EarRumble: Discreet Hands- and Eyes-Free Input by Voluntary Tensor Tympani Muscle Contraction

Röddiger, Tobias; Clarke, Christopher; Wolffram, Daniel; Budde, Matthias; Beigl, Michael

doi:10.1145/3411764.3445205

Cited by 12 publications

(4 citation statements)

References 50 publications

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“…Bedri et al [5] proposed a method that recognizes the movement of the jaw by using an infrared distance sensor attached to the tip of a canal-type earphone. An input method that uses canal-type earphones to acquire sound using a small muscle in the middle ear called the tensor tympani muscle has been proposed [10]. There is an ear-worn device with which users use their ears as a hand-held input controller [13].…”

Section: Gesture Recognition Methods Using Ear Accessoriesmentioning

confidence: 99%

“…As the shape and wearing of ear accessories are socially acceptable, devices that use them as controllers for hands-free input are likely to be accepted by society. Gesture input methods based on intentional face movements using sensors (e.g., barometers, microphones, infrared distance sensors and electrodes) mounted on canal-type earphones have been proposed in previous studies [4][5][6][7][8][9][10]. Such gesture input methods based on facial movements using ear accessories is effective as a simple input method in scenarios where the number of input contents is not large and can be used even in situations where it is difficult to use the hands-free input method using voice recognition (e.g., scenes with a lot of environmental voices and a high psychological load of speaking).…”

Section: Introductionmentioning

confidence: 99%

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Augmenting Ear Accessories for Facial Gesture Input Using Infrared Distance Sensor Array

2022

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Simple hands-free input methods using ear accessories have been proposed to broaden the range of scenarios in which information devices can be operated without hands. Although many previous studies use canal-type earphones, few studies focused on the following two points: (1) A method applicable to ear accessories other than canal-type earphones. (2) A method enabling various ear accessories with different styles to have the same hands-free input function. To realize these two points, this study proposes a method to recognize the user’s facial gesture using an infrared distance sensor attached to the ear accessory. The proposed method detects skin movement around the ear and face, which differs for each facial expression gesture. We created a prototype system for three ear accessories for the root of the ear, earlobe, and tragus. The evaluation results for nine gestures and 10 subjects showed that the F-value of each device was 0.95 or more, and the F-value of the pattern combining multiple devices was 0.99 or more, which showed the feasibility of the proposed method. Although many ear accessories could not interact with information devices, our findings enable various ear accessories with different styles to have eye-free and hands-free input ability based on facial gestures.

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Section: Gesture Recognition Methods Using Ear Accessoriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Augmenting Ear Accessories for Facial Gesture Input Using Infrared Distance Sensor Array

2022

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“…As another promising direction, research proposed various handsfree body-centric interaction techniques that leverage the degrees of freedom of other body parts, such as the head [35,108] and the eye [63,75]. Further examples include the ear [84], the mouth [26,86], and the face [66], as well as interpreting blow gestures [10,82] or combinations of multiple body parts [24,64]. As the most related area of such hands-free body-centric interfaces, foot-based interaction techniques have a long tradition in the operation of industrial machinery [3,4,15,50,77].…”

Section: Body-centric Interactionmentioning

confidence: 99%

TicTacToes: Assessing Toe Movements as an Input Modality

Müller

Schmitt

Matviienko

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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In this work, we investigate the feasibility of a toe-based input modality for interacting with computing systems. For this, we investigate the influence of five factors on the accuracy, efficiency, and user expierience of such interfaces. In addition to the direction and the toes as illustrated in this figure, we further investigate the influence of the posture, the scaling of the interaction range and the location of the target.

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“…And mouth-related interface (Whoosh [38], TieLent [20]) such as tongue interface [11,24] and teeth interface (TeethTap [43],Bitey [2], EarSense [37]) also offers users a novel way for hands-free input. What's more, ear-based interaction (EarRumble [39]), waist gestures (HulaMove [48]) and foot gestures (FootUI [17], FEETICHE [26]) are also explored by researchers. Compared with existing work, head gestures present another way for hands-free input like navigation in 3D space [5].…”

Section: Hands-free Gesture Inputmentioning

confidence: 99%

HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece

Xu¹,

Wang²,

Fang³

et al. 2022

Preprint

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Fig. 1. Application scenarios of HeadText. Fig.a shows that users could perform head gestures to respond to messages when they are eating and their hands are holding the cake. Fig. b presents that a user could use HeadText to input text on a smart watch, whose text entry is originally limited due to the small screen size. Fig.c shows that HeadText could be utilized to help users with motor impairments for text entry. Fig. d presents the application scenario where users could perform head gestures for text entry on the smart glass which is private and socially acceptable in public. We present HeadText, a hands-free technique on a smart earpiece for text entry by motion sensing. Users input text utilizing only head gestures for key selection, word selection, word commitment and word cancelling tasks. Head gesture recognition is supported by motion sensing on a smart earpiece to capture head moving signals and machine learning algorithms (K-Nearest-Neighbor (KNN)with a Dynamic Time Warping (DTW) distance measurement). A 10-participant user study proved that HeadText could recognize head gestures at an accuracy of 94.29%. After that, the second user study presented that HeadText could achieve a maximum accuracy

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EarRumble: Discreet Hands- and Eyes-Free Input by Voluntary Tensor Tympani Muscle Contraction

Cited by 12 publications

References 50 publications

Augmenting Ear Accessories for Facial Gesture Input Using Infrared Distance Sensor Array

Augmenting Ear Accessories for Facial Gesture Input Using Infrared Distance Sensor Array

TicTacToes: Assessing Toe Movements as an Input Modality

HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece

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