Impact Vibration Source Localization in Two-Dimensional Space Around Hand

Ujitoko, Yusuke; Tokuhisa, Ryunosuke; Sakurai, Shigeo; Hirota, Koichi

doi:10.1109/toh.2021.3085756

Cited by 5 publications

(26 citation statements)

References 39 publications

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“…With this method, the user needs to recognize the direction and distance of a remote vibration source based on the propagated vibration. We have investigated the accuracy of source localization with an impact vibration, and found that the direction could be recognized to some extent, but the distance could not be recognized accurately (Ujitoko et al, 2021). Based on the same idea as that proposed in our previous study (Ujitoko et al, 2021), the present study investigates the accuracy of source localization but this time using a sinusoidal vibration due to following reasons.…”

Section: Introductionmentioning

confidence: 86%

“…We have investigated the accuracy of source localization with an impact vibration, and found that the direction could be recognized to some extent, but the distance could not be recognized accurately (Ujitoko et al, 2021). Based on the same idea as that proposed in our previous study (Ujitoko et al, 2021), the present study investigates the accuracy of source localization but this time using a sinusoidal vibration due to following reasons. First, voice-coil motors, which are usually used to simulate sinusoidal vibrations, are a better option for tactile presentation than solenoids, which are usually used to simulate impulse vibrations, in the following three ways: power consumption (see Immersion, 2020), heat, and controllability (see Choi and Kuchenbecker, 2012).…”

Section: Introductionmentioning

confidence: 86%

“…On the other hand, in our previous study, we examined the localization ability of a hand placed on a two-dimensional medium when some place on the medium around the hand was presented with an impact vibration (Ujitoko et al, 2021). If the subjects can localize the vibration source, it will be possible to present not only distance but also directional information.…”

Section: Placing Vibrotactile Actuators Outside the Bodymentioning

confidence: 99%

“…In this study, we investigated the localization performance when using low-or high-frequency vibrations (30 Hz as low frequency and 230 Hz as high frequency) as a signal source, under similar experimental conditions to those used in our previous study (Ujitoko et al, 2021), including the layout of stimulus sources and the medium material. For the hand posture condition, we chose the condition where the whole hand was grounded on the medium, since the localization accuracy was higher with this posture than when only the five fingertips were grounded (Ujitoko et al, 2021).…”

Section: Placing Vibrotactile Actuators Outside the Bodymentioning

confidence: 99%

“…In our previous study, we proposed a method of presenting impact vibrations to the user's body from a remotely distributed vibrotactile array through a medium (Ujitoko et al, 2021). Since this method does not require the array to directly contact the body, it alleviates the problem of laying out the actuators in a limited space and the problem of heat generation.…”

Section: Introductionmentioning

confidence: 99%

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Sinusoidal Vibration Source Localization in Two-Dimensional Space Around the Hand

Ujitoko

Kuroki²

2022

Front. Psychol.

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There are use cases where presenting spatial information via the tactile sense is useful (e.g., situations where visual and audio senses are not available). Conventional methods that directly attach a vibrotactile array to a user's body present spatial information such as direction by having users localize the vibration source from among the sources in the array. These methods suffer from problems such as heat generation of the actuator or the installation cost of the actuators in a limited space. A promising method of coping with these problems is to place the vibrotactile array at a distance from the body, instead of directly attaching it to the body, with the aim of presenting spatial information in the same way as the conventional method. The present study investigates the method's effectiveness by means of a psychophysical experiment. Specifically, we presented users with sinusoidal vibrations from remote vibrotactile arrays in the space around the hand and asked them to localize the source of the vibration. We conducted an experiment to investigate the localization ability by using two vibration frequencies (30 Hz as a low frequency and 230 Hz as a high frequency). We chose these two frequencies since they effectively activate two distinctive vibrotactile channels: the rapidly adapting afferent channel and the Pacinian channel. The experimental results showed that humans can recognize the direction of the vibration source, but not the distance, regardless of the source frequency. The accuracy of the direction recognition varied slightly according to the vibration source direction, and also according to the vibration frequency. This suggests that the calibration of stimulus direction is required in the case of both high and low frequencies for presenting direction accurately as intended. In addition, the accuracy variance of direction recognition increased as the source became farther away, and the degree of increase was especially large with the low-frequency source. This suggests that a high frequency is recommended for presenting accurate direction with low variance.

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Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 86%

Section: Placing Vibrotactile Actuators Outside the Bodymentioning

confidence: 99%

Section: Placing Vibrotactile Actuators Outside the Bodymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sinusoidal Vibration Source Localization in Two-Dimensional Space Around the Hand

Ujitoko

Kuroki²

2022

Front. Psychol.

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An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

Palanisamy¹,

Thangaswamy²

2022

Concurrency and Computation

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Hand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human-computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state-of-art systems mainly used computer vision-based techniques that utilize both the motion sensor and camera to capture the hand gestures in real-time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi-scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN-LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi-block local binary pattern (MB-LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN-LSTM network is applied in the two-benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset-II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN-LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.

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Vibrotactile Spatiotemporal Pattern Recognition in Two-Dimensional Space Around Hand

Ujitoko

Tokuhisa

Hirota

2022

IEEE Trans. Haptics

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Impact Vibration Source Localization in Two-Dimensional Space Around Hand

Cited by 5 publications

References 39 publications

Sinusoidal Vibration Source Localization in Two-Dimensional Space Around the Hand

Sinusoidal Vibration Source Localization in Two-Dimensional Space Around the Hand

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

Vibrotactile Spatiotemporal Pattern Recognition in Two-Dimensional Space Around Hand

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