Hello 👋, I'm Tanuj.
I'm currently pursuing a PhD in Sports Technology from Auckland University of Technology (AUT), where I get to combine my passion for human performance, technology and programming.
My current research focus is on leveraging Artifical Intelligence to enhance monitoring tools for athletic performance. While my PhD research focuses on specifically using wearable sensors to predict athlete recovery and readiness, I also enjoy building other tools that can help practioners monitor and measure athlete training and performance. Go peep the current projects I'm working on.
I'm fascinated by how neural networks work, and enjoy keeping up-to-date on new architectures, while digging deeper into what makes these black boxes tick.
Additionally, I am also involved in resistance training research with a focus on strength and hypertrophy research. Check out my publications for more details.
I also enjoy powerlifting, lifting heavy weights in my free time. Once in a while, I decide to compete in powerlifting competitions.
Experience & Education
To view my entire work and education history, please visit my LinkedIn profile.
Current Projects
- Asynchronous sensor fusion with a Kalman Filter to upsample GPS data using IMU data (as part of Rauch et al.,[Preparing for publication]).
- Exploring the relationship between post-exercise fatigue and gait anomalies assessed via a chest-mounted IMU (data collection stage).
- Custom mobile app for chest-detection (i.e., heart-rate detection) and step-count tracking from a BLE wearable sensor (for Gait project)
- Postgresql server and frontend for collections and storing data from lab collection and free-world BLE sensor data (for Gait and Futsal Project)
- Predicting recovery and readiness-to-perform using signals from a multi-sensor chest-mounted device in Futsal athletes (data collection stage)
- Raspberry Pi as an edge server with cloud sync and auto-update capabilities for offline data collection from BLE wearable sensor (for Futsal project)
- Using a monocular camera to estimate the time-force curve for the concentric-phase of a counter-movement jump (piloting completed).
- Convolutional auto-encoder to denoise and compress ambulatory electrocardiogram signal (fine-tuning stage)
- Using a monocular camera to estimate bar-velocity (planning stage).
Patents
Filed
- Fabrication Of Disposable Paper-based Semi-dry Electrode For Electrocardiography Signal Sensing (Oct 2024)
Publications
To view my entire publication list, please visit my ResearchGate profile
In the meanwhile, enjoy my *limited* set of conference photos.
Awards
- Overall Winner – AUT Innovation Challenge (2023)
- AUT Doctoral Scholarship Awardee (2022)
- Outstanding Graduate Student of the Year, University of Tampa (2018)
