Ramadhan, Rizqi (2024) Analisis Gangguan (Noise) Heart Rate Sensor Pada Wearable Device Berdasarkan Artefak Tangan Menggunakan Polar Oh1. Masters thesis, Institut Teknologi Sepuluh Nopember.
Text
6026221002_Master_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (6MB) | Request a copy |
Abstract
Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis tingkat noise yang dihasilkan oleh perangkat wearable OH1 selama berbagai gerakan tangan, yang dapat mempengaruhi keakuratan pengukuran detak jantung (HR). Masalah umum yang dihadapi adalah ketidakakuratan data HR yang disebabkan oleh noise saat perangkat digunakan selama aktivitas fisik. Masalah khususnya adalah menentukan gerakan tangan mana yang menghasilkan noise tertinggi dan terendah, serta bagaimana variabilitas data BPM dalam kondisi gerakan tangan yang berbeda-beda. Untuk mengatasi masalah ini, penelitian ini mengumpulkan data HR dari 10 jenis gerakan tangan, yaitu Menekan Tangan Lurus, Horizontal Shoulder Extension, Siku ke Hidung, Menyentuh Bahu, Mengangkat Bahu 90°, Supinate, Pronate, Melenturkan Bahu 180°, Tangan ke Dahi, dan Siku Menekuk 90°. Data tersebut kemudian dianalisis menggunakan metode Support Vector Machine (SVM) untuk mengklasifikasikan tingkat noise yang dihasilkan oleh setiap gerakan.Dengan meenggunakan 11 partisipan yang dalam kondisi sehat. Hasil analisis menunjukkan bahwa gerakan Melenturkan Bahu 180° menghasilkan tingkat noise tertinggi, sementara gerakan Siku Menekuk 90° menghasilkan noise terendah. Variabilitas data BPM menunjukkan perbedaan signifikan dalam stabilitas pengukuran HR yang dipengaruhi oleh jenis gerakan tangan. Dengan menggunakan metode SVM, akurasi klasifikasi mencapai 67%, yang menunjukkan efektivitas metode ini dalam mengidentifikasi dan mengklasifikasikan noise dari berbagai gerakan tangan. Kesimpulannya, penelitian ini memberikan wawasan penting tentang pengaruh gerakan tangan terhadap akurasi pengukuran HR oleh perangkat wearable OH1. Dengan mengetahui gerakan yang menghasilkan noise tinggi, pengguna dapat menghindari gerakan tersebut untuk memperoleh data HR yang lebih akurat dan andal.
=================================================================================================================================
This study aims to identify and analyze the level of noise produced by the OH1 wearable device during various hand movements, which can affect the accuracy of heart rate (HR) measurements. The general issue addressed is the inaccuracy of HR data caused by noise when the device is used during physical activities. The specific issues include determining which hand movements produce the highest and lowest noise levels and understanding the variability of BPM data under different hand movement conditions. To address these issues, HR data was collected from 10 types of hand movements, including Pressing Straight Hand, Horizontal Shoulder Extension, Elbow to Nose, Touching Shoulder, Lifting Shoulder 90°, Supinate, Pronate, Shoulder Flexion 180°, Hand to Forehead, and Elbow Bent 90°. The data was analyzed using the Support Vector Machine (SVM) method to classify the noise levels generated by each movement. The study involved 11 healthy participants. The analysis results show that the Shoulder Flexion 180° movement produces the highest noise level, while the Elbow Bent 90° movement produces the lowest noise. The variability in BPM data indicates significant differences in the stability of HR measurements influenced by the type of hand movement. Using the SVM method, the classification accuracy reached 67%, demonstrating the effectiveness of this method in identifying and classifying noise from various hand movements. In conclusion, this study provides important insights into the impact of hand movements on the accuracy of HR measurements by the OH1 wearable device. By identifying movements that generate high noise levels, users can avoid these movements to obtain more accurate and reliable HR data.
Item Type: | Thesis (Masters) |
---|---|
Uncontrolled Keywords: | Classification, Data Variability, Hand Movements, Heart Rate Measurement, HR Accuracy, Noise, OH1, Physical Activity, Support Vector Machine (SVM), Wearable Device |
Subjects: | R Medicine > RC Internal medicine R Medicine > RZ Other systems of medicine T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models. T Technology > T Technology (General) > T58.5 Information technology. IT--Auditing |
Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 59101-(S2) Master Thesis |
Depositing User: | Rizqi Ramadhan |
Date Deposited: | 09 Aug 2024 01:04 |
Last Modified: | 04 Sep 2024 04:38 |
URI: | http://repository.its.ac.id/id/eprint/110335 |
Actions (login required)
View Item |