A Low-Cost Wireless Platform for Multi-Channel Signal Processing
Faculty Mentor Information
Dr. Benjamin Johnson, Boise State University; and Ant Lakatos, Boise State University
Presentation Date
7-16-2026
Abstract
Medical-grade monitoring equipment often requires high performance standards that increase cost and limit accessibility. Low-cost devices may provide useful physiological monitoring in resource-limited settings or during emergencies before patients reach clinical care. We developed a low-cost wireless platform for multichannel signal acquisition, visualization, storage, and analysis, with potential application to ECG monitoring when integrated with an analog front end. The system uses two Arduino Nano ESP32-S3 boards configured as transmit and receive nodes. To benchmark the platform, we generated sine waves using an Analog Discovery 2 and sampled them with the transmitter’s built-in ADC. The transmitter sent data wirelessly using the ESP-NOW protocol. The receiver then forwarded the signals to MATLAB through serial communication for visualization, storage, and analysis. We sampled three ADC inputs simultaneously at 10 kS/s per channel, for an aggregate sampling rate of 30 kS/s. RMS and peak-to-peak noise voltages remained below 9 mV and 127 mV, respectively. For a 1 kHz, 3.24 V peak-to-peak input signal, total harmonic distortion remained below 1%. These results show that the platform can support low-cost wireless multichannel data acquisition for future physiological monitoring applications.
A Low-Cost Wireless Platform for Multi-Channel Signal Processing
Medical-grade monitoring equipment often requires high performance standards that increase cost and limit accessibility. Low-cost devices may provide useful physiological monitoring in resource-limited settings or during emergencies before patients reach clinical care. We developed a low-cost wireless platform for multichannel signal acquisition, visualization, storage, and analysis, with potential application to ECG monitoring when integrated with an analog front end. The system uses two Arduino Nano ESP32-S3 boards configured as transmit and receive nodes. To benchmark the platform, we generated sine waves using an Analog Discovery 2 and sampled them with the transmitter’s built-in ADC. The transmitter sent data wirelessly using the ESP-NOW protocol. The receiver then forwarded the signals to MATLAB through serial communication for visualization, storage, and analysis. We sampled three ADC inputs simultaneously at 10 kS/s per channel, for an aggregate sampling rate of 30 kS/s. RMS and peak-to-peak noise voltages remained below 9 mV and 127 mV, respectively. For a 1 kHz, 3.24 V peak-to-peak input signal, total harmonic distortion remained below 1%. These results show that the platform can support low-cost wireless multichannel data acquisition for future physiological monitoring applications.