CPRPal — Real-Time CPR Feedback and Training Device

Project Overview

CPRPal is a portable, puck-shaped CPR coaching device designed to help untrained bystanders and CPR trainees perform chest compressions with correct force, rhythm, and body positioning during a cardiac emergency or training session.

Sudden cardiac arrest is responsible for approximately 93% of out-of-hospital cardiac arrest deaths. Survival probability drops roughly 10% per minute without CPR or AED intervention, yet only about 3% of the U.S. population is CPR certified annually. CPRPal addresses this gap by providing real-time, easy-to-interpret feedback that requires no prior training to use.

The device is placed between the rescuer's hands and the patient's sternum during chest compressions. It measures compression force directly using a load cell, tracks body posture using a 9-DOF IMU, monitors for heartbeat presence using a PPG optical sensor, and delivers feedback through a color-coded NeoPixel LED ring, a 2.8" TFT LCD display, and a 100 BPM audio metronome buzzer.

Emergency Mode operates fully standalone with no Wi-Fi required, providing immediate real-time feedback the moment the button is pressed.

Training Mode connects to Wi-Fi, runs a 2-minute guided session, and uploads averaged session metrics to an Azure-hosted cloud dashboard for review.

The final prototype measures 4.5" x 4.5" x 2.25" and runs for over 15 hours on four AA batteries at a normal operating draw of approximately 155 mA.

CPRPal Demo: https://drive.google.com/file/d/1d1auYhc-_jhG4WK9BlywpUfyi4_rIxLO/view?usp=sharing (Note video isn't perfectly sync with operation)

Rendering	Final Build

Repository Structure

CPRPal/
├── 3DModel/
│   └── Mechanical enclosure and CAD files
│       - Top shell with lattice structure and LCD/button/LED cutouts
│       - Bottom shell with battery pocket, support beams, and hard stops
│       - Full assembly files for PLA 3D printing
│
├── ArduinoCode/
│   └── CPRPal.ino — Main ESP32 firmware (FreeRTOS-based)
│       - Sensor integration (HX711, BNO055, PPG)
│       - Real-time signal processing and CPM detection
│       - LED and display feedback systems
│       - Emergency and Training mode logic
│       - Power management and idle sleep
│       - HTTP cloud upload for training sessions
│
├── ArduinoWebsite/
│   └── Azure Static Web App source code
│       - Frontend dashboard (HTML/CSS/JS)
│       - Azure Function API endpoint
│       - Receives HTTP POST from ESP32
│       - Displays training session summary
│
├── PartsList/
│   └── Bill of Materials (BOM) and component references
│
└── PCBFiles/
    └── Full circuit schematics and PCB layout files
        - CPRPal_PCB_Sch — complete schematic
        - Panelized 6-board layout for JLCPCB fabrication
        - Custom PCB footprints and 3D component models

System Architecture

CPRPal uses a dual-core FreeRTOS architecture on the Arduino Nano ESP32-S3.

Core 0 — Sensor Acquisition and Processing

All time-critical sensor tasks run on Core 0 to prevent display rendering overhead from introducing jitter into sensor sampling.

Task	Function	Sample Rate
`taskHX711`	Load cell reading, EMA filtering, CPM detection, idle detection	~80 Hz
`taskIMU`	BNO055 orientation reading, tilt computation, posture status	~40 Hz
`taskPPG`	PPG sampling, adaptive peak detection, BPM estimation	~50 Hz
`taskBuzzer`	100 BPM metronome via LEDC PWM, rhythm LED strip sync	100 BPM

Core 1 — User Interface and Control

The Arduino loop() function runs on Core 1 and handles all output rendering:

TFT display refresh at 10 Hz
NeoPixel force bar graph refresh at 40 Hz
Mode selection and session control logic
Training session timer and metric accumulation
Serial calibration commands

Shared State and Synchronization

All shared sensor variables use the g_ prefix, are declared volatile, and are protected by the FreeRTOS mutex xDataMutex. A second mutex xWireMutex serializes I2C bus access between taskIMU and any other bus user. This two-mutex architecture prevents data races while minimizing lock hold time.

Hardware Components

Component	Part	Purpose
Microcontroller	Arduino Nano ESP32-S3	Main processing, Wi-Fi, dual-core FreeRTOS
Force Sensor	Half-bridge strain gauge load cell (120 lb)	Direct compression force measurement
Load Cell Amplifier	HX711 24-bit ADC	Amplifies and digitizes load cell signal at 80 SPS
IMU	Adafruit BNO055 9-DOF Fusion Breakout	Orientation, tilt detection, motion artifact rejection
Pulse Sensor	Pulse Sensor Amped (PPG)	Supplemental heartbeat detection
Display	Adafruit 2.8" ILI9341 TFT LCD	Real-time prompts and numeric status
LED Strips	Adafruit NeoPixel WS2812B (18 total)	Force bar (15 px) and rhythm metronome (3 px)
Buzzer	Piezo buzzer	100 BPM audio metronome
Power Regulation	TPS63070 buck-boost converter x2	Regulated 3.3V and 5V rails from 4xAA batteries
Reverse Protection	LM66200 ideal diode x2	Prevents USB-to-battery reverse current
Storage	SanDisk 32GB microSD	Boot animation BMP frames
Battery	4xAA alkaline	10+ hours runtime at normal operating load
Enclosure	PLA 3D print	4.7" x 4.7" x 2.4" puck housing

Wiring and Pin Assignments

ESP32 GPIO Map

Pin	Peripheral	Function
GPIO2	HX711 CLK	Load cell clock
GPIO3	HX711 DOUT	Load cell data
GPIO4	SD Card CS	microSD chip select
GPIO6 (D6)	Start Button	Mode selection (active low, internal pull-up)
GPIO9	ILI9341 DC	TFT data/command select
GPIO10	ILI9341 CS	TFT chip select
GPIO19	Buzzer	LEDC PWM channel 0, 2 kHz
GPIO21	PPG LED indicator	Heartbeat flash LED
GPIO23	PPG Sensor	Analog ADC input
GPIO2 (LED_PIN)	NeoPixel force bar	15-pixel WS2812B data
GPIO4 (RHYTHM)	NeoPixel rhythm strip	3-pixel WS2812B data
A4 (SDA)	BNO055 SDA	I2C data, 400 kHz
A5 (SCL)	BNO055 SCL	I2C clock, 400 kHz

I2C Bus

BNO055 IMU address 0x28
10 kOhm pull-up resistors on SDA and SCL
Bus speed 400 kHz (fast mode)
External 32.768 kHz crystal for improved BNO055 sensor fusion accuracy

SPI Bus

ILI9341 TFT LCD: DC on GPIO9, CS on GPIO10
microSD: CS on GPIO4
Both share the hardware SPI bus

Load Cell Wiring (Half-Bridge)

Wire Color	HX711 Pin	Function
RED	E+	Excitation positive
BLACK	E-	Excitation negative
WHITE	A-	Signal negative (differential)
GREEN	A+	Signal positive (differential)
YELLOW	GND	Common ground reference

Two 1 kOhm resistors on the HX711 board complete the Wheatstone bridge by providing the two fixed-impedance legs of the half-bridge configuration.

PCB Design

CPRPal uses a custom panelized PCB fabricated by JLCPCB, separated into six press-fit sub-boards using mouse-bite breakaway tabs. Each sub-board can be removed and replaced independently without additional soldering.

Sub-Board Summary

Board	Key Components	Output
3.3V BuckBoost	TPS63070, LM66200, 213k/365k feedback divider, decoupling caps, 1.5uH inductor	Regulated 3.3V rail
5V BuckBoost	TPS63070, LM66200, alternate feedback divider, decoupling caps, 1.5uH inductor	Regulated 5V rail
Power Hub	Distribution headers, 10uF X5R decoupling caps x2	Common power and ground plane
HX711	HX711 IC, 1kOhm bridge resistors, DOUT/CLK headers	Load cell ADC interface
BNO055 IMU	BNO055 breakout, 10kOhm I2C pull-ups, 32.768kHz crystal	Euler angle outputs via I2C
ESP32	Nano ESP32-S3 socket, USB passthrough	Main MCU
Bit Shift	Logic level shifter	3.3V to 5V NeoPixel data translation

Component Footprints

3.3V and 5V BuckBoost Boards

Reference	Component	Footprint
U1	TPS63070 — Buck-boost converter	SOT-23-8
U2	LM66200 — Ideal diode controller	SOT-23-6
L1	1.5 µH inductor	Inductor_SMD 4x4mm
R1, R2	Feedback divider resistors (213k / 365k for 3.3V; alternate values for 5V)	0402
R3	Current sense resistor	0402
R4, R6, R7	Bias/compensation resistors	0402
C1–C6	Decoupling and compensation capacitors	0402
C7	Output bulk capacitor	0805
D1	Schottky diode	SOD-123
J1, J2, J3, J4	Power input/output headers	PinHeader 1x02 2.54mm

Power Hub Board

Reference	Component	Footprint
C10, C11	10 µF X5R decoupling capacitors	0805
J (3.3V)	3.3V distribution header	PinHeader 2x05 2.54mm
J (5V)	5V distribution header	PinHeader 2x05 2.54mm
J (GND)	Ground distribution header	PinHeader 1x08 2.54mm

HX711 Board

Reference	Component	Footprint
U1	HX711 — 24-bit ADC	SOP-16
R5, R8	1 kΩ Wheatstone bridge completion resistors	0402
J (VDD, VCC, DAT, CLK, GND)	MCU interface header	PinHeader 1x05 2.54mm
J (RED, BLK, WHT, GRN, YLW)	Load cell wire header	PinHeader 1x05 2.54mm

BNO055 IMU Board

Reference	Component	Footprint
U1	BNO055 — 9-DOF IMU	LGA-28 (custom)
X1	32.768 kHz crystal	Crystal SMD 3.2x1.5mm
R1, R2	10 kΩ I2C pull-up resistors	0402
J7	Configuration header (PS0, PS1, ADR, INT)	PinHeader 1x04 2.54mm
J (RST, SDA, SCL, GND, 3V, VIN)	MCU interface header	PinHeader 1x06 2.54mm

ESP32 Board

Reference	Component	Footprint
U1	Arduino Nano ESP32-S3	Custom dual-row female socket, 2x15 2.54mm
J (USB)	USB-C passthrough cutout	USB-C receptacle footprint
J (left, right)	GPIO breakout headers	PinHeader 1x15 2.54mm x2

Bit Shift Board

Reference	Component	Footprint
U3	74AHCT125 — Quad level shifter	SOIC-14
R10	Pull-down resistor	0402
J (input)	3.3V NeoPixel data input header	PinHeader 1x02 2.54mm
J (output)	5V NeoPixel data output header	PinHeader 1x02 2.54mm
J (VCC, GND)	Power headers	PinHeader 1x02 2.54mm

Power Architecture

Two independent TPS63070 buck-boost converters are used instead of a single converter with an LDO post-regulator. This improves efficiency across the full 4xAA discharge range (approximately 6V fully charged to 4.8V near end of life), since the buck-boost topology maintains tight output regulation without the linear losses an LDO would introduce at shrinking headroom.

3.3V rail powers: ESP32-S3, BNO055 IMU, PPG sensor, piezo buzzer
5V rail powers: ILI9341 LCD, NeoPixel strips (18 px total), HX711 amplifier

Firmware

The firmware is a single Arduino sketch located in ArduinoCode/CPRPal.ino. It is written in C++ using the Arduino framework for ESP32 and relies on FreeRTOS task management, mutex-protected shared state, and the ESP32's LEDC hardware PWM peripheral for the buzzer metronome.

Key Constants

// Force feedback thresholds (lbs)
const float FORCE_THRESHOLD_LB      = 30.0f;   // Rising edge detection
const float FORCE_LOW_OK            = 75.0f;   // Below = TOO LIGHT (yellow)
const float FORCE_HIGH_OK           = 110.0f;  // Above = TOO STRONG (red)

// CPM thresholds
const float CPM_LOW_OK              = 100.0f;  // Below = TOO SLOW
const float CPM_HIGH_OK             = 120.0f;  // Above = TOO FAST
const unsigned long CPM_DEBOUNCE_MS = 180UL;   // Anti-bounce window
const int CPM_INTERVAL_COUNT        = 6;       // Rolling average window size

// Tilt feedback
const float TILT_WARN_DEG           = 8.0f;    // Above = ADJUST

// Timing
const unsigned long IDLE_TIMEOUT_MS     = 20000UL;  // 20s inactivity -> idle
const unsigned long TRAINING_SESSION_MS = 120000UL; // 2-minute session
const unsigned long TRAINING_SAMPLE_MS  = 3000UL;   // Sample every 3s
const unsigned long TRAINING_HOLD_MS    = 3000UL;   // Button hold -> Training

// Buzzer metronome
#define BUZZER_FREQ_HZ    2000  // 2 kHz tone
#define BUZZER_BEEP_MS    30    // On-time per beat in ms
#define BUZZER_PERIOD_MS  600   // Full period = 100 BPM

// Load cell
float calibration_factor = 6500; // Raw counts per pound-force (empirical)

Live Serial Calibration Commands

The calibration factor can be adjusted without reflashing firmware using the Serial Monitor at 115200 baud:

Key	Action
`+` or `a`	Increase calibration factor by 10
`-` or `z`	Decrease calibration factor by 10
`t` or `T`	Tare the scale (zero current load)
`r` or `R`	Reset session peak weight

EMA Filter

Raw load cell readings are smoothed before any threshold detection:

w_filtered[n] = 0.75 * w_filtered[n-1] + 0.25 * w_raw[n]

Smoothing factor alpha = 0.25 was chosen empirically to balance noise rejection against compression onset detection latency.

CPM Detection Algorithm

EMA-filtered force crosses above FORCE_THRESHOLD_LB (30 lb) — rising edge detected
180ms debounce window prevents double-counting from signal ringing near threshold
Inter-edge interval converted to instantaneous CPM: CPM = 60000 / delta_t_ms
Stored in 6-entry circular buffer; rolling CPM = arithmetic mean of buffer entries
If no compression detected for 2000ms, CPM resets to zero and buffer clears

IMU Baseline Calibration

On startup, 100 BNO055 orientation samples are captured (after 50 warm-up discards) and averaged as the resting baseline. All subsequent readings are deltas from this baseline so tilt feedback is relative to device placement on the patient, not absolute gravity direction.

Tilt magnitude = sqrt(delta_roll² + delta_pitch²)
Above 5 degrees triggers ADJUST on LCD
Above 10 degrees triggers LEAN on LCD

PPG Detection Algorithm

ADC sampled at ~50 Hz, 12-bit resolution, 3.6V full-scale attenuation
10-sample moving average applied for noise reduction
Adaptive baseline tracks slow DC component of signal
Adaptive peak envelope tracks signal upper bound
Beat detected on rising edge exceeding 55% of amplitude above baseline
Flag resets when signal falls below 35% of amplitude (hysteresis)
BPM = average of 4 most recent valid inter-beat intervals
BPM zeroed after 3.5s without a detected beat
Finger contact confirmed when ADC reading exceeds FINGER_ON threshold (1700)

Operating Modes

Boot Sequence

On power-on the device performs the following sequence automatically:

Serial initialization and reset reason diagnostic printout to serial monitor
I2C bus initialization at 400 kHz on pins A4 and A5
BNO055 initialization and external crystal enable
SPI bus and TFT display initialization
Boot animation — 8 BMP frames loaded sequentially from microSD, 1.25s per frame
NeoPixel strip initialization at 40/255 brightness
HX711 initialization, power-up, and tare to zero
FreeRTOS sensor tasks spawned on Core 0
Mode selection screen displayed
User selects mode via button
IMU orientation baseline captured in current resting position

Emergency Mode

Activated by a short press of the start button.

Wi-Fi fully disabled — no radio activity during emergency
100 BPM metronome starts immediately
Live CPM, force, tilt, and PPG status shown on TFT display
NeoPixel force bar updates at 40 Hz
After 20s of inactivity (no load cell change exceeding 10 lb), all sensor tasks are suspended in RAM and device enters software idle mode at approximately 95 mA
To wake: hold button for 2 seconds; all peripherals reinitialize and device returns to mode selection

Training Mode

Activated by holding the button for 3 seconds.

Wi-Fi connection attempted with 10s timeout
If no network found: graceful offline fallback, session runs locally, no upload at end
2-minute timed session with countdown timer displayed on LCD bottom-right
Sensor metrics sampled every 3s into running sum accumulators
At session end: averages computed, JSON serialized manually, HTTP POST sent to Azure endpoint
Session summary displayed on LCD regardless of upload success
Wi-Fi fully shut down after upload; device returns to mode selection

User Operation

Emergency Mode Step-by-Step

Remove CPRPal from first aid kit and slide power switch on. Keep device still on a flat surface during the ~5 second boot and IMU calibration sequence.
Call 911 immediately or direct a bystander to call. Do not delay emergency services.
Place CPRPal face-up on the center of the patient's chest, between the nipple line, with the LCD visible to you.
Short-press the start button once. Emergency Mode begins and the metronome starts at 100 BPM.
Place both hands on top of the device and compress in time with the beep and amber LED ring.
Watch the side LED bar: yellow = push harder, green = good depth, red = ease up.
Watch LCD TILT row: ADJUST or LEAN means recheck your body position and ensure force is going straight down.
Watch LCD PPG row: Heartbeat detect means consider pausing to reassess for pulse.
Continue compressions until EMS arrives or AED is ready.

Training Mode Step-by-Step

Power on and hold button for 3 seconds until "Training selected" appears on LCD.
Device connects to Wi-Fi and displays IP address on successful connection.
Scan QR code on device or navigate to the dashboard URL on a phone or computer.
Begin compressions following the same LED and LCD feedback as Emergency Mode.
2-minute countdown timer shown at bottom right of LCD throughout session.
At session end, summary shown on LCD and data uploaded to dashboard if Wi-Fi available.

Feedback Reference

Channel	States	Meaning
LCD — CPM	WAITING / TOO SLOW / GOOD / TOO FAST	Compression rate guidance toward 100-120 CPM
LCD — Force	TOO LIGHT / GOOD / TOO STRONG	Compression depth guidance toward AHA target
LCD — Tilt	GOOD / ADJUST / LEAN	Posture and off-axis warning
LCD — PPG	BPM value / Heartbeat detect / Missing heartbeat	Supplemental pulse status
NeoPixel bar	Yellow / Green / Red	Visual force classification
Buzzer + rhythm LEDs	100 BPM beep + amber flash	Compression pacing metronome

Signal Processing

Force Classification

Force Range	LED Color	LCD Status	Meaning
< 5 lb	Off	—	No significant contact
5–75 lb	Yellow	TOO LIGHT	Insufficient compression force
75–110 lb	Green	GOOD	Target depth range
> 110 lb	Red	TOO STRONG	Excessive force

CPM Classification

CPM Range	LCD Status
< 1	WAITING
1–99	TOO SLOW
100–120	GOOD
> 120	TOO FAST

Tilt Classification

Tilt Magnitude	LCD Status
< 5 degrees	GOOD
5–10 degrees	ADJUST
> 10 degrees	LEAN

Power System

Rail Assignments

Rail	Powered Components
3.3V	ESP32-S3, BNO055 IMU, PPG sensor, piezo buzzer
5V	ILI9341 LCD, NeoPixel strips (18 px total), HX711 amplifier

Power Budget

Operating Mode	Current Draw	Notes
Peak startup	~235 mA	Boot animation with full brightness LEDs and LCD
Normal (Emergency Mode)	~155 mA	Colored LEDs at 40/255, dim LCD, all sensors active
Idle (tasks suspended)	~95 mA	LCD dim showing idle screen, ESP32 running, all else off
Training Mode	~195 mA	Normal + 40 mA Wi-Fi radio, no idle mode

At 155 mA normal draw with a 2700 mAh 4xAA pack, theoretical runtime is approximately 17 hours. Measured battery life exceeded 10 hours under mixed-use testing.

Cloud and Web Application

Dashboard URL

https://gentle-stone-0c20e761e.6.azurestaticapps.net/

The dashboard is a static web application hosted on Microsoft Azure Static Web Apps using the free tier only. No charges are incurred.

Data Flow

ESP32 collects sensor data every 3s during Training Mode session
  -> Averages computed at 2-minute session end
  -> JSON payload manually serialized (no external library)
  -> HTTP POST to Azure Function API endpoint
  -> Azure processes and stores payload
  -> Dashboard displays session summary

JSON Payload Format

{
  "deviceId": "nano-esp32-test",
  "timestamp": "123456789",
  "mode": "TRAINING",
  "sessionType": "summary",
  "ppg": 1800,
  "imuY": 2.5,
  "imuZ": -1.2,
  "compressionRate": 110,
  "compressionDepth": 85.3,
  "recoil": 1,
  "status": "Good compressions",
  "sampleCount": 40,
  "sessionDurationMs": 120000
}

Status String Logic

Condition	Status Field Value
Rate > 125 CPM or depth > 6.2 lb	`"Bad: too fast/deep"`
Rate < 100 CPM or depth < 5.0 lb or recoil == 0	`"Warn: shallow/recoil"`
All within range	`"Good compressions"`

Offline Fallback

If Wi-Fi is unavailable when Training Mode is entered:

Device displays offline warning on LCD
Full 2-minute session runs locally with all normal feedback active
Session summary displayed on LCD at session end
HTTP POST upload skipped

Setup and Installation

Requirements

Arduino IDE 2.x
Arduino ESP32 board package installed via Boards Manager
- Search for esp32 by Espressif Systems
- Select board: Arduino Nano ESP32

Step 1 — Install Required Libraries

Open Arduino IDE and go to Sketch -> Include Library -> Manage Libraries. Search for and install each of the following:

Library	Author
Adafruit BNO055	Adafruit
Adafruit BusIO	Adafruit
Adafruit EPD	Adafruit
Adafruit FT6206 Library	Adafruit
Adafruit GFX Library	Adafruit
Adafruit HX8357 Library	Adafruit
Adafruit ILI9341	Adafruit
Adafruit ImageReader Library	Adafruit
Adafruit NeoMatrix	Adafruit
Adafruit NeoPixel	Adafruit
Adafruit seesaw Library	Adafruit
Adafruit SH110X	Adafruit
Adafruit SPIFlash	Adafruit
Adafruit SSD1331 OLED Driver Library for Arduino	Adafruit
Adafruit SSD1351 library	Adafruit
Adafruit ST7735 and ST7789 Library	Adafruit
Adafruit STMPE610	Adafruit
Adafruit TouchScreen	Adafruit
Adafruit TSC2007	Adafruit
Adafruit Unified Sensor	Adafruit
HX711	bogde
PulseSensor Playground	World Famous Electronics
SdFat - Adafruit Fork	Adafruit

WiFi and HTTPClient are included with the ESP32 Arduino core and do not require separate installation.

Step 2 — Prepare the microSD Card

The boot animation requires 8 BMP image files placed in the root of a FAT32-formatted microSD card. Name the files exactly as follows:

/Step0.bmp
/Step1.bmp
/Step2.bmp
/Step3.bmp
/Step4.bmp
/Step5.bmp
/Step6.bmp
/Step7.bmp

Each image should be a 24-bit BMP at 240x320 pixels to match the ILI9341 display resolution. If the SD card fails to mount, the firmware will halt and display an error on the TFT screen.

Step 3 — Configure Wi-Fi Credentials

Open ArduinoCode/CPRPal.ino and locate the following lines near the top of the file:

const char* WIFI_SSID     = "YOUR_SSID_HERE";
const char* WIFI_PASSWORD = "YOUR_PASSWORD_HERE";

Replace with your Wi-Fi network credentials. Wi-Fi is only used during Training Mode. Emergency Mode runs fully offline regardless of these settings.

Note: The ESP32 posts directly to the Azure cloud endpoint over any internet-connected Wi-Fi network. The viewing device does not need to be on the same network as the ESP32.

Step 4 — Select Board and Port

In Arduino IDE:

Go to Tools -> Board -> esp32 -> Arduino Nano ESP32
Go to Tools -> Port -> select the correct COM port for your device
If the board is not recognized, hold the BOOT button on the ESP32 while connecting USB

Step 5 — Flash Firmware

Click Upload in Arduino IDE. Open the Serial Monitor at 115200 baud to view boot diagnostics, reset reason history, sensor readings, and calibration output.

Step 6 — Calibrate the Load Cell

Power on the device and let it boot fully
Open Serial Monitor at 115200 baud
Place a known weight on the device
Use + and - keys in Serial Monitor to adjust calibration factor until the displayed value matches the known weight
Note the final calibration factor and update calibration_factor in the sketch to save it permanently

Library Dependencies

Library	Purpose in CPRPal
Adafruit BNO055	BNO055 IMU driver and Euler angle output
Adafruit BusIO	I2C/SPI abstraction layer required by Adafruit sensor libraries
Adafruit GFX Library	Core graphics primitives for TFT display rendering
Adafruit ILI9341	ILI9341 TFT display driver
Adafruit ImageReader Library	BMP image loading from SD card for boot animation
Adafruit NeoPixel	WS2812B addressable LED control for force bar and rhythm strip
Adafruit NeoMatrix	NeoPixel matrix support (dependency)
Adafruit Unified Sensor	Unified sensor abstraction required by BNO055 driver
Adafruit SPIFlash	SPI flash support (alternative storage backend)
SdFat - Adafruit Fork	SD card FAT filesystem for microSD BMP loading
Adafruit EPD	E-paper display support (dependency)
Adafruit FT6206 Library	Capacitive touchscreen support (dependency)
Adafruit HX8357 Library	HX8357 display driver (dependency)
Adafruit seesaw Library	Adafruit seesaw breakout support (dependency)
Adafruit SH110X	OLED display driver (dependency)
Adafruit SSD1331 OLED Driver	SSD1331 OLED support (dependency)
Adafruit SSD1351 library	SSD1351 OLED support (dependency)
Adafruit ST7735 and ST7789 Library	ST77xx display drivers (dependency)
Adafruit STMPE610	Touch controller support (dependency)
Adafruit TouchScreen	Resistive touchscreen support (dependency)
Adafruit TSC2007	Touch controller support (dependency)
HX711 by bogde	HX711 load cell amplifier driver
PulseSensor Playground	PPG pulse sensor signal processing

CPRPal is a prototype device developed for educational purposes. It is not a certified medical device. Always call emergency services first in a cardiac emergency.

Name		Name	Last commit message	Last commit date
3D_Files		3D_Files
Arduino_Code		Arduino_Code
CPRPal_Website_API		CPRPal_Website_API
PCB		PCB
.DS_Store		.DS_Store
CPRPalRendering.png		CPRPalRendering.png
CPRPal_FinalBuild.png		CPRPal_FinalBuild.png
CPRPal_PCB.png		CPRPal_PCB.png
Component_Implementation.png		Component_Implementation.png
LICENSE		LICENSE
README.md		README.md
parts_list.xlsx		parts_list.xlsx

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