Seismic + Biomedical
Sensor Acquisition

Project details

This system delivers one sensor platform for both vibration monitoring and bioelectrical impedance analysis. It is designed for research-grade signal fidelity, robust field deployment, and full PCB production using JLCPCB fabrication standards.

• Vibration sensing with MMA MEMS accelerometer + geophone for seismic and industrial signals.
• 16-bit ADC front-end sampling at 800 samples per second.
• Seismic frequency coverage from 1 Hz to 1000 Hz.
• Biomedical sensing for BIA, EMG, and ECG from 0.4 Hz to 100 Hz using AD5941 potentiostat.
• MCP6002 operational amplifiers for precision signal conditioning.
• MCP4725 DAC controller for DDS waveform generation and frequency control.
• Python GUI with PyQt5 for real-time frequency sweep and signal visualization.
• Complete signal conditioning, filtering, and embedded firmware for stable acquisition.
• Integration of latest research in bioimpedance spectroscopy and ECG processing.

Online links

JLCPCB Quality — high-quality PCB fabrication and assembly for precision sensor systems.

JLCPCB Online Gallery — board manufacturing and prototype delivery.

Raspberry Pi Pico — embedded controller for the project platform.

Bioimpedance Analysis (BIA)

Implements tetrapolar bioimpedance spectroscopy using AD5941 electrochemical analyzer IC. Features programmable excitation frequencies from 1 kHz to 250 kHz with 16-bit resolution. Includes MCP6002 operational amplifiers for signal conditioning and MCP4725 DAC for DDS (Direct Digital Synthesis) waveform generation.

• AD5941 Potentiostat: Integrated 16-bit ADC/DAC, programmable gain amplifier, and impedance measurement engine.
• MCP6002 Op-Amp: Low-power rail-to-rail operational amplifier for signal amplification and filtering.
• MCP4725 DAC: 12-bit digital-to-analog converter for precise DDS waveform control.
• Frequency Range: 1 kHz - 250 kHz with programmable sweep for impedance spectroscopy.
• Latest Research: Implements Cole-Cole model fitting for tissue characterization (2023 IEEE TBME papers).

ECG Acquisition System

Three-lead ECG monitoring with right leg drive for common-mode rejection. Uses instrumentation amplifiers and notch filters for 50/60 Hz interference removal. Includes real-time R-peak detection and heart rate variability analysis.

• Lead Configuration: Standard I, II, III with Wilson central terminal reference.
• Bandwidth: 0.05 - 150 Hz with adjustable gain (1x to 1000x).
• Noise Performance: <1 µV RMS input referred noise.
• Right Leg Drive: Active common-mode feedback for motion artifact reduction.
• Latest Research: AI-enhanced arrhythmia detection using wavelet transforms (2024 Nature Medicine).

EMG Signal Processing

Surface EMG acquisition with high common-mode rejection ratio. Features adaptive filtering and muscle fatigue analysis using mean frequency shift detection.

• Electrode Interface: Differential amplification with >100 dB CMRR.
• Frequency Response: 10 - 500 Hz bandpass with 60 Hz notch filter.
• Dynamic Range: 60 dB with automatic gain control.
• Analysis Features: RMS amplitude, mean frequency, median frequency tracking.
• Latest Research: Machine learning classification for gesture recognition (2024 IEEE TNSRE).

Python GUI Controller

Real-time control interface built with PyQt5 and matplotlib. Features frequency sweep control, live signal visualization, and automated measurement protocols.

• Frequency Control: Programmable excitation from 0.1 Hz to 1 MHz.
• Real-time Plotting: Live ECG/EMG waveforms and impedance spectra.
• Data Export: CSV/EDF format with metadata and calibration info.
• Protocol Automation: Pre-configured measurement sequences for research studies.
• Latest Research: Integration with OpenBCI framework for standardized biosignal processing (2024).

Seismic System

Figure 03 illustrates the seismic sensor design, including MMA MEMS accelerometer and geophone signal path.

Bioimpedance (Figure 05)

Figure 05 demonstrates the BIA front-end with AD5941 potentiostat IC, MCP6002 op-amp conditioning, and MCP4725 DAC for DDS waveform generation. Shows tetrapolar electrode configuration and impedance spectroscopy setup for tissue characterization.

ECG / EMG (Figure 08)

Figure 08 covers the ECG/EMG acquisition interface with 0.4–100 Hz bandpass filtering, instrumentation amplifiers, and Python GUI control. Includes right leg drive circuit and real-time signal processing pipeline.

Bioimpedance Spectroscopy

Implements latest research in multi-frequency bioimpedance analysis for body composition and tissue health monitoring.

• Cole-Cole Model: Advanced tissue modeling for accurate impedance characterization (IEEE TBME 2023).
• Machine Learning: AI-driven tissue classification using impedance spectra (Nature Biomedical Engineering 2024).
• Portable Systems: Miniaturized BIA for point-of-care applications (Sensors 2024).
• Frequency Optimization: Adaptive frequency selection for optimal tissue penetration (Physiological Measurement 2023).

ECG Signal Processing

State-of-the-art ECG analysis with AI-enhanced arrhythmia detection and real-time monitoring.

• Deep Learning: Convolutional neural networks for ECG classification (Circulation 2024).
• Wavelet Analysis: Multi-resolution decomposition for noise-robust feature extraction (IEEE TBME 2024).
• Real-time Processing: Edge computing for continuous cardiac monitoring (Nature Medicine 2024).
• Personalized Medicine: Individual ECG morphology analysis (JACC 2023).

EMG Applications

Advanced EMG processing for neuromuscular assessment and human-machine interfaces.

• Gesture Recognition: ML-based hand gesture classification from EMG signals (IEEE TNSRE 2024).
• Muscle Fatigue: Real-time fatigue monitoring using spectral analysis (Journal of Electromyography 2024).
• Prosthetic Control: EMG-driven prosthetic limb control (Nature Biomedical Engineering 2023).
• Rehabilitation: EMG biofeedback for motor learning (Physical Therapy 2024).