High-Performance and Resource-Efficient IoT-based Sleep Monitoring System

Surantha, Nico; Utomo, Oei Kurniawan; Isa, Sani Muhamad

doi:10.1109/vtc2020-spring48590.2020.9129521

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The 'strongest' features that affected the result were determined using the PSO algorithm, while testing accuracy for fitness values was using the WELM algorithm. More information about feature selection and assessment can be found in previous research published as conference proceedings [19].…”

Section: Performance Of Proposed Welm and Pso Algorithmmentioning

confidence: 99%

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Intelligent Sleep Monitoring System Based on Microservices and Event-Driven Architecture

et al. 2022

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Sleep monitoring using polysomnography (PSG) in hospitals can be considered expensive, so the preferable way is to use contactless and wearable sensors to monitor sleep daily by patients at home. In this work, the Internet of things (IoT) platform was utilized for sleep monitoring with contactless or wearable sensors as an integrated system developed based on an event-driven and microservice architecture. Multiple services that respond to events are provided within the system. Electrocardiogram (ECG) data were used as the input in the sleep monitoring system. The combination of the weighted extreme learning machine (WELM) algorithm with particle swarm optimization (PSO) was used to process the ECG data, followed by fuzzy logic to measure sleep quality, then display the data on the dashboard. Based on the experimental results, the proposed architecture increased throughput by 34.76%, decreased response time by 55.85%, and reduced memory consumption by 37.26% per instance replication compared to the non-event-driven architecture. The accuracies of the sleep stage classification were 78.78% and 73.09% for the three and four classes, respectively, and the area under the ROC curve (AUC) reached 0.89 for both the three and four class classification.

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Section: Performance Of Proposed Welm and Pso Algorithmmentioning

confidence: 99%

“…Good performance (as in better classification accuracy, area under ROC curve (AUC), throughput, memory allocation, and response time) is expected using the novel methods described above. This paper is an extended version of the conference proceedings published in [19].…”

Section: Introductionmentioning

confidence: 99%