ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints
<p dir="ltr">Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has bee...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2018
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513561109200896 |
|---|---|
| author | Mohammed Al Disi (16855407) |
| author2 | Hamza Djelouat (16855410) Christos Kotroni (16855413) Elena Politis (16855416) Abbes Amira (6952001) Faycal Bensaali (12427401) George Dimitrakopoulos (16855419) Guillaume Alinier (6952004) |
| author2_role | author author author author author author author |
| author_facet | Mohammed Al Disi (16855407) Hamza Djelouat (16855410) Christos Kotroni (16855413) Elena Politis (16855416) Abbes Amira (6952001) Faycal Bensaali (12427401) George Dimitrakopoulos (16855419) Guillaume Alinier (6952004) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammed Al Disi (16855407) Hamza Djelouat (16855410) Christos Kotroni (16855413) Elena Politis (16855416) Abbes Amira (6952001) Faycal Bensaali (12427401) George Dimitrakopoulos (16855419) Guillaume Alinier (6952004) |
| dc.date.none.fl_str_mv | 2018-12-07T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2018.2877679 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/ECG_Signal_Reconstruction_on_the_IoT-Gateway_and_Efficacy_of_Compressive_Sensing_Under_Real-Time_Constraints/23994828 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Data management and data science Distributed computing and systems software Real-time systems Electrocardiography Logic gates Biomedical monitoring Sensors Monitoring Energy consumption Connected health Compressed sensing Energy efficiency Heterogeneous multicore platforms Internet of things Mobile real-time health monitoring Multicore processing Remote monitoring Wearable sensors |
| dc.title.none.fl_str_mv | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2018.2877679" target="_blank">https://dx.doi.org/10.1109/access.2018.2877679</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d0d6a776e46a9cd4323b531a806f880b |
| identifier_str_mv | 10.1109/access.2018.2877679 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23994828 |
| publishDate | 2018 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time ConstraintsMohammed Al Disi (16855407)Hamza Djelouat (16855410)Christos Kotroni (16855413)Elena Politis (16855416)Abbes Amira (6952001)Faycal Bensaali (12427401)George Dimitrakopoulos (16855419)Guillaume Alinier (6952004)EngineeringBiomedical engineeringInformation and computing sciencesData management and data scienceDistributed computing and systems softwareReal-time systemsElectrocardiographyLogic gatesBiomedical monitoringSensorsMonitoringEnergy consumptionConnected healthCompressed sensingEnergy efficiencyHeterogeneous multicore platformsInternet of thingsMobile real-time health monitoringMulticore processingRemote monitoringWearable sensors<p dir="ltr">Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2018.2877679" target="_blank">https://dx.doi.org/10.1109/access.2018.2877679</a></p>2018-12-07T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2018.2877679https://figshare.com/articles/journal_contribution/ECG_Signal_Reconstruction_on_the_IoT-Gateway_and_Efficacy_of_Compressive_Sensing_Under_Real-Time_Constraints/23994828CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/239948282018-12-07T00:00:00Z |
| spellingShingle | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints Mohammed Al Disi (16855407) Engineering Biomedical engineering Information and computing sciences Data management and data science Distributed computing and systems software Real-time systems Electrocardiography Logic gates Biomedical monitoring Sensors Monitoring Energy consumption Connected health Compressed sensing Energy efficiency Heterogeneous multicore platforms Internet of things Mobile real-time health monitoring Multicore processing Remote monitoring Wearable sensors |
| status_str | publishedVersion |
| title | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints |
| title_full | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints |
| title_fullStr | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints |
| title_full_unstemmed | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints |
| title_short | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints |
| title_sort | ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints |
| topic | Engineering Biomedical engineering Information and computing sciences Data management and data science Distributed computing and systems software Real-time systems Electrocardiography Logic gates Biomedical monitoring Sensors Monitoring Energy consumption Connected health Compressed sensing Energy efficiency Heterogeneous multicore platforms Internet of things Mobile real-time health monitoring Multicore processing Remote monitoring Wearable sensors |