Overview
This project aims to address the critical gap between the huge potential offered by wearable sensors and their still very limited adoption in clinical practice. This will be achieved by creating scientifically rigorous data processing pipelines that provide users with clear and interpretable information about data accuracy and uncertainty.
About this opportunity
This project builds on an 8-year research collaboration between the Primary Supervisor (Dr Ferrero) and the Partner (Dr Kumar, a consultant paediatric neurologist), aimed at increasing the clinical adoption of wearable technology, through the development of scientifically rigorous software tools that provide clinicians with clear and interpretable information about the accuracy and reliability of data collected by wearable sensors (photoplethysmography, accelerometry, electrodermal activity, and others).
The uniqueness of this team’s research direction, compared to other research carried out in this area, is the focus on a scientifically rigorous and interpretable uncertainty quantification of wearable data, which is missing from the vast majority of studies, especially those using machine learning techniques. An accurate uncertainty quantification is of critical importance for two main reasons: 1) to allow correctly fusing information from multiple sensors, and 2) to allow clinicians or other users to make informed decisions based on the measured data, by distinguishing between actual changes in relevant physiological indicators and artifacts caused by noise/disturbance or signal processing errors.
The Primary Supervisor is uniquely positioned to lead this project, as he is a globally recognised expert in electrical/electronic instrumentation and measurement and uncertainty analysis, as confirmed by his Editor-in-Chief role for the leading international journal in this field, the IEEE Transactions on Instrumentation and Measurement.
Some of the most notable achievements of this research group to date are:
1) A world-first initial attempt to quantify the uncertainty of heart-rate-related indicators calculated from photoplethysmography (PPG) data corrupted by artifacts, through rigorous mathematical analysis (journal paper currently under review).
2) The novel and potentially ground-breaking application of a time-and-frequency-domain method (based on the Taylor-Fourier transform) for the extraction of heart rate and heart rate variability from PPG signals, providing a much better time resolution compared to the state of the art, while also assessing the signal quality [1-3].
In this project, the candidate will build on the existing research, with the following key objectives:
1) Expand the work done on PPG signals, considering also other commonly measured signals (accelerometry, electrodermal activity, and possibly others), to improve the measurement accuracy and uncertainty quantification by fusing information from all those signals.
2) Combine the already-developed Taylor-Fourier analysis with other tools suited for uncertainty quantification (e.g. Gaussian processes) and possibly machine learning techniques, if appropriate.
3) Implement the developed algorithms in a device-agnostic software, allowing clinicians to seamlessly analyse and combine data recorded from a variety of different wearable devices.
The student will be based at the University of Liverpool but will work closely with the Partner (also based in Liverpool) throughout the duration of the project. Opportunities for other collaborations may also be available.
Interested applicants who want to know more about the project are warmly invited to contact the primary supervisor, Dr Roberto Ferrero (Roberto.Ferrero@liverpool.ac.uk).
References:
[1] S. Rahbar et al., IEEE Trans. on Instrum. and Meas., 2025, http://www.doi.org/10.1109/TIM.2025.3621748.
[2] S. Rahbar et al., 2024 IEEE I2MTC, Glasgow, UK, http://www.doi.org/10.1109/I2MTC60896.2024.10560902.
[3] S. Rahbar et al., 2025 IEEE I2MTC, Chemnitz, Germany, http://www.doi.org/10.1109/I2MTC62753.2025.11079169.
Further reading
For a general introduction about potential and challenges of clinical use of wearable devices, please refer to:
- Jamieson, T. J. Chico, S. Jones, N. Chaturvedi, A. D. Hughes, and M. Orini, “A guide to consumer-grade wearables in cardiovascular clinical care and population health for non-experts,” NPJ cardiovascular health, vol. 2, no. 1, p. 44, 2025, https://doi.org/10.1038/s44325-025-00082-6.
- Knowles, A. Smith-Renner, F. Poursabzi-Sangdeh, D. Lu, and H. Alabi, “Uncertainty in current and future health wearables,” Commun. ACM, vol. 61, no. 12, p. 62–67, Nov. 2018, https://doi.org/10.1145/3199201.
For a general introduction about PPG signals and heart rate variability metrics, please refer to:
- Charlton et al., “Detecting beats in the photoplethysmogram: benchmarking open-source algorithms”, Physiol. Meas., vol. 43, 2022, https://doi.org/10.1088/1361-6579/ac826d.
- Shaffer and J. P. Ginsberg, “An overview of heart rate variability metrics and norms,” Frontiers in Public Health, vol. 5, p. 258, 2017, https://doi.org/10.3389/fpubh.2017.00258.
The relevant papers recently published by Dr Ferrero’s group (already cited above) are:
- Rahbar, R. Ferrero, P. A. Pegoraro and S. Toscani, “Application of Taylor–Fourier Analysis to Photoplethysmography Signals for Instantaneous Heart Rate Measurement,” in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-14, 2025, http://www.doi.org/10.1109/TIM.2025.3621748.
- Rahbar, R. Ferrero, P. A. Pegoraro and S. Toscani, “Taylor-Fourier Analysis of Photoplethysmography Signals for Heart Rate Measurement,” 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, United Kingdom, 2024, pp. 1-6, http://www.doi.org/10.1109/I2MTC60896.2024.10560902.
- Rahbar, R. Ferrero, S. Toscani, S. Ronaghi and P. A. Pegoraro, “Photoplethysmography Signal Quality Assessment Using Instantaneous Harmonic Analysis via Taylor-Fourier Method,” 2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Chemnitz, Germany, 2025, pp. 1-6, http://www.doi.org/10.1109/I2MTC62753.2025.11079169.