← Back to research

Affordable ECG Sensing: A Machine Learning-Enhanced Framework for Scalable Human-Machine Interaction in Telehealth

Signals on the skin
Machine learning bridges gaps
Hearts share their rhythms
ECG monitoring
Human machine interaction
Heart rate variability
Machine learning
Low-cost sensors
Telehealth

Yashwant Kumar, Shreya Garg, Peeyush Agarwal, Kulbhushan Chand, Arnav Bhavsar, and Varun Dutt, “Affordable ECG Sensing: A Machine Learning-Enhanced Framework for Scalable Human-Machine Interaction in Telehealth,” IEEE Sensors Journal (2025), doi: 10.1109/JSEN.2025.3593819

Authors
Affiliations

Yashwant Kumar

Indian Institute of Technology Mandi, India

Shreya Garg

Indian Institute of Technology Mandi, India

Peeyush Agarwal

Indian Institute of Technology Mandi, India

Kulbhushan Chand

IIT Mandi iHub and HCI Foundation, Indian Institute of Technology Mandi, India

Arnav Bhavsar

Indian Institute of Technology Mandi, India

Varun Dutt

Indian Institute of Technology Mandi, India

Published

August 2025

Doi

10.1109/JSEN.2025.3593819

Abstract

Affordable and accurate cardiovascular monitoring is a pervasive issue in resource-constrained and rural settings, where sophisticated clinical-grade electrocardiogram (ECG) devices are exorbitantly expensive. Low-cost ECG sensors available today are prone to motion artifacts and noisy signals, limiting their use to heart rate variability (HRV) estimation. To fill this gap, we present a proof-of-concept system calibrating noisy, wearable ECG-based HRV measurements using machine learning (ML) models, against a gold standard clinical-grade sensor. Our objective is to evaluate the feasibility of accurate HRV prediction i.e., SDNN and RMSSD using low-cost sensors (AD8232, MAX30003) using ensemble and regression-based ML models. Data were collected in semi-controlled wearable settings, digital-filtered preprocessed, and processed with model ensembles optimized for generalizability. The top-performing models yielded RMSEs of 14.09 ms (SDNN) and 9.09 ms (RMSSD), with sensor type variable performance. In addition to signal calibration, the system includes a real-time human–machine interface (HMI) with wireless ECG visualization and feedback, integrated by an IoT-capable microcontroller. While preliminary and unclinically validated, our results suggest ML-augmented low-cost ECG systems to be feasible for scaled telehealth and affect-aware applications.

Important figures

Figure 1: The weighted ensemble learning framework for HRV parameter prediction from AD8232 and MAX30003 low-cost sensor data against BitBrain reference recordings.

Citation

 Add to Zotero 

@article{kumar_affordable_2025,
    title = {Affordable {ECG} {Sensing}: {A} {Machine} {Learning}-{Enhanced} {Framework} for {Scalable} {Human}-{Machine} {Interaction} in {Telehealth}},
    author = {Kumar, Yashwant and Garg, Shreya and Agarwal, Peeyush and Chand, Kulbhushan and Bhavsar, Arnav and Dutt, Varun},
    doi = {10.1109/JSEN.2025.3593819},
    journal = {IEEE Sensors Journal},
    year = {2025},
    volume = {TBA},
    number = {TBA},
    pages = {TBA}}