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Computational Balancing between Wearable Sensor and Smartphone towards Energy-Efficient Remote Healthcare MonitoringSECERBEGOVIC, A. , GOGIC, A. , SULJANOVIC, N. , ZAJC, M. , MUJCIC, A.
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wearable sensors, mobile computing, body sensor networks, biomedical signal processing, performance evaluation
mobile(7), computing(6), sensor(5), wearable(4), time(4), systems(4), selection(4), recognition(4), real(4), feature(4)
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About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04001
Web of Science Accession Number: 000451843400001
SCOPUS ID: 85058816909
Recent advances in the development of wearable sensors and smartphones open up opportunities for executing computing operations on the devices instead of using them for streaming raw data. By minimizing power consumption due to the wireless transmission, limited energy resources of wearable devices can be utilized not only for sensing, but also for processing physiological signals. Computational tasks between a wearable sensor and a smartphone can be distributed efficiently in order to provide balance between power consumption of both processing and transmission of the data. In this paper, we have analyzed the computational balancing between a wearable sensor and a smartphone. Presented models show different trade-offs between classification accuracy, processing time and power consumption due to different number and types of extracted features and classification models. Our results are based on a physiological dataset, where electrocardiogram and electro dermal activity signals were collected from 24 individuals in short-term stress and mental workload detection scenario. Our findings show that placing a feature extraction on a wearable sensor is efficient when processing cost of the extracted features is small. On the other hand, moving classification task to the smartphone can improve accuracy of recognition without compromising the overall power consumption.
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 Harmonic Loss Function for Sensor-Based Human Activity Recognition Based on LSTM Recurrent Neural Networks, Hu, Yue, Zhang, Xiao-Qing, Xu, Li, Xian He, Feng, Tian, Zhao, She, Wei, Liu, Wei, IEEE Access, ISSN 2169-3536, Issue , 2020.
Digital Object Identifier: 10.1109/ACCESS.2020.3003162 [CrossRef]
 Effective Wavelet Algorithm for an Automatic Detection of Atrial Fibrillation, ARVINTI, B., ISAR, A., COSTACHE, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.03006 [CrossRef] [Full text]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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