Smart Wearable IoT untuk Deteksi Jatuh dan Pemantauan Kesehatan Lansia Secara Real-Time
DOI:
https://doi.org/10.61722/jssr.v4i6.12949Keywords:
Deteksi Jatuh, Internet of Things (IoT), Keselamatan Lansia, Smart Wearable, Pemantauan KesehatanAbstract
The elderly population is highly susceptible to fall risks and requires continuous health monitoring. Limitations in manual monitoring can lead to delayed emergency responses, while existing visual monitoring solutions often present privacy concerns. This study aims to design and develop an Internet of Things (IoT)-based smart wearable device to detect fall events and monitor heart rate in real time without utilizing visual cameras, thereby safeguarding user privacy. The system employs an ESP32-C3 microcontroller as the central control unit, integrated with an MPU6050 sensor to measure tri-axial acceleration and orientation changes for fall detection, alongside a MAX30102 sensor for heart rate monitoring in beats per minute (BPM). Processed sensor data is transmitted via Wi-Fi to Firebase Realtime Database for web dashboard display, while emergency fall alerts trigger an on-board buzzer and automated Telegram notifications to caregivers. Experimental results across 60 trial activities demonstrate that the MPU6050 sensor accurately classifies 58 activities with an average fall detection latency of 3.93 seconds. The MAX30102 sensor requires an average stabilization time of 8.8 seconds for reliable BPM readings, and Telegram alerts achieve an average response latency of 4.2 seconds. The developed system effectively enhances elderly safety, maintains user privacy, and accelerates emergency response intervention.
Populasi lansia sangat rentan terhadap risiko jatuh dan membutuhkan pemantauan kesehatan secara terus-menerus. Keterbatasan pemantauan manual dapat menyebabkan keterlambatan penanganan darurat, sementara solusi pemantauan visual sering kali menimbulkan masalah privasi. Penelitian ini bertujuan untuk merancang dan mengembangkan perangkat wearable cerdas berbasis Internet of Things (IoT) untuk mendeteksi kejadian jatuh dan memantau detak jantung secara real-time tanpa menggunakan kamera visual sehingga privasi pengguna tetap terjaga. Sistem ini menggunakan mikrokontroler ESP32-C3 sebagai unit kontrol utama yang diintegrasikan dengan sensor MPU6050 untuk mengukur percepatan tri-aksial dan perubahan orientasi guna mendeteksi jatuh, serta sensor MAX30102 untuk pemantauan detak jantung dalam satuan beats per minute (BPM). Data sensor yang diproses dikirimkan melalui Wi-Fi ke Firebase Realtime Database untuk ditampilkan pada dashboard web, sedangkan peringatan darurat saat jatuh akan mengaktifkan buzzer dan notifikasi Telegram otomatis kepada pengasuh. Hasil pengujian pada 60 skenario aktivitas menunjukkan bahwa sensor MPU6050 berhasil mengklasifikasikan 58 aktivitas dengan rata-rata latensi deteksi jatuh sebesar 3,93 detik. Sensor MAX30102 membutuhkan waktu stabilisasi rata-rata 8,8 detik untuk pembacaan BPM yang stabil, dan peringatan Telegram mencapai latensi respons rata-rata 4,2 detik. Sistem yang dikembangkan ini efektif meningkatkan keselamatan lansia, menjaga privasi, dan mempercepat intervensi penanganan darurat.
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