nRF52840 for Fall Detection with Edge Impulse

Hardware Specs

Compatibility: Excellent

Memory-wise, the nRF52840 offers 256 KB SRAM, which 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. Flash storage at 1 MB comfortably houses the Edge Impulse 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. Edge Impulse provides an end-to-end workflow: data collection from the nRF52840 via serial or WiFi, cloud-based training with auto-quantization, and deployment via C++ library export or Arduino library. The platform estimates on-device RAM and flash usage before deployment, reducing trial-and-error. Use the serial data forwarder for data collection from the board. 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

   Create Edge Impulse project for nRF52840

   Sign up at edgeimpulse.com and create a new project for fall detection. Install the Edge Impulse CLI (npm install -g edge-impulse-cli). Use the data forwarder to stream imu data from your Nordic Semiconductor development board.

2. 2

   Collect imu training data

   Connect an IMU sensor (e.g., MPU6050 or LSM6DS3 via I2C/SPI) to the nRF52840 via I2C. Use Edge Impulse's data forwarder or direct board connection to stream samples to the cloud. Collect 500+ labeled samples across all classes. Include normal operating conditions and edge cases in your dataset.

3. 3

   Train model in Edge Impulse Studio

   Design an impulse with the appropriate signal processing block (spectral analysis for motion). Add a LSTM or 1D-CNN on IMU time-series learning block. Train and evaluate — Edge Impulse shows estimated latency and memory usage for the nRF52840. Target under 16 KB model size and under 40 KB peak RAM.

4. 4

   Deploy and validate on nRF52840

   Deploy via Edge Impulse CLI (edge-impulse-cli export) or download the C++ library. 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

Why choose Edge Impulse over other frameworks for nRF52840?

Edge Impulse provides the fastest path from raw data to deployed model for the nRF52840. Its cloud platform handles data preprocessing, model architecture search, quantization, and deployment in a single workflow. Use the serial data forwarder for boards without direct connectivity support. The tradeoff: dependency on Edge Impulse's cloud for training and model optimization.

Can nRF52840 run fall detection inference in real time?

The nRF52840 runs at 64 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 nRF52840?

Power consumption during inference depends on clock configuration, active peripherals, and duty cycle. Consult the nRF52840 datasheet for detailed power profiles at 64 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.

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