Head-tracking interfaces on mobile devices: Evaluation using Fitts’ law and a new multi-directional corner task for small displays

Roig-Maimó, Maria Francesca; MacKenzie, I. Scott; Manresa-Yee, Cristina; Varona, Javier

doi:10.1016/j.ijhcs.2017.12.003

Cited by 32 publications

(28 citation statements)

References 7 publications

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“…The grand mean for throughput was 1.55 bps. This value is within the expected range for head input on mobile and desktop environments (from 1.28 bps to 2.10 bps [MFM15,DSLKT03,RMMMYV18]).…”

Section: Throughputsupporting

confidence: 73%

Collateral effects of the Kalman Filter on the Throughput of a Head-Tracker for Mobile Devices

Roig-Maimó¹,

Mas-Sansó²

2019

Computer Science Research Notes

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We have developed an image-based head-tracker interface for mobile devices that uses the information of the front camera to detect and track the user's nose position and translate its movements into a pointing metaphor to the device. However, as already noted in the literature, the measurement errors of the motion tracking leads to a noticeable jittering of the perceived motion. To counterbalance this unpleasant and unwanted behavior, we have applied a Kalman filter to smooth the obtained positions. In this paper we focus on the effect that the use of a Kalman filter can have on the throughput of the interface. Throughput is the human performance measure proposed by the ISO 9241-411 for evaluating the efficiency and effectiveness of non-keyboard input devices. The softness and precision improvements that the Kalman filter infers in the tracking of the cursor are subjectively evident. However, its effects on the ISO's throughput have to be measured objectively to get an estimation of the benefits and drawbacks of applying a Kalman filter to a pointing device.

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“…The grand mean for throughput was 1.55 bps. This value is within the expected range for head input on mobile and desktop environments (from 1.28 bps to 2.10 bps [MFM15,DSLKT03,RMMMYV18]).…”

Section: Throughputsupporting

confidence: 73%

Collateral effects of the Kalman Filter on the Throughput of a Head-Tracker for Mobile Devices

Roig-Maimó¹,

Mas-Sansó²

2019

Computer Science Research Notes

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“…The grand mean for throughput using the non-ISO task was 0.74 bps (or around 0.81 bps if throughput is recalculated by removing the reaction time from the movement time). This value is low compared to the value of throughput of 1.42 bps obtained for the same head-tracking interaction method using the ISO task [11]. The increase of throughput using the ISO task, together with the low correlation values obtained in the regression models, may suggest that the Face Me task is ill-suited for the evaluation of Fitts' law performance.…”

Section: Discussionmentioning

confidence: 63%

Evaluating fitts' law performance with a non-ISO task

Roig-Maimó

MacKenzie

Manresa-Yee

et al. 2017

Proceedings of the XVIII International Conference on Human Computer Interaction

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We used a target-selection task to evaluate head-tracking as an input method on a mobile device. The procedure used a non-ISO Fitts' law task since targets were randomly positioned from trial to trial. Due to a non-constant amplitude within each sequence of trials, throughput was calculated using two methods of data aggregation: by sequence of trials using the mean amplitude and by common A-W conditions. For each data set, we used four methods for calculating throughput. The grand mean for throughput calculated using the division of means and the adjustment for accuracy was 0.74 bps, which is 45% lower than the value obtained using an ISO task. We recommend calculating throughput using the division of means (and not the slope reciprocal from the regression model) and with the adjustment for accuracy. We present design recommendation for non-ISO tasks: Keep amplitude and target width constant within each sequence of trials and use strategies to avoid or remove reaction time. CCS CONCEPTS • Human-centered computing → User studies; HCI theory, concepts and models;

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“…The grand mean for throughput using the non-ISO task was 0.74 bps (or around 0.81 bps if throughput is recalculated by removing the reaction time from the movement time). This value is low compared to the value of throughput, which is 1.42 bps, and it was obtained using the same head-tracking interaction method of the ISO task [13]. The increase of throughput using the ISO task together with the low correlation values obtained in the regression models may suggest that the "Face Me" task is ill-suited for the evaluation of Fitts' law performance.…”

Section: Discussionmentioning

confidence: 75%

Fitts’ Law: On Calculating Throughput and Non-ISO Tasks

Roig-Maimó¹,

MacKenzie²,

Manresa-Yee³

et al. 2018

Rev. colomb. comput.

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Resumen. En este trabajo se presenta el uso de una tarea de selección de objetivos en la evaluación de un head-tracker para dispositivos móviles. Primero, se describe el método de cálculo del rendimiento mediante un ejemplo detallado. A continuación, se discute el método de cálculo para tareas que no cumplen el estándar ISO. Debido a la amplitud no constante de la tarea dentro de cada secuencia, se calcula el rendimiento utilizando dos métodos de agregación de datos: por secuencia, calculando la amplitud media, y por condiciones comunes A-W. Se recomienda calcular el rendimiento utilizando la división de medias y el ajuste de precisión. La media general de rendimiento ha sido de 0,74 bps (un 45 % menor que con una tarea ISO). Se presentan dos recomendaciones de diseño para tareas que no cumplen el estándar ISO: mantener constantes A-W dentro de cada secuencia y utilizar estrategias para evitar el tiempo de reacción.Palabras clave: Ley de Fitts, rendimiento, ISO 9241-411, HCI móvil, head-tracking.Abstract. We used a target-selection task to evaluate head-tracking as an input method for mobile devices. First, the method of calculating Fitts' throughput is described by means of a raw data detailed example. Then, the method of calculating throughput is discussed for non-ISO tasks, since the procedure targets were randomly positioned from trial to trial. Due to a non-constant amplitude within each sequence of trials, throughput was calculated using two methods of data aggregation: the first one, by sequence of trials using the mean amplitude, and the second one, by common A-W conditions.For each data set, we used four methods for calculating throughput. The grand mean for throughput (calculated through the division of means and the adjustment for accuracy) was of 0.74 bps, which is 45 % lower than the value obtained using an ISO task. We recommend to calculate throughput using the division of means plus the adjustment for accuracy, and to avoid using the reciprocal slope of the regression model. We present various design recommendations for non-ISO tasks, such as: i) to keep amplitude and constant target within each sequence of trials, and ii) to use strategies to avoid or remove reaction time.

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Head-tracking interfaces on mobile devices: Evaluation using Fitts’ law and a new multi-directional corner task for small displays

Cited by 32 publications

References 7 publications

Collateral effects of the Kalman Filter on the Throughput of a Head-Tracker for Mobile Devices

Collateral effects of the Kalman Filter on the Throughput of a Head-Tracker for Mobile Devices

Evaluating fitts' law performance with a non-ISO task

Fitts’ Law: On Calculating Throughput and Non-ISO Tasks

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