Gesture Recognition System using Optical Muscle Deformation Sensors

Hosono, Satoshi; Nishimura, Shoji; Iwasaki, Kei; Tamaki, Emi

doi:10.1145/3324033.3324037

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Numerous studies have been reported in the literature on decoding hand posture information using muscle deformation. Tamaki [24] identified three types of proximal interphalangeal (PIP) joint angles by detecting small muscle deformations using a photoreflector sensor. Kato [25] realized continuous control of the prosthetic wrist by using the deformation of skin surface muscles.…”

Section: Introductionmentioning

confidence: 99%

A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles

Wang

Zheng

Yang

et al. 2022

Sensors

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It is a considerable challenge to realize the accurate, continuous detection of handgrip strength due to its complexity and uncertainty. To address this issue, a novel grip strength estimation method oriented toward the multi-wrist angle based on the development of a flexible deformation sensor is proposed. The flexible deformation sensor consists of a foaming sponge, a Hall sensor, an LED, and photoresistors (PRs), which can measure the deformation of muscles with grip strength. When the external deformation squeezes the foaming sponge, its density and light intensity change, which is detected by a light-sensitive resistor. The light-sensitive resistor extended to the internal foaming sponge with illuminance complies with the extrusion of muscle deformation to enable relative muscle deformation measurement. Furthermore, to achieve the speed, accuracy, and continuous detection of grip strength with different wrist angles, a new grip strength-arm muscle model is adopted and a one-dimensional convolutional neural network based on the dynamic window is proposed to recognize wrist joints. Finally, all the experimental results demonstrate that our proposed flexible deformation sensor can accurately detect the muscle deformation of the arm, and the designed muscle model and convolutional neural network can continuously predict hand grip at different wrist angles in real-time.

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Section: Introductionmentioning

confidence: 99%

A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles

Wang

Zheng

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, different degrees of deformation can be simulated by tracking the reflective ball attached to the surface [7,13]. There are some other related work, such as the use of optical sensors [14,15] and the use of fiber-based deformation Electronics 2021, 10, 2991 2 of 13 sensor clusters [16] to detect human intentional deformation, etc. Our implementation scheme is based on the OptiTrack motion capture system.…”

Section: Introductionmentioning

confidence: 99%

The Study of Bending and Twisting Input Modalities in Deformable Interfaces

et al. 2021

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The deformable input provides users with the ability of physical operation equipment to interact with the system. In order to facilitate further development in flexible display interactive technology, we devised FlexSheet, an input device that can simulate the deformation environment. This paper presents two forms of deformation input, bending and twisting, with regard to three selection techniques. We conduct a controlled experiment to select discrete targets by combining two input forms and three selection strategies, taking into account the influence of visual feedback. Further, we use the deformation angle to reflect the degree of deformation and map it to the experimental variables. In accordance with the experimental results, we analyze the experimental performance under three evaluation indexes and prove the viability of our selection technology in bending and twisting input modes. Finally, we provide suggestions on the control level in bending and twisting input modes, respectively.

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