A runtime-adaptive cognitive IoT node for healthcare monitoring

Scrugli, Matteo Antonio; Loi, Daniela; Raffo, Luigi; Meloni, Paolo

doi:10.1145/3310273.3323160

Cited by 16 publications

(14 citation statements)

References 18 publications

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“…On one hand, the community has designed novel ultra-low power processing platforms, providing previously unmatched computation capabilities on typical AI and data analysis workloads. In this work, we extend [44] taking into account that in the current state-of-the-art landscape, network topologies, processing platforms and software tools can be much more complex. In summary, as a main novel contribution, we propose:…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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“…In [7], an ANN is used to determine the patient's emotional state (happiness or sadness). In [8], energy/power efficiency is improved, using near-sensor processing to save data transfers, and dynamically adapting application setup and system frequency to the OM requested by an external user and to data-dependent workload.…”

Section: Related Workmentioning

confidence: 99%

An adaptable cognitive microcontroller node for fitness activity recognition

Scrugli¹,

Blažica²,

Meloni³

2022

Preprint

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The new generation of wireless technologies, fitness trackers, and devices with embedded sensors can have a big impact on healthcare systems and quality of life. Among the most crucial aspects to consider in these devices are the accuracy of the data produced and power consumption. Many of the events that can be monitored, while apparently simple, may not be easily detectable and recognizable by devices equipped with embedded sensors, especially on devices with low computing capabilities. It is well known that deep learning reduces the study of features that contribute to the recognition of the different target classes. In this work, we present a portable and battery-powered microcontroller-based device applicable to a wobble board. Wobble boards are low-cost equipment that can be used for sensorimotor training to avoid ankle injuries or as part of the rehabilitation process after an injury. The exercise recognition process was implemented through the use of cognitive techniques based on deep learning. To reduce power consumption, we add an adaptivity layer that dynamically manages the device's hardware and software configuration to adapt it to the required operating mode at runtime. Our experimental results show that adjusting the node configuration to the workload at runtime can save up to 60% of the power consumed. On a custom dataset, our optimized and quantized neural network achieves an accuracy value greater than 97% for detecting some specific physical exercises on a wobble board.

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“…Continuous heart rate monitoring and immediate heartbeat detection are primary concerns in contemporary healthcare. Experimental evidence has shown that many of the CVDs could be better diagnosed, controlled, and prevented through continuous monitoring, as well as analysis of electrocardiogram (ECG) signals [4][5][6][7][8][9]. Hence, the monitoring of physiological signals, such as electrocardiogram (ECG) signals, offers a new holistic paradigm for the assessment of CVDs, supporting disease control and prevention.…”

Section: Introductionmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

231

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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A runtime-adaptive cognitive IoT node for healthcare monitoring

Cited by 16 publications

References 18 publications

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

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

An adaptable cognitive microcontroller node for fitness activity recognition

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

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