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arxiv: 2605.25643 · v1 · pith:IIUXEKMUnew · submitted 2026-05-25 · 💻 cs.HC

WeeCare: Towards Handheld Bladder Fullness Sensing with a Conformable Pad

Zhikai Qin , Siqi Zhang , Junyi Zhu , Justin Chan This is my paper

Pith reviewed 2026-06-29 20:51 UTC · model grok-4.3

classification 💻 cs.HC
keywords bladder fullness sensingelectrical impedance tomographyconformable padfabric electrodeshandheld devicebladder dysfunctionon-demand monitoringEIT reconstruction
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The pith

A handheld conformable pad with fabric electrodes performs on-demand bladder fullness sensing via electrical impedance tomography despite repeated repositioning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Patients who lose bladder sensation must rely on fixed catheterization schedules that cause discomfort and complications. WeeCare addresses this by using a flexible pad with fabric electrodes to apply electrical impedance tomography for measuring bladder volume on demand. The system is evaluated through simulations of electrode layouts and noise, phantom tests with varying urine salinities and fill levels, and a human study tracking fullness, voiding, and refilling. The central technical focus is handling contact and position variations that occur each time the pad is removed and reapplied. If effective, this approach would let users check bladder state only when needed rather than on a rigid timetable.

Core claim

WeeCare demonstrates that a handheld conformable pad equipped with fabric electrodes can support electrical impedance tomography for bladder fullness sensing, with the design and validation showing robustness to electrode placement variations through in-silico electrode characterization, in-vitro phantom measurements across salinities and volumes, and in-vivo human data on fullness, voiding, and filling dynamics.

What carries the argument

The handheld conformable pad with fabric electrodes that applies electrical impedance tomography to map bladder conductivity changes.

If this is right

  • Users could shift from fixed-interval catheterization to checks triggered only by sensed fullness.
  • Monitoring of voiding and refilling dynamics becomes possible without continuous attachment.
  • Phantom and simulation results indicate the electrode layout tolerates typical noise levels in EIT reconstructions.
  • The fabric electrode approach supports repeated use on skin without rigid hardware.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pad geometry might be tested for other abdominal conductivity targets beyond the bladder.
  • Integration with a simple readout device could allow home-based rather than clinic-only measurements.
  • Extending the in-vivo protocol to more subjects would clarify how well the single human trial generalizes.

Load-bearing premise

That variations in electrode position and contact quality from repeated removal and reattachment can still be overcome enough for reliable detection of bladder fullness levels.

What would settle it

Human trials in which electrical impedance tomography readings from the pad fail to distinguish between empty, partially full, and full bladder states across multiple independent placements on the same subjects.

Figures

Figures reproduced from arXiv: 2605.25643 by Junyi Zhu, Justin Chan, Siqi Zhang, Zhikai Qin.

Figure 1
Figure 1. Figure 1: WeeCare, a handheld electrical impedance tomography pad for on-demand bladder fullness sensing. (Left) Envisioned use in a care setting for patients with bladder dysfunction who lose the sensation of bladder filling. On-demand bladder fullness sensing has the potential to reduce the need for frequent and unnecessary catheterization. (Right) WeeCare prototype, a foldable grid of fabric electrodes that confo… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Conventional ring electrode layout in belt form [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of bladder volume under simulated electrode [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of simulated electrode perturbation on blad [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Handheld and conformable hardware prototype for [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Alternative prototypes explored. (a) Glove form [PITH_FULL_IMAGE:figures/full_fig_p004_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Benchtop validation on bladder phantom. (a) Abdomen simulating tissue, bladder, and skin. (b) [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: In-vivo evaluation. (a) Bladder ultrasound [ [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
read the original abstract

Patients with bladder dysfunction often lose the sensation of bladder fullness and cannot void naturally, forcing reliance on fixed-schedule catheterization that is uncomfortable and risks complications. We present WeeCare, a handheld conformable pad with fabric electrodes for on-demand bladder fullness sensing using electrical impedance tomography (EIT). The central challenge is that repeated removal and reattachment can introduce variation in electrode position and contact quality. We assess WeeCare along three axes: in-silico simulations characterizing electrode layout and noise robustness, in-vitro phantom experiments across urine salinities and filling levels, and an in-vivo human measurement for bladder fullness sensing, voiding, and filling dynamics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents WeeCare, a handheld conformable fabric-electrode pad for on-demand bladder fullness sensing via electrical impedance tomography (EIT). The central claim is that the device can overcome variations from repeated removal/reattachment through three evaluation axes: in-silico simulations of electrode layout and noise robustness, in-vitro phantom experiments across urine salinities and filling levels, and a single in-vivo human measurement session capturing bladder fullness, voiding, and filling dynamics.

Significance. If the robustness to electrode repositioning and contact variation is quantitatively demonstrated, the work could enable practical, non-invasive, on-demand sensing for patients with bladder dysfunction, reducing risks of fixed-schedule catheterization. The multi-modal evaluation (simulation, phantom, human) is a positive structural choice; machine-checked elements or reproducible code are not mentioned.

major comments (2)
  1. [§3, §4] §4 (In-vivo experiments) and §3 (Phantom experiments): the reported reconstructions do not include a quantitative tolerance analysis (e.g., maximum allowable electrode displacement in mm or contact impedance drift in ohms) such that the bladder conductivity contrast between empty and full states remains distinguishable above the reconstruction error; without this margin the handheld on-demand claim is not yet load-bearing.
  2. [§2.2] §2.2 (EIT reconstruction pipeline): the noise-robustness simulations characterize additive noise but do not propagate the specific positional jitter and contact-quality variations expected from repeated pad removal/reattachment; the resulting conductivity maps are therefore not shown to remain stable within the signal difference observed in the phantom and human data.
minor comments (2)
  1. [Figures 4-6] Figure captions for the phantom and in-vivo EIT images should explicitly state the color scale units (conductivity in S/m) and the exact electrode placement coordinates used in each trial.
  2. [Abstract] The abstract states three evaluation axes but supplies no numerical performance metrics (e.g., classification accuracy, RMSE); these should be added for immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that quantitative tolerance margins for electrode displacement and contact variations are needed to fully support the on-demand handheld claim, and we will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [§3, §4] §4 (In-vivo experiments) and §3 (Phantom experiments): the reported reconstructions do not include a quantitative tolerance analysis (e.g., maximum allowable electrode displacement in mm or contact impedance drift in ohms) such that the bladder conductivity contrast between empty and full states remains distinguishable above the reconstruction error; without this margin the handheld on-demand claim is not yet load-bearing.

    Authors: We agree that the manuscript would be strengthened by an explicit quantitative tolerance analysis. In the revision we will add Monte Carlo simulations of electrode displacements (0–15 mm) and contact impedance drifts (0–50 Ω) applied to the in-silico and phantom geometries, reporting the maximum values at which the empty-to-full conductivity contrast remains above reconstruction error. These margins will be cross-validated against the observed signal differences in the existing phantom and single in-vivo datasets. revision: yes

  2. Referee: [§2.2] §2.2 (EIT reconstruction pipeline): the noise-robustness simulations characterize additive noise but do not propagate the specific positional jitter and contact-quality variations expected from repeated pad removal/reattachment; the resulting conductivity maps are therefore not shown to remain stable within the signal difference observed in the phantom and human data.

    Authors: The §2.2 simulations were limited to additive Gaussian noise to benchmark the reconstruction pipeline. We acknowledge they do not yet incorporate positional jitter or contact-quality variations. In revision we will extend the simulation framework to include realistic reattachment jitter (drawn from 5–10 mm Gaussian displacements and 10–30 Ω contact impedance perturbations) and will demonstrate that the resulting conductivity maps remain stable within the empty–full signal differences measured in the phantom and in-vivo experiments. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental evaluation chain is self-contained

full rationale

The paper describes a hardware prototype and its empirical validation through in-silico simulations of electrode layouts and noise, in-vitro phantom tests across salinities and fill levels, and a single in-vivo human session. No equations, fitted parameters, derivations, or self-citations appear as load-bearing steps in the provided abstract or evaluation description. The central claim rests on measured stability of EIT reconstructions rather than any reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, invented entities, or detailed axioms beyond the implicit domain assumption that EIT conductivity differences can track bladder volume.

axioms (1)
  • domain assumption Electrical impedance tomography can detect bladder volume changes via conductivity contrast between urine and surrounding tissues despite electrode contact variations.
    Central to the proposed sensing method and the claim that the pad enables on-demand detection.

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discussion (0)

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Reference graph

Works this paper leans on

53 extracted references · 14 canonical work pages

  1. [1]

    2020.PyCaret: An open source, low-code machine learning library in Python

    Moez Ali. 2020.PyCaret: An open source, low-code machine learning library in Python. https://www.pycaret.org PyCaret version 1.0.0

    2020

  2. [2]

    Lynn Stothers Andrew J. Macnab. 2008. Near-infrared spectroscopy: validation of bladder-outlet obstruction assessment using non-invasive parameters.Canadian Journal of Urology15, 5 (2008), 4241–4248. http://www.techscience.com/CJU/ v15n5/62252

    2008

  3. [3]

    Benoit Brazey, Yassine Haddab, and Nabil Zemiti. 2022. Robust imaging using electrical impedance tomography: review of current tools.Proceedings. Mathe- matical, Physical, and Engineering Sciences478 (2022). https://api.semanticscholar. org/CorpusID:246445033

    2022

  4. [4]

    Long Long Cao, Feng Wen Wang, Jingwei Xue, Fulei Liu, and Ming Liang Jin

  5. [5]

    arXiv:https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202520926 doi:10.1002/advs.202520926

    Integrated Ultrasound Device for Precision Bladder Volume Monitoring via Acoustic Focusing and Machine Learning.Advanced Science13, 17 (2026), e20926. arXiv:https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202520926 doi:10.1002/advs.202520926

    work page doi:10.1002/advs.202520926 2026

  6. [6]

    CO-Z. 2026. CO-Z 1.1 Gallon Water Distiller. https://www.amazon.com/dp/ B0D87F64BX Accessed 2026-05-23

    2026

  7. [7]

    Peter Curran and Simavita Limited. 2017. Everyday Monitoring of Incontinence Products: A New Disruptive Technology. Technical presentation. https:// announcements.asx.com.au/asxpdf/20171109/pdf/43p2lkbnjrptnv.pdf Presented at Hygienix 2017

    work page arXiv 2017

  8. [8]

    Kanika Dheman, Stefan Walser, Philipp Mayer, Manuel Eggimann, Marko Ko- zomara, Denise Franke, Thomas Hermanns, Hugo Sax, Simone Schürle, and Michele Magno. 2024. Noninvasive Urinary Bladder Volume Estimation With Artifact-Suppressed Bioimpedance Measurements.IEEE Sensors Journal24, 2 (2024), 1633–1643. doi:10.1109/JSEN.2023.3324819

    work page doi:10.1109/jsen.2023.3324819 2024

  9. [9]

    Peter T Dorsher and Peter M McIntosh. 2012. Neurogenic bladder.Advances in urology2012, 1 (2012), 816274

    2012

  10. [10]

    Lin Duan and Ming-Liang Jin. 2025. A biocompatible integrated bladder elec- tronics for wireless capacity monitoring assessment.Soft Science5, 1 (2025). doi:10.20517/ss.2024.46

    work page doi:10.20517/ss.2024.46 2025

  11. [11]

    Eoghan Dunne, Adam Santorelli, Brian Mc Ginley, Geraldine Leader, Martin O’Halloran, and Emily Porter. 2018. Image-based classification of bladder state using electrical impedance tomography.Physiological Measurement39 (12 2018). doi:10.1088/1361-6579/aae6ed

    work page doi:10.1088/1361-6579/aae6ed 2018

  12. [12]

    Pascal Fechner, Fabian König, Wolfgang Kratsch, Jannik Lockl, and Maximilian Röglinger. 2023. Near-infrared spectroscopy for bladder monitoring: a machine learning approach.ACM Transactions on Management Information Systems14, 2 (2023), 1–23

    2023

  13. [13]

    Daniel D Fong, Xiaofan Yu, Jiageng Mao, Mahya Saffarpour, Prashant Gupta, Rami Abueshsheikh, Alejandro Velazquez Alcantar, Eric A Kurzrock, and Soheil Ghiasi. 2018. Restoring the sense of bladder fullness for spinal cord injury patients. Smart Health9 (2018), 12–22

    2018

  14. [14]

    Generic Bio-Tech. 2026. Gel Pads for Abs Stimulator. https://www.amazon.com/ dp/B0FLJHG4C8 Accessed 2026-05-23

    2026

  15. [15]

    Holder (Ed.)

    David S. Holder (Ed.). 2005.Electrical Impedance Tomography: Methods, History and Applications. CRC Press

    2005

  16. [16]

    Yasuhiko Igawa, Jean-Jacques Wyndaele, and Osamu Nishizawa. 2008. Catheteri- zation: possible complications and their prevention and treatment.International journal of urology15, 6 (2008), 481–485

    2008

  17. [17]

    Kiwibunn. 2021. Female Torso Anatomy Study Sketch. https: //sketchfab.com/3d-models/female-torso-anatomy-study-sketch- 5f186790eb5d436cad3b73b6117464d8. Accessed: 2026-05-18

    2021

  18. [18]

    F Krewer, F Morgan, E Jones, M Glavin, and M O’Halloran. 2014. Development of a wearable microwave bladder monitor for the management and treatment of urinary incontinence. InRadar Sensor Technology XVIII, Vol. 9077. SPIE, 295–303

    2014

  19. [19]

    Niels Kristian Kristiansen, Hans Nygaard, and Jens Christian Djurhuus

  20. [20]

    arXiv:https://doi.org/10.1080/00365590510031165 doi:10.1080/00365590510031165 PMID: 16118108

    Clinical evaluation of a novel ultrasound-based bladder volume mon- itor.Scandinavian Journal of Urology and Nephrology39, 4 (2005), 321–328. arXiv:https://doi.org/10.1080/00365590510031165 doi:10.1080/00365590510031165 PMID: 16118108

    work page doi:10.1080/00365590510031165 2005

  21. [21]

    Eliza Lamin and Diane K Newman. 2016. Clean intermittent catheterization revisited.International urology and nephrology48, 6 (2016), 931–939

    2016

  22. [22]

    Kyungsu Lee, Moon Hwan Lee, Dongho Kang, Sewoong Kim, Jin Ho Chang, Seung-June Oh, and Jae Youn Hwang. 2024. Intelligent bladder volume mon- itoring for Wearable Ultrasound devices: enhancing Accuracy through Deep Learning-based coarse-to-fine shape estimation.IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control71, 7 (2024), 775–785

    2024

  23. [23]

    Steffen Leonhardt, Axel Cordes, Harry Plewa, Robert Pikkemaat, Irina Sol- janik, Klaus Moehring, Hans J Gerner, and Rüdiger Rupp. 2011. Electric impedance tomography for monitoring volume and size of the urinary blad- der.Biomedizinische Technik. Biomedical engineering56, 6 (December 2011), 301—307. doi:10.1515/bmt.2011.022

    work page doi:10.1515/bmt.2011.022 2011

  24. [24]

    Dorothea Leonhäuser, Carlos Castelar, Thomas Schlebusch, Martin Rohm, Rüdi- ger Rupp, Steffen Leonhardt, Marian Walter, and Joachim O. Grosse. 2018. Eval- uation of electrical impedance tomography for determination of urinary blad- der volume: comparison with standard ultrasound methods in healthy volun- teers.BioMedical Engineering OnLine17 (2018). https...

    2018

  25. [25]

    Rihui Li, Jinwu Gao, Yaning Li, Junpeng Wu, Zhanqi Zhao, and Yang Liu. 2016. Preliminary study of assessing bladder urinary volume using electrical impedance tomography.Journal of Medical and Biological Engineering36, 1 (2016), 71–79

    2016

  26. [26]

    Xuyang Li, Elena Pancera, Lukasz Niestoruk, Wilhelm Stork, and Thomas Zwick

  27. [27]

    InThe 7th European Radar Conference

    Performance of an ultra wideband radar for detection of water accumulation in the human bladder. InThe 7th European Radar Conference. IEEE, 212–215

  28. [28]

    Benyuan Liu, Bin Yang, Canhua Xu, Junying Xia, Meng Dai, Zhenyu Ji, Fusheng You, Xiuzhen Dong, Xuetao Shi, and Feng Fu. 2018. pyEIT: A python based framework for Electrical Impedance Tomography.SoftwareX7 (2018), 304–308

    2018

  29. [29]

    H Madersbacher. 1990. The various types of neurogenic bladder dysfunction: an update of current therapeutic concepts.Spinal Cord28, 4 (1990), 217–229

    1990

  30. [30]

    Behnam Molavi, Babak Shadgan, Andrew J Macnab, and Guy A Dumont. 2013. Noninvasive optical monitoring of bladder filling to capacity using a wireless near infrared spectroscopy device.IEEE transactions on biomedical circuits and systems8, 3 (2013), 325–333

    2013

  31. [31]

    Nagle, Adam P

    Anna S. Nagle, Adam P. Klausner, Jary Varghese, Rachel J. Bernardo, Andrew F. Colhoun, Robert W. Barbee, Laura R. Carucci, and John E. Speich. 2017. Quan- tification of bladder wall biomechanics during urodynamics: A methodologic investigation using ultrasound.Journal of Biomechanics61 (2017), 232–241. doi:10.1016/j.jbiomech.2017.07.028

    work page doi:10.1016/j.jbiomech.2017.07.028 2017

  32. [32]

    Shuhei S Noyori, Gojiro Nakagami, and Hiromi Sanada. 2022. Non-invasive urine volume estimation in the bladder by electrical impedance-based methods: A review.Medical Engineering & Physics101, 1 (2022), 103748

    2022

  33. [33]

    Shuhei S Noyori, Gojiro Nakagami, and Hiromi Sanada. 2022. Non-invasive urine volume estimation in the bladder by electrical impedance-based methods: A re- view.Medical Engineering & Physics101 (2022), 103748. doi:10.1016/j.medengphy. 2021.103748

    work page doi:10.1016/j.medengphy 2022

  34. [34]

    M O’Halloran, F Morgan, M Glavin, E Jones, RC Conceio, and D Byrne. 2013. Bladder-state monitoring using ultra wideband radar. In2013 7th European Con- ference on Antennas and Propagation (EuCAP). IEEE, 624–627

    2013

  35. [35]

    Parker Laboratories Inc. [n. d.]. Aquasonic Ultrasound Gel Blue 1 Liter Bot- tle. https://www.amazon.com/dp/B07CX17646?ref_=ppx_hzsearch_conn_dt_b_ fed_asin_title_5. Accessed: 2026-05-05

    2026

  36. [36]

    Nolan Perry. 2026. EIT for Bladder Monitoring: Principles, Methods, and Clinical Validation in Urodynamic Research. Biomedical Engineering Science. https://www.biomedengsci.com/posts/eit-for-bladder-monitoring- principles-methods-and-clinical-validation-in-urodynamic-research Accessed: 2026-05-25

    2026

  37. [37]

    Aiyada Phisaiphan, Phanpasorn Laor-Iam, Chamaiporn Sukjamsri, Anurak Dowloy, and Taweechai Ouypornkochagorn. 2025. Bladder monitoring with time- frequency-difference electrical impedance tomography technique.Measurement 250 (2025), 117162. doi:10.1016/j.measurement.2025.117162

    work page doi:10.1016/j.measurement.2025.117162 2025

  38. [38]

    Cong Pu, Ben Fu, Lehang Guo, Huixiong Xu, and Chang Peng. 2024. A Stretchable and Wearable Ultrasonic Transducer Array for Bladder Volume Monitoring Application.IEEE Sensors Journal24, 10 (2024), 15875–15883. doi:10.1109/JSEN.2024.3382244

    work page doi:10.1109/jsen.2024.3382244 2024

  39. [39]

    A E Rees, L C Ward, B H Cornish, and B J Thomas. 1999. Sensitivity of multiple frequency bioelectrical impedance analysis to changes in ion status.Physiological Measurement20, 4 (nov 1999), 349. doi:10.1088/0967-3334/20/4/302 UbiComp/ISWC ’26, June 10–12, 2026, Shanghai, China Qin et al

    work page doi:10.1088/0967-3334/20/4/302 1999

  40. [40]

    Gregory Samson and Diana D Cardenas. 2007. Neurogenic bladder in spinal cord injury.Physical medicine and rehabilitation clinics of North America18, 2 (2007), 255–274

    2007

  41. [41]

    Thomas Schlebusch, Steffen Nienke, S Leonhardt, and M Walter. 2014. Blad- der volume estimation from electrical impedance tomography.Physiological measurement35, 9 (2014), 1813–1823

    2014

  42. [42]

    Seung-chul Shin, Junhyung Moon, Saewon Kye, Kyoungwoo Lee, Yong Seung Lee, and Hong-Goo Kang. 2017. Continuous bladder volume monitoring system for wearable applications. In2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 4435–4438. doi:10.1109/ EMBC.2017.8037840

    work page arXiv 2017

  43. [43]

    Alp Timucin Toymus, Umut Can Yener, Emine Bardakci, Özgür Deniz Temel, Ersin Koseoglu, Dincay Akcoren, Burak Eminoglu, Mohsin Ali, Rasim Kilic, Tufan Tarcan, et al. 2024. An integrated and flexible ultrasonic device for continuous bladder volume monitoring.Nature Communications15, 1 (2024), 7216

    2024

  44. [44]

    Verathon. 2018. BladderScan Prime Plus. https://www.verathon.com/assets/0900- 4510-xx-60.pdf. Product brochure. Accessed: 2026-05-05

    2018

  45. [45]

    Verathon Medical. 2008. BladderScan BVI 9400 Product Brochure. https://www. verathon.com/sites/default/files/2021-08/0900-4412-xx-60.pdf. Accessed: 2026- 05-05

    2008

  46. [46]

    Carla Verpoorten and Gunnar M Buyse. 2008. The neurogenic bladder: medical treatment.Pediatric nephrology23, 5 (2008), 717–725

    2008

  47. [47]

    Women’s Health and Education Center. 2009. Urodynamic Assessment: Tech- niques. https://polylang.womenshealthsection.com/urog/urog003 Accessed: 2026-05-21

    2009

  48. [48]

    WVOVW. 2026. 2"X12"X12" Sound Proofing Egg Crate Foam Pad. https://www. amazon.com/dp/B0FP4K2CJB/ Accessed 2026-05-23

    2026

  49. [49]

    WVOVW. 2026. Faraday Fabric Tape 1inch x 50 Feet Double Conductive High 1 Inch x 50. https://www.amazon.com/dp/B097HPSLTH Accessed 2026-05-23

    2026

  50. [50]

    JJ Wyndaele. 2002. Complications of intermittent catheterization: their prevention and treatment.Spinal cord40, 10 (2002), 536–541

    2002

  51. [51]

    Yuantide Bio-Tech. 2026. Yuantide Bio Lab-Grade 100% Agar Agar Powder,150g / 5oz. https://www.amazon.com/dp/B0CN41D2D4 Accessed 2026-05-23

    2026

  52. [52]

    Ruoyu Zhang, Ruijie Fang, Chongzhou Fang, Houman Homayoun, and Gozde Goncu Berk. 2023. Privee: A wearable for real-time bladder monitoring system. InAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing. 291–295

    2023

  53. [53]

    Snowden, Joshua Verdejo, Emily Chen, Paul Zhang, Hamid Ghaednia, Joseph H

    Junyi Zhu, Jackson C. Snowden, Joshua Verdejo, Emily Chen, Paul Zhang, Hamid Ghaednia, Joseph H. Schwab, and Stefanie Mueller. 2021. EIT-kit: An Electrical Impedance Tomography Toolkit for Health and Motion Sensing. InProceedings of the 34th Annual ACM Symposium on User Interface Software and Technology (UIST ’21). ACM, New York, NY, USA, 400–413. doi:10....

    work page doi:10.1145/3472749.3474758 2021