Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms
This paper aims to reduce the power consumption of electrocardiography based wearable healthcare devices, by introducing power reduction approaches and considerations at system level design, where we have the highest potential to influence power. It focuses, in particular, on algorithm design and im...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Open Journal of Circuits and Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9178390/ |
| _version_ | 1818927583166726144 |
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| author | Jiamin Li Adnan Ashraf Barry Cardiff Rajesh C. Panicker Yong Lian Deepu John |
| author_facet | Jiamin Li Adnan Ashraf Barry Cardiff Rajesh C. Panicker Yong Lian Deepu John |
| author_sort | Jiamin Li |
| collection | DOAJ |
| description | This paper aims to reduce the power consumption of electrocardiography based wearable healthcare devices, by introducing power reduction approaches and considerations at system level design, where we have the highest potential to influence power. It focuses, in particular, on algorithm design and implementation, data acquisition, and transmission under constrained resources. A thorough investigation of the suitability of nine existing algorithms for on-sensor QRS feature detection is conducted, with respect to metrics such as sensitivity, positive predictivity, power consumption, parameter choice and time delay. Optimisation of data acquisition on CPU-based IoT systems is performed, and the current consumption is reduced by a factor of 3 using a combination of direct memory access (DMA) list approach and low-level register manipulations for task delegation. The acquisition data rate, sampling rate, buffer and batch size are also optimised. To reduce the power consumption by data transmission, the effect of on-sensor versus off-sensor processing is investigated. While focusing on CPU-based systems with experiments performed on a generic low-power wearable platform, the design optimisation and considerations proposed in this work could be extended to custom designs and allow further investigation into QRS detection algorithm optimisation for wearable devices. |
| first_indexed | 2024-12-20T03:15:19Z |
| format | Article |
| id | doaj.art-4edbf1cd0e8e4b6dae005bafb008e752 |
| institution | Directory Open Access Journal |
| issn | 2644-1225 |
| language | English |
| last_indexed | 2024-12-20T03:15:19Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Circuits and Systems |
| spelling | doaj.art-4edbf1cd0e8e4b6dae005bafb008e7522022-12-21T19:55:21ZengIEEEIEEE Open Journal of Circuits and Systems2644-12252020-01-01111512310.1109/OJCAS.2020.30098229178390Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection AlgorithmsJiamin Li0Adnan Ashraf1Barry Cardiff2https://orcid.org/0000-0003-1303-8115Rajesh C. Panicker3Yong Lian4Deepu John5https://orcid.org/0000-0002-6139-1100Department of Electrical and Computer Engineering, National University of Singapore, SingaporeSchool of Electrical and Electronic Engineering, University College Dublin, Dublin 4, IrelandSchool of Electrical and Electronic Engineering, University College Dublin, Dublin 4, IrelandDepartment of Electrical and Computer Engineering, National University of Singapore, SingaporeDepartment of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, CanadaSchool of Electrical and Electronic Engineering, University College Dublin, Dublin 4, IrelandThis paper aims to reduce the power consumption of electrocardiography based wearable healthcare devices, by introducing power reduction approaches and considerations at system level design, where we have the highest potential to influence power. It focuses, in particular, on algorithm design and implementation, data acquisition, and transmission under constrained resources. A thorough investigation of the suitability of nine existing algorithms for on-sensor QRS feature detection is conducted, with respect to metrics such as sensitivity, positive predictivity, power consumption, parameter choice and time delay. Optimisation of data acquisition on CPU-based IoT systems is performed, and the current consumption is reduced by a factor of 3 using a combination of direct memory access (DMA) list approach and low-level register manipulations for task delegation. The acquisition data rate, sampling rate, buffer and batch size are also optimised. To reduce the power consumption by data transmission, the effect of on-sensor versus off-sensor processing is investigated. While focusing on CPU-based systems with experiments performed on a generic low-power wearable platform, the design optimisation and considerations proposed in this work could be extended to custom designs and allow further investigation into QRS detection algorithm optimisation for wearable devices.https://ieeexplore.ieee.org/document/9178390/Bluetooth low energydirect memory accessInternet of Thingson-chip processingQRS detectionwearable sensors |
| spellingShingle | Jiamin Li Adnan Ashraf Barry Cardiff Rajesh C. Panicker Yong Lian Deepu John Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms IEEE Open Journal of Circuits and Systems Bluetooth low energy direct memory access Internet of Things on-chip processing QRS detection wearable sensors |
| title | Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms |
| title_full | Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms |
| title_fullStr | Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms |
| title_full_unstemmed | Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms |
| title_short | Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms |
| title_sort | low power optimisations for iot wearable sensors based on evaluation of nine qrs detection algorithms |
| topic | Bluetooth low energy direct memory access Internet of Things on-chip processing QRS detection wearable sensors |
| url | https://ieeexplore.ieee.org/document/9178390/ |
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