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OPTIMASI SISTEM DETEKSI JATUH UNTUK LANJUT USIA BERBASIS EDGE COMPUTING MENGGUNAKAN ALGORITMA HYBRID PADA PLATFORM ESP32
Falls among the elderly are a serious health issue, where current solutions are often hindered by the weaknesses of cloud-based detection systems, such as high latency, significant power consumption, and internet dependency. This research aims to optimize a fall detection system by developing and testing a hybrid architecture based on edge computing on an Artificial Intelligence of Things platform using the ESP32-S3 microcontroller. The research method is Research and Development (R&D), wherein this study builds a functional wearable prototype using an MPU6050 sensor and an ESP32-S3 microcontroller. It implements a hybrid detection algorithm, which combines a threshold-based method as an initial trigger and a Random Forest classifier for final validation, to run entirely locally on the device. The study trains the Random Forest model offline using the public SisFall dataset and tests it through a series of simulated fall scenarios and Activities of Daily Living (ADLs) in a controlled laboratory environment. The results show that this edge-based system achieves a fall detection success rate of 91.25% with a very low false positive rate of 2.5%. The end-to-end system response time, from the moment of the incident until the notification is received, averages 4.85 seconds, which demonstrates the superiority of the edge computing architecture in terms of speed and efficiency compared to cloud-based approaches. This system offers a more responsive, reliable, and power-efficient solution. Keywords: Fall Detection, Edge Computing, Artificial Intelligence of Things, ESP32 microcontroller, Random Forest.
KODEPRODI20201#Teknik_Elektro_S1, Universitas Pendidikan Indonesia
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