Machine-Learning-Enhanced Soft Robotic System Inspired by Rectal Functions for Investigating Fecal incontinence
<|reference_start|>Machine-Learning-Enhanced Soft Robotic System Inspired by Rectal Functions for Investigating Fecal incontinence: Fecal incontinence, arising from a myriad of pathogenic mechanisms, has attracted considerable global attention. Despite its significance, the replication of the defecatory system for studying fecal incontinence mechanisms remains limited largely due to social stigma and taboos. Inspired by the rectum's functionalities, we have developed a soft robotic system, encompassing a power supply, pressure sensing, data acquisition systems, a flushing mechanism, a stage, and a rectal module. The innovative soft rectal module includes actuators inspired by sphincter muscles, both soft and rigid covers, and soft rectum mold. The rectal mold, fabricated from materials that closely mimic human rectal tissue, is produced using the mold replication fabrication method. Both the soft and rigid components of the mold are realized through the application of 3D-printing technology. The sphincter muscles-inspired actuators featuring double-layer pouch structures are modeled and optimized based on multilayer perceptron methods aiming to obtain high contractions ratios (100%), high generated pressure (9.8 kPa), and small recovery time (3 s). Upon assembly, this defecation robot is capable of smoothly expelling liquid faeces, performing controlled solid fecal cutting, and defecating extremely solid long faeces, thus closely replicating the human rectum and anal canal's functions. This defecation robot has the potential to assist humans in understanding the complex defecation system and contribute to the development of well-being devices related to defecation.<|reference_end|>
@article{mao2024machine-learning-enhanced,
    title={Machine-Learning-Enhanced Soft Robotic System Inspired by Rectal
  Functions for Investigating Fecal incontinence},
    author={Zebing Mao, Sota Suzuki, Hiroyuki Nabae, Shoko Miyagawa, Koichi
  Suzumori, Shingo Maeda},
    journal={arXiv preprint arXiv:2404.10999},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10999},
    primaryClass={cs.RO}
}