Pengembangan Sistem FES Berbasis Fuzzy Logic untuk Mereduksi Tingkat Spastisitas pada Ekstrimitas Lengan Atas Pasien Post Stroke

Zuhdi, Rahardian Asyam (2025) Pengembangan Sistem FES Berbasis Fuzzy Logic untuk Mereduksi Tingkat Spastisitas pada Ekstrimitas Lengan Atas Pasien Post Stroke. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Spastisitas adalah kondisi umum yang dialami pasien post-stroke, ditandai dengan peningkatan tonus otot berlebihan yang menyebabkan kekakuan serta penurunan kemampuan motorik. Penelitian ini mengembangkan sistem Functional Electric Stimulation (FES) berbasis fuzzy logic untuk pengaturan intensitas stimulasi sesuai tingkat spastisitas pasien. Sistem menggunakan sensor IMU dengan akurasi error 2-3 derajat untuk mengukur Range of motion (ROM) dan torsi sebagai input fuzzy logic controller, dilengkapi boost converter hingga 163V, pulse generator 250 μs pada 20 Hz, dan driver channel responsif. Hasil pengujian threshold pada subjek normal menunjukkan otot biceps kontraksi pada saverity 7% (21,54V) dan triceps pada 9% (24,49V), sedangkan subjek post-stroke belum kontraksi hingga 70% (64V) namun menunjukkan respon getaran dan sensasi nyaman. Pengujian pada 6 subjek (5 normal, 1 post-stroke) menunjukkan efektivitas klasifikasi spastisitas 100% dengan akurasi MMAS 100%, dimana ROM tinggi (105,18°-120,61°) dikategorikan MMAS 1 severity "Low", ROM moderate (65,05°-86,31°) sebagai MMAS 1-2 severity "Medium", dan ROM rendah post-stroke (10,85°-22,24°) sebagai MMAS 4 severity "Medium". Target square wave menghasilkan respons 105% lebih tinggi pada ROM dan 71% pada torsi dibanding target sinus. Sistem berhasil mengintegrasikan parameter kinematik-dinamik untuk assessment akurat sesuai standar klinis dengan korelasi ROM-MMAS yang kuat, memungkinkan personalisasi stimulasi otomatis real-time. Hasil menunjukkan sistem mampu mengukur spastisitas objektif dengan efektivitas 100%, peningkatan kontrol motorik, dan kualitas hidup pasien lebih baik. Pengembangan selanjutnya memerlukan uji klinis subjek lebih besar, desain wearable kompak, adaptasi machine learning, evaluasi jangka panjang, dan interface mobile dengan standardisasi protokol medis internasional untuk implementasi klinis luas.
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Spasticity is a common condition experienced by post-stroke patients, characterized by excessive muscle tone that leads to stiffness and reduced motor function. This study develops a fuzzy logic-based Functional Electric Stimulation (FES) system for regulating stimulation intensity according to patient spasticity levels. The system utilizes IMU sensors with 2-3 degree error accuracy to measure Range of motion (ROM) and torque as fuzzy logic controller inputs, equipped with a boost converter up to 163V, pulse generator at 250 μs with 20 Hz frequency, and responsive driver channels. Threshold testing results in normal subjects showed biceps muscle contraction at 7% saverity (21.54V) and triceps at 9% (24.49V), while post-stroke subjects showed no contraction up to 70% (64V) but demonstrated vibration responses and comfortable sensations. Testing on 6 subjects (5 normal, 1 post-stroke) demonstrated 100% effectiveness in spasticity classification with 100% MMAS accuracy, where high ROM (105.18°-120.61°) was categorized as MMAS 1 with "Low" severity, moderate ROM (65.05°-86.31°) as MMAS 1-2 with "Medium" severity, and low post-stroke ROM (10.85°-22.24°) as MMAS 4 with "Medium" severity. Square wave targets produced 105% higher ROM response and 71% higher torque compared to sine targets. The system successfully integrated kinematic-dynamic parameters for accurate assessment according to clinical standards with strong ROM-MMAS correlation, enabling automatic real-time stimulation personalization. Results demonstrate the system's capability to objectively measure spasticity with 100% effectiveness, achieving significant spasticity reduction, improved motor control, and enhanced patient quality of life. Future development requires larger clinical trials, compact wearable design, machine learning adaptation, long-term evaluation, and mobile interface with international medical protocol standardization for broader clinical implementation.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Stroke, Spastisitas, Functional Electrical Stimulation (FES), Fuzzy Clinical Decision Support System (FCDSS), Stroke, Spasticity, Functional Electrical Stimualtion (FES), Fuzzy Clinical Decision Support System (FCDSS)
Subjects:	R Medicine > RM Therapeutics. Pharmacology > RM871 Electric stimulation. R Medicine > RM Therapeutics. Pharmacology > RM950 Rehabilitation technology. R Medicine > RZ Other systems of medicine
Divisions:	Faculty of Electrical Technology > Biomedical Engineering > 11410-(S1) Undergraduate Thesis
Depositing User:	Rahardian Asyam Zuhdi
Date Deposited:	04 Aug 2025 02:50
Last Modified:	04 Aug 2025 02:50
URI:	http://repository.its.ac.id/id/eprint/126404

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