PENGEMBANGAN SISTEM REKOGNISI AKTIVITAS DAN PREDIKSI JATUH PADA LANSIA MENGGUNAKAN SENSOR IMU
Abstract
Urgensi pengembangan suatu sistem pengenalan aktivitas lansia serta memprediksi kemungkinan terjadinya jatuh menjadi prioritas dalam menghadapi total populasi lansia yang telah mencapai 11,34% di tahun 2020 dan diproyeksikan meningkat menjadi seperlima populasi Indonesia pada tahun 2045. Keluarga yang hidup bersama dengan lansia memiliki keterbatasan dalam memantau dan menjaga lansia, ditambah lagi dengan kekhawatiran akan kemungkinan terjadinya jatuh. Bertujuan untuk membantu keluarga lansia dalam memonitor aktivitas lansia maka dikembangkanlah suatu sistem pemantauan aktivitas lansia. Sedangkan untuk mengurangi risiko jatuh, sebuah alat yang dapat dikenakan pada lansia dilengkapi dengan mode peringatan berupa getaran yang akan aktif sesaat diprediksi lansia akan terjatuh. Sistem pemantauan aktivitas lansia menggunakan metode human activity recognition (HAR) dengan model XGBoost dan 6 fitur sedangkan prediksi jatuh diambil dari deteksi sensor IMU menggunakan threshold dari nilai mutlak terbesar pada akselerometer di sumbu . Performa yang didapat dari metode tersebut adalah akurasi sebesar 90% untuk HAR, 95% untuk prediksi jatuh, dan 83,3% untuk hasil integrasi keduanya.
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References
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[4] Rusminingsih, E, Marwanti, M, Sawitri, E dan Cahyani, A.D. 2021. Pengaruh Latihan Keseimbangan (Forward Stepping) Terhadap Risiko Jatuh Pada Lansia. Urecol Journal. Part C: Health Sciences. 1, 1, pp. 22–28. doi: 10.53017/ujhs.43.
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[20] Rizal, A dan Istiqomah. 2022. Lung Sounds Classification Based on Time Domain Features. J. Ilm. Tek. Elektro Komput. dan Inform. 8, 2, pp. 318.
[21] Friedman, JH. 2002. Stochastic gradient boosting. Comput Stat Data Anal 38:367–378
[22] Chen, T and Guestrin, C. 2016. XGBoost: a scalable tree boosting system. In: KDD ’16 Proceedings of 22nd ACM SIGKDD international conference knowledge discovery and data mining Medication, pp 785–794.
[23] Gunawan, G.I., Silalahi, D.K., Mukhtar, H., Barus, D.T., Rahmawati, D. 2021. Performance Comparison of Classification Algorithms for Locating the Dominant Heel Pain Using Electromyography Signal. Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering, vol 746. Springer, Singapore. https://doi.org/10.1007/978-981-33-6926-9_45.
[24] Jakkula, V. 2011. Tutorial on support vector machine (SVM). School EECS, Washington State University, 1–13.
[25] Ismail, Istiqomah, and Mukhtar, H. 2023. Development Human Activity Recognition for the Elderly Using Inertial Sensor and Statistical Feature. Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics. pp. 293–305.
[26] Lin, H. C, Chen, MJ, Lee, CH, Kung, LC, dan Huang, JT. 2023. Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT. Sensors. 23, 12, .doi: 10.3390/s23125472.
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[28] Nabriya, P. 2020. Feature Engineering on Time-Series Data for Human Activity Recognition. Data Science.
[29] Mohamed, K. S. 2019. The Era of Internet of Things: Towards a Smart World.
[30] Rizal, A., Priharti, W., Rahmawati, D., Mukhtar, H., 2022. Classification of Pulmonary Crackle and Normal Lung Sound Using Spectrogram and Support Vector Machine. JBBBE. https://doi.org/10.4028/p-tf63b7.
[31] Viswanatha, V, Ramachandra, A.C, Prasanna, PR, et.al. 2022. Implementation of Tiny Machine Learning Models on Arduino 33 – BLE for Gesture and Speech Recognition. Journal of Xi'an University of Architecture & Technology. 14,7, pp. 160-169.
[32] M. Fayad et al. 2023. Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey. Appl. Sci. 13, 18. doi: 10.3390/app131810352.
Published
2024-06-10
How to Cite
MUKHTAR, Husneni.
PENGEMBANGAN SISTEM REKOGNISI AKTIVITAS DAN PREDIKSI JATUH PADA LANSIA MENGGUNAKAN SENSOR IMU.
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal), [S.l.], v. 11, n. 1, p. 54 - 68, june 2024.
ISSN 2442-4404.
Available at: <//journals.telkomuniversity.ac.id/jett/article/view/7299>. Date accessed: 03 july 2024.
doi: https://doi.org/10.25124/jett.v11i1.7299.
Section
ELECTRONICS
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