International Journal For Multidisciplinary Research

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Real-Time Electrode Pairing and Stimulation Parameter Optimization for Pelvic Floor Therapy Using Cross-Platform Kotlin Algorithms

Author(s) Ronak Indrasinh Kosamia
Country United States
Abstract The increasing prevalence of pelvic floor disorders, particularly among postpartum and aging populations, has created a strong demand for intelligent, personalized, and portable therapeutic solutions. Existing neuromodulation therapies rely heavily on clinician-guided electrode programming and static stimulation protocols, limiting their adaptability to individual patient physiology and real-time biological fluctuations. In this paper, we propose a modular, clinically informed, Kotlin-based algorithm designed for cross-platform deployment via Kotlin Multiplatform Mobile (KMM). The algorithm analyzes pseudo-monopolar (PM) sensor data to identify dominant and non-dominant electrodes, forming optimal stimulation pairs and dynamically generating stimulation parameters—including amplitude, pulse width, and frequency.
Our solution supports two primary operational modes. The Batch Evaluation Mode is designed for retrospective analysis of session-long data, aggregating electrode responses over time to identify consistent stimulation targets. The Real-Time Dynamic Mode, by contrast, provides closed-loop therapy capabilities by continuously ingesting new sensor data and updating stimulation recommendations per cycle. These modes share a common architecture, leveraging a unified logic layer to maintain consistency, simplify testing, and reduce maintenance overhead.
One of the central contributions of this work is its integration of signal processing, dynamic electrode ranking, and real-time parameter tuning into a highly modular and reusable codebase. The entire logic resides in the shared Kotlin module, allowing uniform access across Android and iOS platforms through native bindings. This ensures not only cross-device treatment consistency but also enables broader adoption by clinics and mobile health developers. A particular innovation lies in the use of pseudo-monopolar sensing as a core analytic method. PM values serve as proxies for physiological activation, enabling the system to infer dominant sites for stimulation based on muscle recruitment or sensor voltage. The use of these values for both real-time decision-making and batch trend analysis provides a powerful diagnostic and therapeutic foundation.
The platform’s modularity makes it extensible to additional use cases such as EMG fusion, patient-controlled therapy calibration, or integration with Bluetooth-enabled wearable sensors. Furthermore, the system is designed with structured data outputs and telemetry compatibility, facilitating both patient-facing applications and backend research platforms.
To validate the algorithm, we simulate electrode activity under multiple scenarios using synthetic sensor inputs that mimic real pelvic floor engagement patterns. Through these simulations, we demonstrate that our real-time mode responds adaptively to changing activation profiles, while batch mode consolidates patterns to produce robust treatment strategies. Our implementation offers clinicians and developers a clinically informed, technically portable, and programmatically extensible toolset for advancing pelvic health therapy.
The proposed system lays the groundwork for scalable, intelligent therapeutic systems that align with modern mobile architectures and clinical decision workflows. It also illustrates how Kotlin-based engineering can be adapted to solve domain-specific healthcare problems in a way that is reproducible, maintainable, and medically relevant.
Keywords Pelvic Floor Therapy, Kotlin Multiplatform Mobile, Pseudo-Monopolar Sensing, Closed-Loop Neuromodulation, Electrode Pairing, Real-Time Therapy, Signal-Based Stimulation, Cross-Platform Algorithm Design.
Field Engineering
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-07-11
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.79517
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