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- De Giovanni, Elisabetta, et al.
(författare)
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Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors
- 2021
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Ingår i: IEEE Transactions on Emerging Topics in Computing. - 2168-6750. ; 9:4, s. 1654-1666
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Tidskriftsartikel (refereegranskat)abstract
- In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel computing capabilities that can enable prevention diagnosis and treatment of diseases in ambulatory or home-based setups. In this article, we explore the benefits of the parallelization capabilities and computing heterogeneity of new wearable sensors in the context of a personalized online atrial fibrillation (AF) prediction method for daily monitoring. First, we apply optimizations to a single-core design to reduce energy, based on patient-specific training models. Second, we explore multi-core and memory banks configuration changes to adapt the computation and storage requirements to the characteristics of each patient. We evaluate our methodology on the Physionet Prediction Challenge (2001) publicly available database, and assess the energy consumption of single-core (ARM Cortex-M3 based) and new ultra-low power multi-core architectures (open-source RISC-V based) for next-generation of wearable platforms. Overall, our exploration at the application level highlights that a parallelization approach for personalized AF in multi-core wearable sensors enables energy savings up to 24% with respect to single-core sensors. Moreover, including the adaptation of the memory subsystem (size and number of memory banks), in combination with deep sleep energy saving modes, can overall provide total energy savings up to 34%, depending on the specific patient.
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| 2. |
- Momeni, Niloofar, et al.
(författare)
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CAFS : Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices
- 2022
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 69:3, s. 1072-1084
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Today, stress monitoring on wearable devices is challenged by the tension between high-detection accuracy and battery lifetime driven by multimodal data acquisition and processing. Limited research has addressed the classification cost on multimodal wearable sensors, particularly when the features are cost-dependent. Thus, we design a Cost-Aware Feature Selection (CAFS) methodology that trades-off between prediction-power and energy-cost for multimodal stress monitoring. Methods: CAFS selects the most important features under different energy-constraints, which allows us to obtain energy-scalable stress monitoring models. We further propose a self-aware stress monitoring method that intelligently switches among the energy-scalable models, reducing energy consumption. Results: Using CAFS methodology on experimental data and simulation, we reduce the energy-cost of the stress model designed without energy constraints up to 94.37%. We obtain 90.98% and 95.74% as the best accuracy and confidence values, respectively, on unseen data, outperforming state-of-the-art studies. Analyzing our interpretable and energy-scalable models, we showed that simple models using only heart rate (HR) or skin conductance level (SCL), confidently predict acute stress for HR>93.30BPM and non-stress for SCL< 6.42 μS, but, outside these values, a multimodal model using respiration and pulse wave's features is needed for confident classification. Our self-aware acute stress monitoring proposal saves 10x energy and provides 88.72% of accuracy on unseen data. Conclusion: We propose a comprehensive solution for the cost-aware acute stress monitoring design addressing the problem of selecting an optimized feature subset considering their cost-dependency and cost-constraints. Significant: Our design framework enables long-term and confident acute stress monitoring on wearable devices.
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