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tinyML Summit 2023: Why TinyML Applications Fail: An examination of common challenges and issues...
tinyML Talks: On-device model fine-tuning for industrial anomaly detection applications
IoT Fall Detection System using MPU6050, ESP32, and Blynk - Protect Your Loved Ones! #iot
Demo: Real-Time Fall Detection with Hybrid CNN-LSTM Using IWR6843AOP FMCW Radar —Justin Fairman Tan
IoT Fall Detection System using Smartphone Accelerometer & Edge AI
TinyML Tutorial 3.9 - Person Detection Deployment
tinyML Talks: NanoEdge AI Studio: All On-Device Anomaly Detection for Industry 4.0
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Last Updated: September 29, 2026
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Summary
I made a medical alert system with Development Board: Arduino nano 33 BLE sense Program: Arduino, Python, Tensorflow, Tensorflow lite micro # In this project, we developed SmartFall AI — a real-time abnormal motion "On-device model fine-tuning for industrial anomaly Falls are a serious concern, especially for the elderly and those with medical conditions. With our IoT-based In this video, we present our engineering project: a hardware-free, software-defined IoT ... IDE and on file examples scroll down to NanoEdge AI Studio: All On-Device Anomaly