Toward lightweight biometric signal processing for wearable devices

Francescon, Roberto; Hooshmand, Mohsen; Gadaleta, Matteo; Grisan, Enrico; Yoon, Seung Keun; Rossi, Michele

doi:10.1109/embc.2015.7319318

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…This makes direct methods more suited for remote patient monitoring than those in the transformation and parameter methods [12]. Although other methods achieve higher compression ratios than direct methods, they consume more time, power and computational resources to be suitable for deployment with wearable sensors and mobile devices [6] [13].…”

Section: Ecg Data Reduction Methodsmentioning

confidence: 99%

“…Most recent real-time ECG compression algorithms such as [5] [7] [24] based on transformation techniques require preprocessing and high computational resources [6] [13]. Thus, they are not suited for real-time ECG reduction on wearable sensors and mobile devices because these devices have limitations of CPU execution time, memory capacity and energy consumption.…”

Section: Ecg Data Reduction Methodsmentioning

confidence: 99%

“…Power=Volts * Amp (5) Energy consumption= Power * Runtime (6) TABLE IV demonstrates that the proposed algorithm consumes a very low memory and energy when implement with a window size of 5. The memory and energy consumption are comparable with the algorithms listed in Table IV and is particularly good given that the algorithm executes in real-time and increases battery life using high CR, minimal CPU, storage requirements and power consumption.…”

Section: Energy Consumption For Wearable Ecg Sensormentioning

confidence: 99%

“…The large datasets and limited processing capacity of devices near the sensors results in the need to consider the near, or real-time reduction of large ECG data sets for critical operations in ambulatory recording systems, ECG signal storage systems and for ECG signal transmission over wireless networks [5] [6]. However, any reduction in ECG signals should not impede clinicians' diagnostic assessments [7].…”

Section: Introductionmentioning

confidence: 99%

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ECG Reduction for Wearable Sensor

Allami

Stranieri

Balasubramanian

et al. 2016

2016 12th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for realtime computation and incremental data arrival. In this paper, we describe a computationally naïve, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10:1 while still maintaining an error rate below 10%.

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Section: Ecg Data Reduction Methodsmentioning

confidence: 99%

Section: Ecg Data Reduction Methodsmentioning

confidence: 99%

Section: Energy Consumption For Wearable Ecg Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ECG Reduction for Wearable Sensor

Allami

Stranieri

Balasubramanian

et al. 2016

2016 12th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

View full text Add to dashboard Cite

show abstract

“…This may become particularly acute in the case of aggregate storage of longitudinal data of large numbers of patients, illustrating the need to partner with technology experts to develop not only strategic approaches but also technical solutions. 22…”

Section: High-priority Areas For Research and Policy Related To Data mentioning

confidence: 99%

Priorities to Overcome Barriers Impacting Data Science Application in Emergency Care Research

Puskarich

Callaway

Silbergleit

et al. 2018

Academic Emergency Medicine

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For a variety of reasons including cheap computing, widespread adoption of electronic medical records, digitalization of imaging and biosignals, and rapid development of novel technologies, the amount of health care data being collected, recorded, and stored is increasing at an exponential rate. Yet despite these advances, methods for the valid, efficient, and ethical utilization of these data remain underdeveloped. Emergency care research, in particular, poses several unique challenges in this rapidly evolving field. A group of content experts was recently convened to identify research priorities related to barriers to the application of data science to emergency care research. These recommendations included: 1) developing methods for cross-platform identification and linkage of patients; 2) creating central, deidentified, open-access databases; 3) improving methodologies for visualization and analysis of intensively sampled data; 4) developing methods to identify and standardize electronic medical record data quality; 5) improving and utilizing natural language processing; 6) developing and utilizing syndrome or complaint-based based taxonomies of disease; 7) developing practical and ethical framework to leverage electronic systems for controlled trials; 8) exploring technologies to help enable clinical trials in the emergency setting; and 9) training emergency care clinicians in data science and data scientists in emergency care medicine. The background, rationale, and conclusions of these recommendations are included in the present article.

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Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

Hooshmand

Zordan

Testa

et al. 2017

IEEE Internet Things J.

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Modern wearable IoT devices enable the monitoring of vital parameters such as heart or respiratory rates (RESP), electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. However, a common issue of wearable technology is that signal transmission is powerdemanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering one dimensional biosignals such as ECG, RESP and PPG, which are often available from commercial wearable IoT devices, we provide a throughout review of existing biosignal compression algorithms and introduce novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of their compression, reconstruction and energy consumption performance. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-topeak signal amplitude. Avenues for future research are finally discussed.

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Toward lightweight biometric signal processing for wearable devices

Cited by 5 publications

References 16 publications

ECG Reduction for Wearable Sensor

ECG Reduction for Wearable Sensor

Priorities to Overcome Barriers Impacting Data Science Application in Emergency Care Research

Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

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