Hardware Guide

STM32U5 for Fall Detection with TensorFlow Lite Micro

For fall detection, the STM32U5 with TFLite Micro scores Excellent. Its 786 KB internal SRAM (12.3x the required 64 KB) and 160 MHz clock ensure smooth real-time inference on 20 KB models. Hardware DSP extensions boost throughput.

Compatibility: Excellent

Memory-wise, the STM32U5 offers 786 KB SRAM, which provides 12.3x 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, USB OTG HS stack, state management) all fit without contention. The remaining 736 KB after model allocation supports complex application features. Flash storage at 2 MB 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 STM32U5 combines Cortex-M33 with TrustZone for secure ML inference and ultra-low power consumption. Its 786 KB SRAM is among the largest in low-power MCU families. The SMPS voltage regulator extends battery life in duty-cycled inference scenarios. For fall detection, connect an IMU sensor (e.g., MPU6050 or LSM6DS3 via I2C/SPI) via SPI to the STM32U5. 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 STM32U5'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 $6-15 per chip ($20-50 for dev boards), the STM32U5 offers strong value for fall detection deployments. Key STM32U5 features for this workload: Ultra-low-power (best-in-class Cortex-M33), TrustZone hardware security, Hardware crypto (AES/PKA/HASH), SMPS for power efficiency, Up to 2514 KB SRAM on U5A5/U5G9 variants.

Alternatives

FAQ

Can STM32U5 run fall detection inference in real time?

The STM32U5 runs at 160 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 STM32U5?

Power consumption during inference depends on clock configuration, active peripherals, and duty cycle. Consult the STM32U5 datasheet for detailed power profiles at 160 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.

What vibration sampling rate does STM32U5 support for fall detection?

The STM32U5 can sample accelerometers at 1-10 kHz via SPI (faster) or ADC. For fall detection, 50-200 Hz is typically sufficient. Collect windows of 64-256 samples for gesture/motion classification. The STM32U5's DSP instructions compute FFT efficiently in firmware.