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<http://repository.upi.edu/id/eprint/144322> <http://purl.org/dc/terms/title> "OPTIMASI SISTEM DETEKSI JATUH UNTUK LANJUT USIA\r\nBERBASIS EDGE COMPUTING MENGGUNAKAN ALGORITMA\r\nHYBRID PADA PLATFORM ESP32"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://repository.upi.edu/id/eprint/144322> <http://purl.org/ontology/bibo/abstract> "Falls among the elderly are a serious health issue, where current solutions\r\nare often hindered by the weaknesses of cloud-based detection systems, such as high\r\nlatency, significant power consumption, and internet dependency. This research\r\naims to optimize a fall detection system by developing and testing a hybrid\r\narchitecture based on edge computing on an Artificial Intelligence of Things\r\nplatform using the ESP32-S3 microcontroller. The research method is Research and\r\nDevelopment (R&D), wherein this study builds a functional wearable prototype\r\nusing an MPU6050 sensor and an ESP32-S3 microcontroller. It implements a\r\nhybrid detection algorithm, which combines a threshold-based method as an initial\r\ntrigger and a Random Forest classifier for final validation, to run entirely locally\r\non the device. The study trains the Random Forest model offline using the public\r\nSisFall dataset and tests it through a series of simulated fall scenarios and Activities\r\nof Daily Living (ADLs) in a controlled laboratory environment. The results show\r\nthat this edge-based system achieves a fall detection success rate of 91.25% with a\r\nvery low false positive rate of 2.5%. The end-to-end system response time, from the\r\nmoment of the incident until the notification is received, averages 4.85 seconds,\r\nwhich demonstrates the superiority of the edge computing architecture in terms of\r\nspeed and efficiency compared to cloud-based approaches. This system offers a\r\nmore responsive, reliable, and power-efficient solution.\r\nKeywords: Fall Detection, Edge Computing, Artificial Intelligence of Things,\r\nESP32 microcontroller, Random Forest."^^<http://www.w3.org/2001/XMLSchema#string> .
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