A Stretchable Array of Electronic Receptors for Esophageal Swallowing Robot for Biomimetic Simulations of Bolus Transport

Din, Abdul Sattar; Wang, Xu; Cheng, Leo K.; Dirven, Steven

doi:10.1109/jsen.2018.2836330

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…Table 1 lists the purpose of developing the peristaltic pump, a summary, and a comparison with the regarding the function in hardware and software of the peristaltic mixing conveyor proposed in this paper. Table 1 compares three devices to peristaltic mixing conveyor devices: a tube pump that uses two rollers to generate peristaltic waves [17], a swallowing device that imitates the digestive tract of a living body [18] [19], and a pump using ballooning [20]. These compared devices are not intended for industrial use, such as the production of solid fuel.…”

Section: A Peristaltic Pumpmentioning

confidence: 99%

“…The purpose of this experiment was to verify the change in the state of the simulant during the mixing process and its mixing completion time to label the unmixed and mixed states of the simulant for use in supervised machine consisting of a premixed metal powder and a highly viscous fluid. They were used as simulant for solid fuel and were based on recipes in a previous study [19] (hereafter referred to as simulant). The materials were sealed in polyethylene bags (90mm × 300 mm, 0.08 mm thick) large enough to be mixed by the three units of the device for the experiment.…”

Section: A Outline Of Experimentsmentioning

confidence: 99%

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Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement

Terayama,

Oshino,

Nishihama

et al. 2024

IEEE Access

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The high frequency of rocket launches requires low-cost solid rocket fuel. Currently, the fuel manufacturing process faces increased launch costs caused by the risk of ignition from rotary mixers and increased equipment and labor costs from batch processes in which mixing and conveying are separated. Therefore, this paper proposes and verifies an automatic switching system between mixing and conveying modes for a peristaltic mixing conveyor that enables safe and continuous mixing and conveying of solid fuel. In a previous study, peristaltic mixing conveyor with low shear force was developed and successfully produced solid fuel. However, there was room for improvement for more efficient fuel production because the device was controlled by pre-determined driving pattern. The actual intestine generates movement autonomously by enteric nerves. Therefore, the development of a sensing function that imitates the enteric nervous system and generates movement patterns based on the acquired data is expected to improve manufacturing efficiency. In this study, the sensor data of a mixed solid fuel simulant packaged in a bag were acquired, and the degree of mixing (unmixed and mixed completely) was discriminated using supervised learning (the k-nearest neighbor method). Furthermore, a system was constructed to continue the mixing mode when unmixing and automatically switch the motion to the conveying mode when the mixing was complete. The experiment showed that the motion mode automatically switched to the conveying mode at almost the same time as the labeled training data, and mixing and conveying of the simulated material was successfully performed.

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Section: A Peristaltic Pumpmentioning

confidence: 99%

Section: A Outline Of Experimentsmentioning

confidence: 99%

Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement

Terayama,

Oshino,

Nishihama

et al. 2024

IEEE Access

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show abstract

“…In recent years, great breakthroughs have been achieved in the development of ML‐stretchable sensing systems for human–machine interfaces and healthcare. [ 19,80–97 ] To date, these reported ML‐stretchable sensing systems can be generally divided into three types ( Table 1 ): ML‐stretchable electrode systems, ML‐stretchable tactile sensor systems, and ML‐multiple sensor systems, which are used for bioelectrical signal recognition, haptic perception, and multimodal information integration, respectively.…”

Section: Ml‐stretchable Sensing Systemsmentioning

confidence: 99%

Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces

Wang

Luo

et al. 2021

Adv Funct Materials

110

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Sensors and algorithms are two fundamental elements to construct intelligent systems. The recent progress in machine learning (ML) has produced great advancements in intelligent systems, owing to the powerful data analysis capability of ML algorithms. However, the performance of most systems is still hindered by sensing techniques that typically rely on rigid and bulky sensor devices, which cannot conform to irregularly curved and dynamic surfaces for high‐quality data acquisition. Skin‐like stretchable sensing technology with unique characteristics, such as high conformability, low modulus, and light weight, has been recently developed to solve this issue. Here, the recent progress in the fusion of emerging stretchable electronics and ML technology, for bioelectrical signal recognition, tactile perception, and multimodal integration is summarized, and the challenges and future developments are further discussed. These efforts aim to accelerate various perception and reasoning tasks for advanced intelligent applications, such as human–machine interfaces, healthcare, and robotics.

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Experimental Investigation into the Dynamics of a Radially Contracting Actuator with Embedded Sensing Capability

et al. 2020

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A Stretchable Array of Electronic Receptors for Esophageal Swallowing Robot for Biomimetic Simulations of Bolus Transport

Cited by 7 publications

References 24 publications

Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement

Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement

Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces

Experimental Investigation into the Dynamics of a Radially Contracting Actuator with Embedded Sensing Capability

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