Power efficient compressive sensing for continuous monitoring of ECG and PPG in a wearable system

Natarajan, V.; Vyas, Apoorv

doi:10.1109/wf-iot.2016.7845493

Cited by 16 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in [25], the researchers propose an IoT system using CS theory applied to electrocardiographic (ECG) and photoplethysmography (PPG) signals acquired by an IoT node. In particular, the researchers propose an adaptive CS algorithm, whereby according to the morphology and the noise and signal quality, the compression ratio related to the non-uniform sampling operation is tuned.…”

Section: Energy-aware Sampling Methodsmentioning

confidence: 99%

Research challenges in Measurement for Internet of Things systems

et al. 2019

View full text Add to dashboard Cite

In this paper, an overview of the research challenges in measurements for the design of Internet of Things (IoT) systems is proposed. To this end, a general architecture of an IoT system is presented, which is specialized according to two key requirements: the power supply capabilities of the infrastructure and the time delay constraints of the application. Guidelines for the design of an IoT system are summarized, and the measurement needs are highlighted. A review of the research contributions is given concerning three main measurement topics: (i) energy-aware data acquisition systems, (ii) localization of mobile IoT nodes, and (iii) precise synchronization protocols.

show abstract

Section: Energy-aware Sampling Methodsmentioning

confidence: 99%

Research challenges in Measurement for Internet of Things systems

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In [22] and [23], authors treat with particular attention the aspect of Signal Quality Assessment (SQA), identifying the signal quality level is useful for knowing when to ignore the input data or even when to put the device into a sleep state. In [24] a system that uses Compressive Sensing (CS) to compress bio-signals in a power-efficient way is proposed. In [25], authors propose a monitoring device with a particular focus on low energy consumption.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things

et al. 2022

View full text Add to dashboard Cite

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Cardiovascular diseases monitoring, usually involving electrocardiogram (ECG) traces analysis, is one of the most promising and high-impact applications. Nevertheless, to fully exploit the potential of IoMT in this domain, some steps forward are needed. First, the edgecomputing paradigm must be added to the picture. A certain level of near-sensor processing has to be enabled, to improve the scalability, portability, reliability and responsiveness of the IoMT nodes. Second, novel, increasingly accurate data analysis algorithms, such as those based on artificial intelligence and Deep Learning, must be exploited. To reach these objectives, designers, and programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets. In this work, we explore the implementation of a cognitive data analysis algorithm, based on a convolutional neural network trained to classify ECG waveforms, on a resource-constrained microcontroller-based computing platform. To minimize power consumption, we add an adaptivity layer that dynamically manages the hardware and software configuration of the device to adapt it at runtime to the required operating mode. Our experimental results show that adapting the node setup to the workload at runtime can save up to 50% power consumption. Our optimized and quantized neural network reaches an accuracy value higher than 97% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset. INDEX TERMSAdaptive system, health information management, Internet of Things, low power electronics, neural network, remote sensing, runtime, wearable sensors.

show abstract

“…The possibility of compressing continuous bio-signals in a wearable sensor network was analysed in [ 18 ]. The authors have used a binary permuted block diagonal matrix encoder to compress electrocardiogram and photoplethysmogram data.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Lewandowski

Płaczek

Bernaś

2020

Sensors

View full text Add to dashboard Cite

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.

show abstract

Power efficient compressive sensing for continuous monitoring of ECG and PPG in a wearable system

Cited by 16 publications

References 5 publications

Research challenges in Measurement for Internet of Things systems

Research challenges in Measurement for Internet of Things systems

An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Contact Info

Product

Resources

About