Edge-Assisted Sensor Control in Healthcare IoT

Amiri, Delaram; Anzanpour, Arman; Azimi, Iman; Levorato, Marco; Rahmani, Amir M.; Liljeberg, Pasi; Dutt, Nikil

doi:10.1109/glocom.2018.8647457

Cited by 14 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Particularly, in long-term monitoring, the battery life of wearables (i.e., the time a device works before its battery requires to be recharged) could significantly impact the system’s feasibility and usability. There is a wide variety of energy efficiency methods in the literature for addressing energy consumption in IoT-based systems [ 79 , 80 , 81 , 82 ].…”

Section: Evaluation and Discussionmentioning

confidence: 99%

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Sarhaddi

Azimi

Labbaf

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.

show abstract

Section: Evaluation and Discussionmentioning

confidence: 99%

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Sarhaddi

Azimi

Labbaf

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, local decision-making is a solution at the edge by which the system's availability and reliability are increased particularly when the Internet connection is poor [6]. Adaptive sensing and actuation is another application that intelligently tunes the system's configuration at the edge according to the context information [4]. Such a dynamic reconfiguration can considerably improve the system-driven quality attributes such as energy efficiency.…”

Section: Fog Computing and Its Benefitsmentioning

confidence: 99%

“…In fact, probability of misdetection can be defined as a joint event of ubiquity of abnormal vital signs and sensor's error tolerance. We proved in [4] that upper bound for probability of misdetection in abnormality (e.g. P D ) can be written as, P D (X, U ) = P θ (X)P τ (X, U ).…”

Section: Equation 3 Along With the Marked Abnormal Vital Signs In Equmentioning

confidence: 99%

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

et al. 2020

Self Cite

View full text Add to dashboard Cite

Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient's contextual information -including health and activity status -can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient's contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a run-time distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of decision making process over a certain threshold. Our optimization algorithm resides in the Fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy-efficiency with a bounded loss in accuracy.

show abstract

“…First, a filter-based method is used to extract respiratory and heartbeat signals. In this method, the cut-off frequencies are selected based on Power Spectral Density (PSD) of the PPG signals [42][43][44]. Note that an acceptable SNR is needed in this method, as high noise level influences the PSD of the signal and subsequently interrupts cut-off frequency selection.…”

Section: Experimental Datamentioning

confidence: 99%

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

et al. 2019

Self Cite

View full text Add to dashboard Cite

Cardiovascular diseases are one of the world's major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability-even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that-under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions)-our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.

show abstract

Edge-Assisted Sensor Control in Healthcare IoT

Cited by 14 publications

References 17 publications

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

Contact Info

Product

Resources

About