Interactions Under the Desk: A Characterisation of Foot Movements for Input in a Seated Position

Velloso, Eduardo; Alexander, Jason; Bulling, Andreas; Gellersen, Hans

doi:10.1007/978-3-319-22701-6_29

Cited by 24 publications

(11 citation statements)

References 25 publications

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“…The previous projects demonstrated how the same principle of motion correlation can be used with two different modalities, namely gaze and mid-air hand gestures. However, it is easy to imagine how the technique could work with other human movement modalities, such as head [35] or feet movements [58]. With this in mind, we developed TraceMatch, a computer vision technique that searches for matching movements in a video feed, effectively enabling interaction based on motion correlation regardless of the modality being used.…”

Section: Tracematchmentioning

confidence: 99%

Motion Correlation

Velloso

Carter

Newn

et al. 2017

ACM Trans. Comput.-Hum. Interact.

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Selection is a canonical task in user interfaces, commonly supported by presenting objects for acquisition by pointing. In this article, we consider motion correlation as an alternative for selection. The principle is to represent available objects by motion in the interface, have users identify a target by mimicking its specific motion, and use the correlation between the system’s output with the user’s input to determine the selection. The resulting interaction has compelling properties, as users are guided by motion feedback, and only need to copy a presented motion. Motion correlation has been explored in earlier work but only recently begun to feature in holistic interface designs. We provide a first comprehensive review of the principle, and present an analysis of five previously published works, in which motion correlation underpinned the design of novel gaze and gesture interfaces for diverse application contexts. We derive guidelines for motion correlation algorithms, motion feedback, choice of modalities, overall design of motion correlation interfaces, and identify opportunities and challenges identified for future research and design.

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Section: Tracematchmentioning

confidence: 99%

Motion Correlation

Velloso

Carter

Newn

et al. 2017

ACM Trans. Comput.-Hum. Interact.

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“…Previous studies which have evaluated the ability for a single human subject to coordinate three hands together (trimanual activities) include 12 , who studied re-sizing, 13,14 who demonstrated the ability to control three hands with the natural limb and 15 who investigated the impact of adding a third hand to different bi-manual tasks of varying coupling level. From these studies it has been observed that human subjects can control three hands simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Human Performance in Three-hands Tasks

Noccaro

Eden

Pino

et al. 2021

Preprint

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The successful completion of complex tasks like hanging a picture or laparoscopic surgery requires coordinated motion of morethan two limbs. User-controlled supernumerary robotic limbs (SL) have been proposed to bypass the need for coordination witha partner in such tasks. However, neither the capability to control multiple limbs alone relative to collaborative control with partners, nor how that capability varies across different tasks, is well understood. In this work, we present an investigation of tasks requiring three-hands where the foot was used as an additional source of motor commands. We considered: i) how does simultaneous control of three hands compare to a cooperating dyad; ii) how this relative performance was altered by the existence of constraints emanating from real or virtual physical connections (mechanical constraints) or from cognitive limits (cognitive constraints). It was found that a cooperating dyad outperformed a single user in all scenarios in terms of tasks core, path efficiency and motion smoothness. However, while the participants were able to reach more targets with increasing mechanical constraints/decreasing number of simultaneous goals, the relative difference in performance between a dyad and a participant performing trimanual activities decreased, suggesting further potential for SLs in this class of scenario.

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“…Related work generally assumes use of the hands for pointing (e.g., [61,52,5,34,13]) but work in other areas has shown that humans are equally natural at pointing with other parts of their body (literally, from head [45,38] to toe [58]). We reflect this in an approach that is input-agnostic and supports any body movement to be adopted for pointing, contrasting existing systems that are optimised for specific modalities such as tracking of hand gestures [48], head pose [54], or feet [56].…”

Section: Background and Related Workmentioning

confidence: 99%

MatchPoint

Clarke

Gellersen

2017

Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology

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Figure 1. Spontaneous spatial coupling is a hybrid technique of motion-matching and pointing. Controls in the form of moving targets are presented to the user (A). When the user synchronises their movement with a target (B), a spatial coupling is created between the input modality (C) and the control (D). The technique enables ad hoc appropriation of any part of their body, or any object they hold, as a pointing device.

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Interactions Under the Desk: A Characterisation of Foot Movements for Input in a Seated Position

Cited by 24 publications

References 25 publications

Motion Correlation

Motion Correlation

Human Performance in Three-hands Tasks

MatchPoint

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