Elisabetta De Giovanni | EPFL Graph Search

Elisabetta De Giovanni received the M.Sc. degree in Biomedical Engineering, with specialization in Technology for Healthcare, at University of Pavia, Italy, in April 2016. The final thesis, done in the Embedded System Laboratory (ESL) of EPFL, involved applying a smart and low power algorithm to compute vital parameters from physiological signals implemented in an embedded system.

Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise

David Atienza Alonso, Elisabetta De Giovanni, Grégoire Millet, Tomas Teijeiro Campo

Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sudden physiological changes occur to the signals, compromising the robustness of standard algorithms. Methods: Our method, called BayeSlope, is based on unsupervised learning, Bayesian filtering, and non-linear normalization to enhance and correctly detect the R peaks according to their expected positions in the ECG. Furthermore, as BayeSlope is computationally heavy and can drain the device battery quickly, we propose an online design that adapts its robustness to sudden physiological changes, and its complexity to the heterogeneous resources of modern embedded platforms. This method combines BayeSlope with a lightweight algorithm, executed in cores with different capabilities, to reduce the energy consumption while preserving the accuracy. Results: BayeSlope achieves an F1 score of 99.3% in experiments during intense cycling exercise with 20 subjects. Additionally, the online adaptive process achieves an F1 score of 99% across five different exercise intensities, with a total energy consumption of 1.55 ± 0.54 mJ. Conclusion: We propose a highly accurate and robust method, and a complete energy-efficient implementation in a modern ultra-low-power embedded platform to improve R peak detection in challenging conditions, such as during high-intensity exercise. Significance: The experiments show that BayeSlope outperforms state-of-the-art QRS detectors up to 8.4% in F1 score, while our online adaptive method can reach energy savings up to 38.7% on modern heterogeneous wearable platforms.

2022

System-level Design of Adaptive Wearable Sensors for Health and Wellness Monitoring

Elisabetta De Giovanni

In recent years, population aging and the consequent higher incidence of noncommunicable diseases, have increased the need for long-term health monitoring. Moreover, as healthcare cost is projected to grow substantially by 2030 in OECD countries, the demand for portable, easy-to-use and low cost ultra-low power means of monitoring, diagnosis and prevention rises. Wearable sensors technology for remote health and wellness monitoring is an optimal candidate to tackle this problem and has advanced drastically in the last years. However, wearable sensors impose several design constraints. They must process data in real-time and provide highly accurate diagnosis and adapt to different cases. At the same time, to perform long-term monitoring, they must maximize battery lifetime, hence, usability. However, the need for more accurate algorithms and the need to obtain energy-efficient implementations can work against each other. For this reason, enhancing the energy-accuracy trade-off is essential. Several works in the literature have addressed the energy-accuracy trade-off problem. The general approach is to first develop offline methodologies that maximize the algorithm's accuracy, using signal processing and machine learning. Then, these methods are optimized to be implemented as online designs in resource-constrained ultra-low power platforms. However, different problems can occur in these two steps. Most methods use highly variable datasets (mainly having different subjects), others use fixed parameters tailored to specific conditions, which overall decreases the robustness of the algorithm. In traditional single-core devices, some optimizations whose goal is to lower the algorithms' complexity and computational burden, such as downsampling and features reduction, lead to a loss in precision. With the advances of ultra-low power platforms and new machine learning strategies, even more challenges arise. However, these advances allow to exploit the growing capabilities of the platforms and use innovative and more complex strategies that achieve high levels of accuracy, robustness and energy-efficiency. In this thesis, I propose a set of adaptive strategies in the context of remote health and wellness monitoring for an enhanced energy-accuracy trade-off in wearable sensors. First, I present three methodologies for multi-biosignal monitoring and pathology detection, which adapt to the specific physiological conditions by means of personalization to the subject and knowledge acquired from the signal. Second, in the context of modern heterogeneous wearable platforms, I propose a modular approach to software parallelization and hardware acceleration for biomedical applications to maximize the attainable speed-up and, therefore, minimizing energy consumption. Moreover, I propose an approach to scale computing resources and independent memory banks based on the specific characteristics of the patient in modern wearable sensors. Finally, in the context of intensive physical exercise, I propose an online design that adapts to the sudden physiological changes occurring in the signal. This method combines a lightweight algorithm with a more robust though more complex one to reduce energy consumption while maintaining a very high accuracy. Moreover, this adaptive strategy exploits the heterogeneity of modern platforms by matching the complexity of each algorithm with the capabilities of each core, which further enhances the energy-accuracy trade-off.

EPFL2021

Modular Design and Optimization of Biomedical Applications for Ultra-Low Power Heterogeneous Platforms

David Atienza Alonso, Luca Benini, Elisabetta De Giovanni, Benoît Walter Denkinger, Simone Machetti, Fabio Montagna, Miguel Peon Quiros

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multi-core systems with advanced power-saving capabilities and additional heterogeneous components. In this paper, we present an approach that applies optimization and parallelization techniques uncovered by modern ultra-low power platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-second excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17x and energy savings of 41.6% for the multi-core implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead ECG signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a hearbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96x and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multi-core implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally, by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.

2020