Lightweight Compressed Sensing (CS) and Partial DCT Based Compression Schemes for Energy-Efficient Wearable PPG Monitoring Devices

Reddy, Gangireddy Narendra Kumar; Manikandan, M. Sabarimalai; Murty, N. V. L. Narasimha

doi:10.1109/inhence52833.2021.9537262

Cited by 17 publications

(3 citation statements)

References 25 publications

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“…From the past PPG data compression studies as reported in literature, it can be observed that achieving high compression ratio is the main objective of existing methods at the cost of computational resources including the battery power, high speed processor and memory space [3], [22]. Furthermore, real-time implementation of PPG data compression was not addressed in the past studies by considering constraints of wearable PPG sensing devices.…”

Section: B Key Limitations Of Existing Ppg Processing Systemsmentioning

confidence: 99%

“…Moreover, energy consumption analysis was not studied that is most important not only for computing the percentage of energy saving but also for knowing energy consumption of the compression method. Further, most compression methods can enable higher compression of PPG signals but not suitable for extracting the vital parameter(s) directly in the compressed domain or not an integral part of compressed algorithms [3], [22]. In such scenarios, additional signal processing techniques were used to first reconstruct the original PPG signal for estimating the PR and/or RR parameter(s) in real-time or on-device vital sign estimation application that demands more computational power and resources which are constraints of affordable wearable multi-parameter health monitoring devices.…”

Section: B Key Limitations Of Existing Ppg Processing Systemsmentioning

confidence: 99%

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Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

et al. 2023

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Objective: Due to the high demands of tiny, compact, lightweight and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often corrupted severely under ambulatory and exercise recording conditions that leads to frequent false alarms. Method: In this paper, we propose a unified qualityaware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding technique for jointly performing signal quality assessment (SQA), data compression and pulse rate (PR) and respiration rate (RR) estimation without use of different domains of signal processing techniques that can be achieved by using the features extracted from the smoothed prediction error signal. Results: By using the five standard PPG databases, the performance of the proposed unified framework is evaluated in terms of compression ratio (CR), mean absolute error (MAE), false alarm reduction rate (FARR), processing time (PT) and energy saving (ES). The compression, PR and RR estimation and SQA results are compared with that of the existing methods and also with results of uncompressed PPG signals with sampling rates of 125 Hz and 25 Hz. Conclusion: The proposed unified quality-aware framework achieves an average CR of 4%, SQA (Se of 92.00%, FARR of 84.87%), PR (MAE: 0.46 ±1.20) and RR (MAE: 1.75 (0.65-4.45), PT (sec) of 15.34 ±0.01) and ES of 70.28% which outperforms the results of uncompressed PPG signal with a sampling rate of 125 Hz. Significance: Arduino Due computing platform based implementation demonstrates the real-time feasibility of the proposed unified quality-aware PR-RR estimation and data compression and transmission framework on the limited computational resources. Thus, it has great potential in improving energy-efficiency and trustworthiness of wearable and edge PPG monitoring devices.

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Section: B Key Limitations Of Existing Ppg Processing Systemsmentioning

confidence: 99%

Section: B Key Limitations Of Existing Ppg Processing Systemsmentioning

confidence: 99%

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

et al. 2023

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“…In addition, Adler et al propose a sustainable CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires significantly less memory to store the sensing matrix [21]. Reddy et al present an effective lightweight data-reduction method by investigating the performance of CS-based and partial discrete cosine transform (DCT)-based compression methods [22]. However, these methods still have some drawbacks; for example, they are not flexible enough, and the reconstruction quality depends on the complex design of the sampling matrix.…”

Section: Introductionmentioning

confidence: 99%

Learning to be ethnic : the case of Tibetans in Minzu University of China

Yang¹,

阳妙艳²

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Optimized robust learning framework based on big data for forecasting cardiovascular crises

Elseddeq,

Elghamrawy,

Eldesouky

et al. 2024

Sci Rep

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Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy, necessitating the incorporation of robust pre-processing techniques, including data cleaning, preparation, normalization, and addressing imbalances. These steps are crucial for generating a robust dataset for training. Designing frameworks capable of handling such data without compromising efficiency is essential to ensuring robustness. This research aims to propose a novel healthcare framework that selects the best features and enhances performance. This robust deep learning framework, called (R-DLH2O), is designed for forecasting cardiovascular crises. Unlike existing methods, R-DLH2O integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This multi-phase approach ensures superior accuracy and efficiency in crisis prediction, offering a significant advancement in healthcare analytics. H2O is utilized in the R-DLH2O framework for processing big data. The main improvement of this paper lies in the unique form of the Whale Optimization Algorithm (WOA), specifically the Modified WOA (MWOA). The Gaussian distribution approach for random walks was employed with the diffusion strategy to choose the optimal MWOA solution during the growth phase. To validate the R-DLH2O framework, six performance tests were conducted. Surprisingly, the MWOA-2 outperformed other heuristic algorithms in speed, despite exhibiting lower accuracy and scalability. The suggested MWOA was further analyzed using benchmark functions from CEC2005, demonstrating its advantages in accuracy and robustness over WOA. These findings highlight that the framework’s processing time is 436 s, mean per-class error is 0.150125, accuracy 95.93%, precision 92.57%, and recall 93.6% across all datasets. These findings highlight the framework’s potential to produce significant and robust results, outperforming previous frameworks concerning time and accuracy.

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Lightweight Compressed Sensing (CS) and Partial DCT Based Compression Schemes for Energy-Efficient Wearable PPG Monitoring Devices

Cited by 17 publications

References 25 publications

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Learning to be ethnic : the case of Tibetans in Minzu University of China

Optimized robust learning framework based on big data for forecasting cardiovascular crises

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