nRF52840 for Fall Detection with TensorFlow Lite Micro

Hardware Specs

Compatibility: Excellent

The nRF52840's 256 KB SRAM provides 4.0x 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, Bluetooth 5.0 LE stack, state management) all fit without contention. The remaining 206 KB after model allocation supports complex application features. For firmware and model storage, the 1 MB flash comfortably houses the TFLite Micro runtime, the 20 KB model binary, application firmware, and basic configuration data. Flash usage is well within budget for this configuration. The nRF52840 is widely used for BLE-connected ML applications. Its 256 KB SRAM handles keyword spotting, gesture recognition, and sensor anomaly detection models. Zephyr RTOS support and Edge Impulse's first-class nRF integration streamline the development workflow. For fall detection, connect an IMU sensor (e.g., MPU6050 or LSM6DS3 via I2C/SPI) via SPI to the nRF52840. 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 nRF52840'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 $5-8 per chip ($20-35 for dev boards), the nRF52840 offers strong value for fall detection deployments. 22 PlatformIO-listed boards provide decent hardware selection. Key nRF52840 features for this workload: Built-in 9-axis IMU (LSM9DS1) on Arduino Nano 33 BLE, Arduino ecosystem, Ultra-low-power BLE, Built-in microphone (Sense variant).

Getting Started

1. 1

   Set up nRF52840 development environment

   Install nRF Connect SDK (Zephyr-based) or Arduino via PlatformIO. Create a project targeting the nRF52840 and verify basic functionality (blink LED, serial output). For TFLite Micro, clone the framework repository and add it as a library dependency. Ensure the toolchain supports C++11 or later for the ML runtime.

2. 2

   Collect imu training data

   Connect an IMU sensor (e.g., MPU6050 or LSM6DS3 via I2C/SPI) to the nRF52840 via I2C. Write a data logging sketch that captures imu readings at the target sample rate and outputs via serial/SD card. Collect 500+ labeled samples across all classes. Include normal operating conditions and edge cases in your dataset.

3. 3

   Train and quantize model for TFLite Micro

   Build a LSTM or 1D-CNN on IMU time-series in TensorFlow or PyTorch. Apply int8 post-training quantization — this typically reduces model size by 4x with minimal accuracy loss. Convert to .tflite and generate a C array (xxd -i model.tflite > model_data.h). Target model size: under 20 KB to fit the nRF52840's 256 KB SRAM with room for application code.

4. 4

   Deploy and validate on nRF52840

   Include the TFLite Micro runtime and compiled model in your Nordic Semiconductor project. Allocate a tensor arena of 30-50 KB in a static buffer. Run inference on live imu data and compare predictions against your test set. Log results to serial for desktop validation. Measure inference latency and peak RAM usage to verify they meet application requirements.

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FAQ

What vibration sampling rate does nRF52840 support for fall detection?

The nRF52840 can sample accelerometers at 100 Hz - 1 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 nRF52840's DSP instructions compute FFT efficiently in firmware.

How do I update the fall detection model on nRF52840 in production?

Without wireless connectivity, model updates require physical access via USB/JTAG. For field deployments, consider adding a wireless module or using an MCU with built-in connectivity. Always validate model integrity with a checksum before switching to the new version.

What size fall detection model fits on nRF52840?

The nRF52840 has 256 KB SRAM and 1 MB flash. A typical fall detection model is 20 KB after int8 quantization. The tensor arena needs 30-40 KB at runtime. After model allocation, approximately 216 KB remains for application logic, sensor drivers, and Bluetooth 5.0 LE stack.

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