Abstract
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.
Highlights
I N RECENT years, an increasing amount of effort has been devoted to developing real-time wearable cardiac monitoring devices, as they offer greater mobility and enable early detection of cardiovascular diseases as compared to traditional ambulatory Holter monitors
This paper investigates power consumption at data acquisition, processing, and transmission stages on the Internet of Things (IoT) wearable sensor, and proposes techniques and design recommendations to reduce the system power consumption
EVALUATION METRICS As this paper examines the power consumption of data acquisition, processing (i.e., QRS detection) on chip with respect to the performance of feature extraction and transmission, it is essential to define several common metrics and benchmarks so that a fair and reasonable comparison study/trade-off analysis could take place
Summary
I N RECENT years, an increasing amount of effort has been devoted to developing real-time wearable cardiac monitoring devices, as they offer greater mobility and enable early detection of cardiovascular diseases as compared to traditional ambulatory Holter monitors. Through constant monitoring, these wearable devices can detect cardiac rhythm disorders before the disease deteriorates, allowing treatment at the pre-clinical stage which increases the chance of complete recovery and curbs mounting healthcare expenditure. The detection of cardiac diseases relies on identifying, extracting and analysing the features of each heartbeat event from the person’s electrocardiogram (ECG) signal. Extensive algorithms on QRS detection have been developed and enhanced, with the focus being improving the detection
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
