A survey on Human Computer Interaction Mechanism Using Finger Tracking

Shah, Kinjal; Rathod, Kirit R.; Agravat, Shardul J.

doi:10.14445/22312803/ijctt-v7p148

Cited by 18 publications

(7 citation statements)

References 4 publications

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“…Methods are proposed based on performed tests on face databases with variations in terms of subjects, poses, emotions, races, and light conditions. Despite these studies, self- expression of these facial expressions is not sufficient to recognize nose-based gestures: finger movement recognition [ 64 ] should be involved. Facial expression recognition combined with finger movement recognition should enable us to recognize more nose-based gestures being inspired by our taxonomy ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

A Gesture Elicitation Study of Nose-Based Gestures

Pérez-Medina

Villarreal

Vanderdonckt

2020

Sensors

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Presently, miniaturized sensors can be embedded in any small-size wearable to recognize movements on some parts of the human body. For example, an electrooculography-based sensor in smart glasses recognizes finger movements on the nose. To explore the interaction capabilities, this paper conducts a gesture elicitation study as a between-subjects experiment involving one group of 12 females and one group of 12 males, expressing their preferred nose-based gestures on 19 Internet-of-Things tasks. Based on classification criteria, the 912 elicited gestures are clustered into 53 unique gestures resulting in 23 categories, to form a taxonomy and a consensus set of 38 final gestures, providing researchers and practitioners with a larger base with six design guidelines. To test whether the measurement method impacts these results, the agreement scores and rates, computed for determining the most agreed gestures upon participants, are compared with the Condorcet and the de Borda count methods to observe that the results remain consistent, sometimes with a slightly different order. To test whether the results are sensitive to gender, inferential statistics suggest that no significant difference exists between males and females for agreement scores and rates.

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Section: Resultsmentioning

confidence: 99%

A Gesture Elicitation Study of Nose-Based Gestures

Pérez-Medina

Villarreal

Vanderdonckt

2020

Sensors

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“…Many other studies present positive results from the use of AR for learning musical instruments as: piano [22], guitar [23] and percussion [24].…”

Section: Related Workmentioning

confidence: 98%

Teaching Children Musical Perception with MUSIC-AR

Martins¹,

Gomez²,

Corrêa³

2015

EAI Endorsed Transactions on e-Learning

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Unfortunately in Brazil there is a non compulsory musical education in schools that leads to loss of sound/musical perception of Brazilian children. This fact, associated with the lack of software for the teaching of musical perception, inspired the creation of Music-AR, a set of software that uses Augmented Reality technology for the teaching of sound properties, such as timbre, pitch and sound intensity. There were four small applications for that: the first one allows the child to manipulate virtual objects linked to sounds, this way, the child can loosen and stretch virtual objects relating them to the (bass and treble) sound pitch; the second focus on the concept of sound intensity, associating it to virtual animals been far or near to the children; the third is related to duration of the sound (short or long), and the last is about timbrethe personality of the sound. Tests were applied and the results are presented in this work.

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“…In a mobile context, utilization of the touch screen is also commonplace, yet, suffers from issues, too -such as occlusion of the screen and ergonomics [7]. Researchers therefore started exploring finger tracking for handheld AR interaction as well (e.g., [5,6,7]; see [13] for an overview of different scenarios, including but not limited to handheld AR). When comparing touch versus finger tracking, Hürst et al [7] showed that the latter often suffers in performance, likely due to a lack of haptic feedback.…”

Section: Context and Related Workmentioning

confidence: 99%

Multimodal feedback for finger-based interaction in mobile augmented reality

Hürst

Vriens

2016

Proceedings of the 18th ACM International Conference on Multimodal Interaction

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Mobile or handheld augmented reality uses a smartphone's live video stream and enriches it with superimposed graphics. In such scenarios, tracking one's fingers in front of the camera and interpreting these traces as gestures offers interesting perspectives for interaction. Yet, the lack of haptic feedback provides challenges that need to be overcome. We present a pilot study where three types of feedback (audio, visual, haptic) and combinations thereof are used to support basic finger-based gestures (grab, release). A comparative study with 26 subjects shows an advantage in providing combined, multimodal feedback. In addition, it suggests high potential of haptic feedback via phone vibration, which is surprising given the fact that it is held with the other, non-interacting hand.

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A survey on Human Computer Interaction Mechanism Using Finger Tracking

Cited by 18 publications

References 4 publications

A Gesture Elicitation Study of Nose-Based Gestures

A Gesture Elicitation Study of Nose-Based Gestures

Teaching Children Musical Perception with MUSIC-AR

Multimodal feedback for finger-based interaction in mobile augmented reality

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