Hardware Guide

STM32H7 for Fall Detection with TensorFlow Lite Micro

For fall detection, the STM32H7 with TFLite Micro scores Excellent. Its 1024 KB internal SRAM (16.0x the required 64 KB) and 480 MHz clock ensure smooth real-time inference on 20 KB models. Hardware DSP extensions boost throughput.

Compatibility: Excellent

With 1024 KB of internal SRAM, the STM32H7 provides 16.0x the 64 KB minimum for fall detection. This generous headroom means the 20 KB model tensor arena, sensor input buffers, and application logic (imu polling, Ethernet stack, state management) all fit without contention. The remaining 974 KB after model allocation supports complex application features. The STM32H7 provides 2 MB of flash memory, which comfortably houses the TFLite Micro runtime, the 20 KB model binary, application firmware, and OTA update partitions for field upgrades. Flash usage is well within budget for this configuration. The STM32H7 at 480 MHz with double-precision FPU and ART accelerator is among the highest-performance Cortex-M MCUs in ST's lineup. Its 1 MB SRAM accommodates models that smaller MCUs cannot fit in memory. Dual-bank flash enables safe OTA firmware updates during operation. For fall detection, connect an IMU sensor (e.g., MPU6050 or LSM6DS3 via I2C/SPI) via SPI to the STM32H7. Sample at 50-200 Hz and collect windows of 64-256 samples as model input. The DSP extensions efficiently compute FFT features from raw sensor data. TFLite Micro's static memory allocation model maps well to the STM32H7's memory architecture — define a fixed tensor arena at compile time with no runtime heap fragmentation risk. The framework's operator coverage supports dense and convolutional layers needed for fall detection. Model conversion uses the standard TFLite converter with int8 post-training quantization. At $8-20 per chip ($30-80 for dev boards), the STM32H7 offers strong value for fall detection deployments. 22 PlatformIO-listed boards provide decent hardware selection. Key STM32H7 features for this workload: Double-precision FPU, L1 cache (16 KB I + 16 KB D), JPEG codec, Chrom-ART Accelerator (DMA2D).

Alternatives

FAQ

Why choose TFLite Micro over other frameworks for STM32H7?

TFLite Micro has the widest operator coverage and largest community for cortex-m7 targets. It supports int8 and float32 models with a static memory allocation model that eliminates heap fragmentation. The STM32H7's 1024 KB SRAM works well with TFLite Micro's predictable memory usage. Alternative: Edge Impulse wraps TFLite Micro with a simpler workflow if you prefer cloud-based training.

Can STM32H7 run fall detection inference in real time?

The STM32H7 runs at 480 MHz with DSP acceleration. Whether this enables real-time fall detection depends on your specific model architecture and acceptable latency. A 20 KB int8 model is a reasonable target for this hardware class. Smaller models on this clock speed typically allow continuous inference. Benchmark your specific model on hardware to validate timing.

What is the power consumption for fall detection on STM32H7?

Power consumption during inference depends on clock configuration, active peripherals, and duty cycle. Consult the STM32H7 datasheet for detailed power profiles at 480 MHz. For battery-powered fall detection, use duty cycling: run inference at intervals and enter low-power sleep mode between cycles. Profile your specific workload to estimate battery life accurately.