give me the complete pcb again. this is not the complete pcb - then ensure all connections would work please and that it can be placed on a pcb. thanks. ⛳️ Golf Swing Sensor Sticker System – Complete Project Description
🧩 1. Project Overview
This project involves the design and implementation of an ultra-thin, flexible, rechargeable sensor sticker that attaches to the flat back of a golf club head. Its purpose is to measure a golfer’s swing characteristics — including direction, speed, angle, and impact force — and transmit the data wirelessly to a mobile app for real-time prediction of golf ball direction and distance.
The system integrates a small set of onboard sensors with a Bluetooth-enabled microcontroller, forming a compact IoT device that assists golfers in evaluating and improving their swings in both practice and competitive settings.
🧠 2. Functional Goals
✅ Measure swing velocity, rotation, and impact force from the club.
✅ Transmit data to a mobile app via Bluetooth (BLE).
✅ Process the data using a trained neural network model (on-device or app backend).
✅ Predict and display golf ball direction and distance, adjusted for real-time wind data.
✅ Store historical swing data and offer performance feedback.
📐 3. Physical Design Constraints
Parameter Target
Dimensions ≤ 30 mm × 20 mm
Total Thickness ≤ 2.5 mm
Mounting Waterproof 3M adhesive on club head
PCB Type 2-layer flexible PCB (PET or Kapton substrate)
Weight < 5 g
Battery Life 6–10 hours per charge (1000+ swings)
🔌 4. Electronics Subsystem
➤ 4.1 Core Components
Function Component Details
MCU + BLE nRF52832-QFAA 32-bit Cortex-M4F with integrated Bluetooth 5.2
IMU (Accel + Gyro) MPU-6050 6-axis sensor (I²C), QFN-24 4×4 mm
Force Sensor FSR 400 Measures club-ball impact force (external pad)
Battery Charger MCP73831T-2ACI/OT 1-cell LiPo charger, SOT-23-5
Voltage Regulator AP2112K-3.3 LDO 3.3V Regulator, SOT-23-5
Battery LP401225 (100 mAh) 25 × 12.5 × 1.6 mm, thin LiPo
Connector JST SH 2-pin 1.0 mm pitch battery connection
Passives 0402 capacitors and resistors Used for filtering, pull-ups, and biasing
➤ 4.2 PCB Design Summary
Microcontroller handles sensor acquisition, filtering, and BLE data transmission.
MPU-6050 provides 3D acceleration and gyroscope data sampled at ~200 Hz.
FSR sensor triggers data recording when impact threshold is reached.
I²C bus used for MPU-6050 communication with ~4.7kΩ pull-up resistors.
Power is supplied by a 3.7V LiPo, stepped down to 3.3V using an LDO.
System is optimized for low-power sleep when idle, waking on motion detection.
📲 5. Software & Data Flow
➤ 5.1 Onboard MCU Logic (Firmware)
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[Startup → Low Power Sleep]
↓ (motion detected)
[Check force sensor]
↓ (if impact)
[Sample IMU for 300 ms]
↓
[Preprocess + transmit data over BLE]
↓
[Return to sleep]
➤ 5.2 Mobile App Backend
Receives BLE packet: {"accel": [...], "gyro": [...], "force": x, "timestamp": t}
Fetches real-time wind data using GPS or ZIP code
Inputs data into trained neural network:
Regression → Predict distance (in yards)
Regression → Predict direction (angle/compass)
Classification → Predict swing type (e.g., slice, hook, straight)
Displays output via:
Compass-style UI
Wind correction advisories
Training history and score tracking
🌬️ 6. Wind Data Integration
Real-time wind pulled from APIs like OpenWeatherMap or Tomorrow.io
Adjusts model predictions using:
Wind speed vector
Directional drag estimation
Airborne trajectory adjustment formulas
🔋 7. Power & Charging
Feature Description
Battery 100 mAh LiPo (thin cell)
Charging Wired charging via magnetic pogo pins or USB tail
Charger IC MCP73831, programmable current (50–100 mA)
Charge Time ~30–60 minutes
Runtime >6 hours active use, 1000+ swings per charge
💡 8. Future Enhancements
Add high-resolution barometer for elevation adjustments.
Use solar trickle charging for passive charging during play.
Integrate NAND flash for offline data storage if BLE unavailable.
Support club auto-detection to adjust swing profiles (wedge vs. driver).
Offer machine learning retraining pipeline for personalized ball prediction.
✅ 9. Summary
This project delivers a fully integrated, sensor-rich smart PCB that fits on the back of a golf club, collects detailed swing analytics, and pairs with a mobile app to give real-time performance predictions. It balances extreme miniaturization with practical runtime, efficient wireless communication, and intelligent feedback — forming the core of a next-generation golf training tool.
Let me know if you'd like a downloadable version of this description as a PDF project spec, or to bundle it with a KiCad schematic/PCB file, BOM spreadsheet, or mobile app architecture diagram.
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