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What is PhysisLab?

PhysisLab is an open-source collection of low-cost physics laboratory experiments designed for educational purposes. The project enables students and educators to perform accurate physics measurements using accessible technology: webcams, ESP32 microcontrollers, and standard computers. By combining computer vision, embedded systems, and signal processing, PhysisLab transforms everyday hardware into precision measurement instruments for classical mechanics experiments.

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Educational Philosophy

PhysisLab bridges the gap between theoretical physics and experimental practice by:
  • Reducing costs: Experiments use common hardware instead of expensive lab equipment
  • Teaching multiple disciplines: Students learn physics, programming, electronics, and data analysis simultaneously
  • Providing real-world skills: Experience with OpenCV, Arduino, Python scientific computing, and signal processing
  • Enabling reproducibility: All code is open-source and experiments are well-documented

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Available Experiments

PhysisLab currently includes five major experiment categories:

Free Fall

Measure gravitational acceleration by tracking falling objects using camera-based detection, microcontroller timing with IR sensors, or audio-based impact detection.

Pendulum Motion

Analyze simple pendulum dynamics including period measurement, amplitude decay, and gravitational constant determination using color-based tracking.

Spring-Mass System

Study harmonic oscillation, measure spring constants, calculate damping coefficients, and analyze transfer functions with homography-based calibration.

Projectile Motion

Track parabolic trajectories, measure launch angles and velocities, and experimentally determine gravitational acceleration from 2D motion.

Kinematics

Real-time position and velocity tracking using VL53L0X time-of-flight sensors or ultrasonic distance sensors with advanced filtering (Butterworth, α-β, EMA).

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Three Measurement Approaches

PhysisLab offers flexibility through three distinct measurement methodologies:

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1. Camera-Based (Computer Vision)

Uses OpenCV for color-based object tracking and motion analysis:
  • Technique: HSV color space filtering, contour detection, frame-by-frame tracking
  • Hardware: Any USB webcam (30-60 fps recommended)
  • Calibration: Homography or affine transformations for pixel-to-meter conversion
  • Experiments: Free fall, pendulum, spring-mass, projectile motion, kinematics
  • Advantages: Non-contact measurement, captures full trajectory, rich visual feedback
Example: The free fall camera experiment (FreeFallCam.py) detects when an object crosses two reference lines and calculates time intervals based on frame counting: 
DESIRED_FPS = 10
RESOLUTION = (320, 240)

# Real FPS measurement for accuracy
real_fps = min(fps_from_cap, measured_fps)

# Calculate time from frame difference
delta_frames = frame_end - frame_start
delta_t = delta_frames / real_fps

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2. Microcontroller-Based (ESP32/Arduino)

Uses ESP32 with sensors for high-precision timing:
  • Technique: Interrupt-driven detection, hardware timers, sensor polling with FreeRTOS
  • Hardware: ESP32 microcontroller, IR sensors, time-of-flight (VL53L0X), ultrasonic sensors
  • Timing: Microsecond precision using micros() function
  • Experiments: Free fall timing, kinematics with distance sensors
  • Advantages: High temporal resolution, low latency, compact setup
Example: The free fall microcontroller system (FreeFallEpsfreeRTOSFunciona.ino) uses two IR sensors to measure time intervals: 
#define PIN_INICIO 18
#define PIN_FIN    5

// Capture start time on first sensor trigger
if (!esperandoFin && estadoAntInicio == HIGH && estadoInicio == LOW) {
  tInicio = micros();  // Microsecond precision
  esperandoFin = true;
}

// Capture end time on second sensor trigger
if (esperandoFin && estadoAntFin == HIGH && estadoFin == LOW) {
  tFin = micros();
  uint32_t delta = tFin - tInicio;  // Time in microseconds
}

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3. Audio-Based (Sound Detection)

Uses microphone input to detect impact events:
  • Technique: Real-time audio streaming, RMS threshold detection, latency compensation
  • Hardware: Computer with microphone or headphone jack
  • Sampling: 44.1 kHz sample rate with configurable block sizes
  • Experiments: Free fall (ball drop impacts)
  • Advantages: Minimal setup, detects events invisible to camera
Example: The sound detection system (deteccion_por_sonido.py) monitors audio RMS levels: 
SAMPLE_RATE = 44100
BLOCK_SIZE = 8  # Small buffer for low latency
THRESHOLD = 0.65

# Calculate RMS and detect impacts
audio = indata[:, 0]
rms = np.sqrt(np.mean(audio**2))

if rms > THRESHOLD:
    event_times.append(time.perf_counter())
    print(f"⚡ Event {len(event_times)} detected (RMS={rms:0.4f})")

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Signal Processing & Analysis

All experiments include comprehensive data analysis with:
  • Filtering: Butterworth filters, exponential moving average (EMA), α-β filters
  • Curve fitting: Sinusoidal fitting for oscillations, parabolic fitting for trajectories
  • Visualization: Matplotlib plots for position, velocity, acceleration, phase space, frequency spectra
  • System identification: Transfer function analysis, Bode plots, pole-zero diagrams (spring-mass)

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Bonus: Oscilloscope & Signal Generator

PhysisLab includes a dual-channel oscilloscope and arbitrary waveform generator using ESP32:
  • Oscilloscope: 200 samples/second dual ADC, real-time streaming via serial/WebSocket
  • Signal Generator: Independent DAC channels with sine, square, triangle, sawtooth, and DC waveforms
  • Control: Python GUI interface with FFT analysis capabilities
  • Sample rate: 40 kHz DAC output, configurable ADC sampling

Get Started

Check hardware and software requirements

Installation

Set up your development environment

View Source

Explore the complete source code
Academic Use: PhysisLab is designed for physics education at the university level. All experiments include theoretical background, calibration procedures, uncertainty analysis, and comparison with expected values.

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