CNN-FIFO-LSTM: An Energy-Efficient Hybrid Architecture for Real-Time Fall Detection on Edge Devices

Abstract

Fall detection for wearable health monitoring must combine subject-independent accuracy, low false-alarm risk, real-time response, and multi-day battery operation. Deep learning can capture fall dynamics, but recurrent or long-window models often increase memory access, inference latency, and energy use on microcontroller-class devices. This paper introduces CNN-FIFO-LSTM, a compact TinyML architecture that decouples spatial and temporal processing for real-time fall detection. A convolutional neural network maps each four-channel inertial window to a 32-dimensional embedding, a first-in-first-out (FIFO) buffer stores recent embeddings, and a lightweight LSTM classifies the resulting temporal sequence. Applying recurrence to compressed embeddings rather than raw sensor windows preserves temporal context while reducing both the sequence-model input volume and computational resource use. On KFall, leave-one-subject-out (LOSO) validation achieves 98.85% accuracy, 97.04% precision, 98.95% recall, and a 97.98% F1-score. When trained on KFall and evaluated on SisFall, the model maintains 97.4% accuracy and a 96.7% F1-score. The selected floating-point ESP32-S3 deployment requires 227 KB for the models, 573 KB of firmware flash, and 132.3 KB of RAM, with 48.9 ms of model inference, 56.6 ms of the full cycle, and 5.3 days of estimated continuous operation from a 1000 mAh battery. A mixed-precision post-training quantization variant, using an int8 CNN and a float32 LSTM, reduces inference latency to 28.3 ms and extends estimated battery life to 7.8 days, with a modest reduction in F1-score to 97.67%. Together, these results show that CNN-FIFO-LSTM achieves a practical trade-off among detection performance, response time, and battery life in evaluations using public datasets and prototype hardware.