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Research and Publications

Evidence-Based Performance Science

Our work is grounded in rigorous scientific research. We continuously contribute to the field of sports science through peer-reviewed publications and collaborative studies.

RECENT RESEARCH HIGHLIGHTS

Image by Sangharsh Lohakare

Genomic Markers for Athletic Performance Optimization

Pioneering study identifying specific genetic markers that correlate with elite athletic performance in Indian athletes, enabling personalized training protocols based on genetic predisposition.

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Athlete Profiling & Performance Assessment Tool

A comprehensive, research-backed athlete profiling platform that integrates physical, biomechanical, and physiological assessments to inform training decisions and injury risk awareness. Deployed across 2,000+ able-bodied and para-athletes, it ensures equitable access to performance science through IIT Madras–validated frameworks.

PUBLICATIONS

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Heart Rate Variability

Evaluating the Utility of a Cardiac Health Assessment Test in Predicting VO2max Obtained from CPET: A Pilot Study

12th IEEE SeGAH 2024

Mridula Badrinarayanan, V Sricharan, Rohan R Jais, N Danush Adhithya, G Sri Gayathri, Sp Preejith

This study investigates a game-based Cardiac Assessment Protocol using wearable ECG and accelerometer data to estimate VOâ‚‚max. In 22 subjects, a machine learning model achieved RMSE values of 5.37 (CPET HR data) and 5.82 (protocol HR data) against CPET ground truth.

 

Results indicate that VOâ‚‚max can be reasonably estimated through a simulated, less invasive alternative to traditional exercise testing.

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Motion Capture & Gait

Ride Profiling for a Single Speed Bicycle Using an Inertial Sensor

2019 IEEE, MeMeA

D. R., P. S.P., M. Sivaprakasam

This paper presents a single-sensor system for monitoring cycling performance on a single-speed bicycle. The rear-wheel-mounted device measures speed, cadence, and derives pedal force, acceleration, and braking metrics, accounting for terrain and slope effects.

 

Data are processed on-device and transmitted to a smartphone for real-time visualization and long-term performance tracking, providing cyclists actionable insights for training and improvement.

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Interested in collaborating on sports science research ?

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Blood Oxygenation

High altitude study on finger reflectance SpO2

2017 IEEE, MeMeA

Preejith S P, R. Hajare, J. Joseph and

M. Sivaprakasam

This paper presents a wrist-worn reflectance pulse oximeter for monitoring SpOâ‚‚ and heart rate, integrated with a smartphone for cloud connectivity.

 

Device calibration was performed during high-altitude exercises, and tests on three volunteers at 4,259 m and 5,360 m showed strong correlation with standard reference measurements, demonstrating its suitability for wearable, minimally intrusive monitoring

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Open Book

Deep Learning Models for Wearables

Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification
 

42nd IEEE EMBC, 2020

Sricharan Vijayarangan, Balamurali Murugesan, R Vignesh, SP Preejith, Jayaraj Joseph, Mohansankar Sivaprakasam

This study enhances deep learning–based ECG arrhythmia diagnosis with interpretability.

 

Grad-CAM and input deletion mask methods visualize CNN and LSTM model saliency, linking predictions to ECG segments. The approach improves clinical understanding while maintaining high classification performance.

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50+

Peer-Reviewed Publications

8

International Collaborations

15+

Ongoing Research Projects

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