An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).

The performance of mHealth in cancer supportive care: a research agenda

Background

Since the advent of smartphones, mHealth has risen to the attention of the health care system as something that could radically change the way health care has been viewed, managed, and delivered to date. This is particularly relevant for cancer, as one of the leading causes of death worldwide, and for cancer supportive care, since patients and caregivers have key roles in managing side effects. Given adequate knowledge, they are able to expect appropriate assessments and interventions. In this scenario, mHealth has great potential for linking patients, caregivers, and health care professionals; for enabling early detection and intervention; for lowering costs; and achieving better quality of life. Given its great potential, it is important to evaluate the performance of mHealth. This can be considered from several perspectives, of which organizational performance is particularly relevant, since mHealth may increase the productivity of health care providers and as a result even the productivity of health care systems.

Objective

This paper aims to review studies on the evaluation of the performance of mHealth, with particular focus on cancer care and cancer supportive care processes, concentrating on its contribution to organizational performance, as well as identifying some indications for a further research agenda.

Methods

We carried out a review of literature, aimed at identifying studies related to the performance of mHealth in general or focusing on cancer care and cancer supportive care.

Results

Our analysis revealed that studies are almost always based on a single dimension of performance. Any evaluations of the performance of mHealth are based on very different methods and measures, with a prevailing focus on issues linked to efficiency. This fails to consider the real contribution that mHealth can offer for improving the performance of health care providers, health care systems, and the quality of life in general.

Conclusions

Further research should start by stating and explaining what is meant by the evaluation of mHealth’s performance and then conduct more in-depth analysis in order to create shared frameworks to specifically identify the different dimensions of mHealth’s performance.