Ghost Edge AI Sticker

A new paper-thin, flexible AI sticker sensor node called Ghost Edge has been developed, built around the Nordic Semiconductor nRF54L15 wireless SoC. The device uses a flexible printed circuit board to conform to curved surfaces, enabling unobtrusive physical activity tracking, structural health monitoring, and smart wearables. It runs real-time machine learning models on-device using a dual-core processor architecture.

The Ghost Edge AI Sticker is a paper-thin, flexible sticker-style smart sensor node built around the next-generation Nordic Semiconductor nRF54L15 wireless SoC. Traditional IoT sensor nodes are often bulky, requiring rigid PCBs and battery cases that limit where they can be mounted. To break this limitation, we designed a device utilizing a Flexible PrintedCircuit FPC board. This bendable, sticker-like design allows the node to conform to curved surfaces, making it ideal for unobtrusive physical activity tracking, structural health monitoring, and smart wearables. Equipped with a 6-axis IMU LSM6DSO and a digital PDM microphone, this thin sticker runs real-time machine learning models Edge AI directly on-device using a dual-core processor architecture ARM Cortex-M33 + RISC-V FLPR . This project main topics are as follows: FPC Design : Ultra-thin, flexible, and mountable on curved surfaces. Dual-Core Processing : ARM M33 and RISC-V core collaboration via IPC. Edge AI : Real-time IMU and mic data analysis using Edge Impulse. OTA Updates : Wireless firmware updates over BLE via MCUboot. This bellow video summarizes the main results. It's all wrapped up in about two minutes, so please take a look 3. Hardware DesignThe custom FPC board layout and schematic were designed in KiCad. The sensor section is placed at the tip of a narrow flexible tail, allowing it to be positioned away from the main power and debugging circuitry. • KiCad Design Files: Fully open-source project files for schematics and PCB/FPC layouts. • Manufacturing Output: Gerber files and Bill of Materials BOM ready for FPC manufacturing. • Schematic Drawings: PDF schematic sheets for quick hardware tracing. https://github.com/iotengineer22/ghost-sticker/tree/main/board https://github.com/iotengineer22/ghost-sticker/tree/main/board All firmware applications are built using the nRF Connect SDK NCS / Zephyr RTOS and organized inside the /src folder: https://github.com/iotengineer22/ghost-sticker/tree/main/src https://github.com/iotengineer22/ghost-sticker/tree/main/src src/├── blinky/ Alternating dual LED test├── dfu test/ BLE Peripheral UART with MCUboot and OTA DFU├── peripheral lbs/ BLE LED Button Service LBS demo├── peripheral uart i2c/ LSM6DSO data BLE streaming High-Speed Packed Mode ├── peripheral uart i2c demo/ LSM6DSO data BLE streaming 1Hz Demo Mode ├── peripheral uart i2c edge/ Edge Impulse gesture classification & BLE notify├── peripheral uart pdm/ Edge Impulse audio classification via PDM mic WIP ├── riscv gpio/ Multi-core IPC ARM + RISC-V LED controller└── riscv uart/ Multi-core IPC ARM + RISC-V LSM6DSO monito 5. Multi-Core Inter-Processor Communication IPC One of the highlights of this project is the multi-core utilization of the nRF54L15 SoC. Using the riscv uart sample, the Remote core RISC-V FLPR handles high-frequency sensor acquisition from the LSM6DSO IMU over I2C. The RISC-V core then transfers the formatted data packets to the Host core ARM Cortex-M33 via IPC using icmsg shared memory . The Host core acts as the BLE gateway, receiving data over IPC and forwarding it to the connected BLE client using the Nordic UART Service NUS . This separates the critical time-sensitive sensor. By deploying the peripheral uart i2c edge application, the nRF54L15 performs on-device real-time machine learning. We trained a gesture classification model e.g., Wave, Shake, Idle on Edge Impulse Studio using accelerometer data. The C++ deployment library is integrated directly into the firmware. Classification outputs are printed in a clean single-line format: Predictions DSP: 4 ms, Class: 8 ms : idle: 0.02, wave: 0.98 This output is transmitted live over BLE, enabling low-latency control of smart devices using physical gestures. During the hardware testing phase, we identified a footprint issue on the PCB/FPC artwork regarding the PDM microphone: Footprint Orientation Error : The PDM microphone component footprint was designed with a 180-degree rotation error placed backward on the board. Because of this footprint mistake, we were unable to debug or confirm the operation of the audio features on the actual physical hardware. Important Design Note : Although we have added warning notes to the uploaded schematic PDF and PCB layout artwork, anyone attempting to manufacture this board should carefully review the component datasheets and verify the footprint pins before starting production. This project main topics are as follows: FPC Design : Ultra-thin, flexible, and mountable on curved surfaces. Dual-Core Processing : ARM M33 and RISC-V core collaboration via IPC. Edge AI : Real-time IMU and mic data analysis using Edge Impulse. OTA Updates : Wireless firmware updates over BLE via MCUboot. This bellow video summarizes the main results. It's all wrapped up in about two minutes, so please take a look This has been a great fun challenge. Thanks to NextPCB for hosting this exciting program. Thank you very much for all the support provided. Read more javascript:void 0