operators perform as a multi- or single-channel system in high dof tasks. With practice, do people increase their ability to coordinate multiple dof in parallel, or are they able to switch between channels at a faster frequency?


Thanks to Paul Milgram, Scott MacKenzie, Julius Grodski and the Input Research Group. at the University of Toronto.


[1] Durlach, N. I., and Mavor, A. S. ed., Virtual Reality: Scientific and Technological Challenges, (Washington, DC: National Academy Press, (1995).

[2] Jacob, R. J. K., Sibert, L. E., McFarlane, D. C., and Mullen, M. P. JR. Integrality and Separability of Input Devices, ACM Transactions on Computer-Human Interaction 1,1 (1994), 3-26.

[3] Zhai, S. and Milgram, P. Quantifying Coordination in Multiple DOF Movement and Its Application to Evaluating 6 DOF Input Devices, In Proceedings of the Conference on Human Factors in Computing Systems CHI '98. ACM, 1998, pp. 320-327.

[4] Zhai, S., and Senders, J. W. Investigating coordination in multidegree of freedom control I: time-on-target analysis of 6 DOF tracking, In 41st Annual Meeting of Human Factors and Ergonomics Society. 1997, pp. 1249-1253.