Overview
Background
Research Fellow at The University of Queensland specialising in wearable and human movement analytics, with expertise in signal processing, applied machine learning, and real-world validation.
My work focuses on how movement, stress, sleep, and pain interact over time, using longitudinal modelling and wearable sensors to study mechanisms behind chronic pain flare-ups. I build data pipelines and analytical frameworks that make wearable-derived measures more reliable in free-living conditions.
Key areas:
- Wearable sensor analytics (IMU, GNSS, heart rate and HRV)
- Signal validation and benchmarking in real-world settings
- Longitudinal modelling of behaviour and physiology
- Field-based biomechanics and endurance movement research
I also lead Metric Trails, a research and standards effort focused on high-precision trail measurement and terrain analytics to improve reproducibility, safety, and fairness in trail and mountain running research.
I collaborate with academic and industry teams interested in wearable validation, study design, and robust modelling for real-world health and performance applications.
Availability
- Dr Raimundo Sanchez is:
- Available for supervision
- Media expert
Fields of research
Qualifications
- Doctor of Philosophy of Systems Engineering, Universidad Adolfo Ibáñez
Research interests
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Real-world wearable validation
I develop methods to validate and benchmark wearable-derived signals in free-living conditions, focusing on data quality, uncertainty, and reproducibility. This includes IMU, GNSS, and heart rate/HRV, and practical frameworks to compare devices, algorithms, and protocols in the real world.
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Field biomechanics and endurance movement
I investigate human locomotion in outdoor environments, with a focus on trail and mountain running. This includes field-based biomechanics, sensor-based movement analysis, and study designs that capture real-world variability in terrain, pacing, fatigue, and movement strategies.
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High-precision GNSS and terrain analytics
I develop geospatial methods to improve measurement accuracy for outdoor locomotion research. This includes high-precision GNSS workflows and terrain analytics to reduce bias in distance, elevation gain, speed, and gradient, supporting safer, fairer, and more reproducible trail and mountain metrics.
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Robust time-series ML for wearables
I build applied machine learning approaches for wearable time-series that remain reliable under missing data, drift, and confounding. My focus is on feature engineering, model evaluation, and interpretability for real-world health and performance applications.
Research impacts
My research improves how wearable sensor data is used to support real-world health and performance decisions. In practice, this means turning noisy, everyday movement and physiology data into measures and models that are trustworthy outside the lab.
In pain research, I help build longitudinal wearable-based frameworks to understand how stress, sleep, movement, and pain interact over time, with the goal of identifying mechanisms and predictors of chronic pain flare-ups. This supports better monitoring approaches and informs the design of interventions that can be evaluated in daily life, not only in controlled settings.
In field-based biomechanics and endurance movement research, I develop methods to increase measurement accuracy and reproducibility. Through Metric Trails, we apply high-precision GNSS and terrain analytics to reduce bias in common metrics such as distance, elevation gain, speed and gradient on trails and mountains. This has practical value for:
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Safer and more consistent course and training-load measurement
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Fairer comparisons in trail-running events and platforms
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More reproducible scientific studies using outdoor movement data
Overall, the impact is improved validity, transparency, and decision-quality when wearables are used in real-world environments.
Works
Search Professor Raimundo Sanchez’s works on UQ eSpace
2020
Journal Article
Uso de dispositivos digitales en el seguimiento de un Trail Runner. Estudio de caso (Use of digital devices to follow a Trail Runner. Case study)
Sánchez, Raimundo and Nieto-Jimenez, Claudio (2020). Uso de dispositivos digitales en el seguimiento de un Trail Runner. Estudio de caso (Use of digital devices to follow a Trail Runner. Case study). Retos (38), 582-586. doi: 10.47197/retos.v38i38.77105
2020
Journal Article
Use of digital devices to follow a Trail Runner. Case study Uso de dispositivos digitales en el seguimiento de un Trail Runner. Estudio de caso
Sánchez, Raimundo and Nieto-Jiménez, Claudio (2020). Use of digital devices to follow a Trail Runner. Case study Uso de dispositivos digitales en el seguimiento de un Trail Runner. Estudio de caso. Retos, 83, 582-586. doi: 10.47197/retos.v38i38.77105
2017
Journal Article
Early successional patterns of bacterial communities in soil microcosms reveal changes in bacterial community composition and network architecture, depending on the successional condition
Rodríguez-Valdecantos, Gustavo, Manzano, Marlene, Sánchez, Raimundo, Urbina, Felipe, Hengst, Martha B., Lardies, Marco Antonio, Ruz, Gonzalo A. and González, Bernardo (2017). Early successional patterns of bacterial communities in soil microcosms reveal changes in bacterial community composition and network architecture, depending on the successional condition. Applied Soil Ecology, 120, 44-54. doi: 10.1016/j.apsoil.2017.07.015
2016
Journal Article
Identifying an optimal analysis level in multiscalar regionalization: A study case of social distress in Greater Santiago
Garreton, Matias and Sánchez, Raimundo (2016). Identifying an optimal analysis level in multiscalar regionalization: A study case of social distress in Greater Santiago. Computers, Environment and Urban Systems, 56, 14-24. doi: 10.1016/j.compenvurbsys.2015.10.007
2016
Conference Publication
Today's agenda: Building LATAM: Our history
Jovel, Carlos and Sánchez, Raimundo (2016). Today's agenda: Building LATAM: Our history. Airline Group of the International Federation of Operations Research Societies (AGIFORS).
Supervision
Availability
- Dr Raimundo Sanchez is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
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Real-world validation and benchmarking of wearable signals
Develop a practical validation framework for wearable-derived signals in free-living settings, focusing on data quality, uncertainty, and reproducibility. The project will benchmark IMU, GNSS, and heart rate/HRV metrics across devices and protocols, and produce a reusable analysis pipeline. Outcomes include validation datasets, benchmark metrics, and recommendations for robust study design. Suitable for candidates with Python/R, time series, and interest in real-world wearables.
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Field biomechanics in trail and mountain locomotion using wearables
Quantify movement strategies in outdoor terrain using wearable sensors, linking biomechanics to terrain, pacing, fatigue, and performance. The project will combine IMU-based movement features with GNSS-derived context, with field protocols designed for ecological validity. Outcomes include validated features for outdoor locomotion research and guidance on best-practice field measurement. Suitable for candidates with biomechanics or sensor analytics background.
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High-precision GNSS and terrain analytics for reproducible trail metrics
Improve measurement accuracy of distance, elevation gain, speed, and gradient in outdoor locomotion by combining high-precision GNSS workflows and terrain analytics. The project will evaluate sources of bias in common tools and propose reproducible standards and pipelines for research-grade trail measurement. Outcomes include methods, datasets, and practical standards for field studies and events. Suitable for candidates with geospatial analytics, GNSS, or applied modelling.
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Robust time-series ML for activity classification and anomaly detection
Develop robust ML methods for wearable time-series to classify activity types and detect anomalies under real-world noise, drift, and mislabeling. Use GNSS and optional IMU/HR features to build models that generalize across users and environments, with emphasis on evaluation and interpretability. Outcomes include deployable modelling recipes and validation results relevant to fitness and health platforms. Suitable for candidates with ML and feature engineering skills.
Supervision history
Completed supervision
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2025
Doctor Philosophy
Wearable Sensor Informed Movement Monitoring in Elite Women's Water Polo
Associate Advisor
Other advisors: Professor Bill Vicenzino, Dr Nathalia Costa, Associate Professor Michelle Smith
Media
Enquiries
Contact Dr Raimundo Sanchez directly for media enquiries about:
- artificial intelligence
- data science
- fitness trackers
- GPS tracking
- health wearables
- hiking
- human movement
- mapping
- mountain
- movement tracking
- physical activity
- running science
- sensor data
- smartwatches
- sports technology
- terrain
- trail running
- wearable technology
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