Special Issue "Low-power Wearable Healthcare Sensors"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 31 October 2019.

Special Issue Editors

Guest Editor
Prof. Dr. R. Simon Sherratt

Department of Biomedical Engineering, University of Reading, RG6 6AY, UK
Website | E-Mail
Interests: wearable healthcare sensors; biosensors; fall detection; Parkinson’s disease
Guest Editor
Dr. Nilanjan Dey

Techno India College of Technology, Kolkata, West Bengal 700156, India
Website | E-Mail
Interests: image processing; AI; healthcare

Special Issue Information

Wearable devices have become a must-have consumer device. Such devices are commonly seen in healthcare, fitness, and location-tracking applications. The technology behind wearable devices has now matured to the point where consumer grade devices can be used to help people manage chronic conditions, including Parkinson’s disease, stroke, diabetes, and dementia. However, for reliable deployment at home, wearable devices must now operate for a long time between charges.

Research enabling devices to last longer per charge is extremely important because, as these devices are almost exclusively powered by batteries, their size is limited for user comfort and usability. While battery capacity can be improved, research also indicates that smarter and lower power CPU architectures, improved wireless connectivity, and task scheduling can also enable devices to last longer.

Aim: To bring together the latest research into low-power wearable healthcare devices.

Scope: low-power CPU architectures; wireless personal area networks; battery management; battery recovery; charge prediction; wireless charging; memory management; sleep mode optimization; software strategies for low-power management

Prof. Dr. R Simon Sherratt
Dr. Nilanjan Dey
Guest Editors

Manuscript Submission Information

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Keywords

  • low-power
  • healthcare
  • wearable
  • architecture
  • wireless personal area network

Published Papers (6 papers)

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Research

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Open AccessFeature PaperArticle
Task Scheduling to Constrain Peak Current Consumption in Wearable Healthcare Sensors
Electronics 2019, 8(7), 789; https://doi.org/10.3390/electronics8070789
Received: 30 May 2019 / Revised: 10 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
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Abstract
Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life, while at the same time supporting ever-increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power [...] Read more.
Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life, while at the same time supporting ever-increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power operation through hardware designs and intelligent methods of scheduling software tasks, all with the objective of minimizing the overall consumed electrical power. However, such an approach inevitably creates points in time where software tasks and peripherals coincide to draw large peaks of electrical current, creating short-term electrical stress for the battery and power regulators, and adding to electromagnetic interference emissions. This position paper proposes that the power profile of an embedded device using a real-time operating system (RTOS) will significantly benefit if the task scheduler is modified to be informed of the electrical current profile required for each task. This enables the task scheduler to schedule tasks that require large amounts of current to be spread over time, thus constraining the peak current that the system will draw. We propose a solution to inform the task scheduler of a tasks’ power profile, and we discuss our application scenario, which clearly benefited from the proposal. Full article
(This article belongs to the Special Issue Low-power Wearable Healthcare Sensors)
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Open AccessFeature PaperArticle
A Wearable Closed-Loop Insulin Delivery System Based on Low-Power SoCs
Electronics 2019, 8(6), 612; https://doi.org/10.3390/electronics8060612
Received: 9 May 2019 / Revised: 25 May 2019 / Accepted: 29 May 2019 / Published: 31 May 2019
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Abstract
The number of patients living with diabetes has increased significantly in recent years due to several factors. Many of these patients are choosing to use insulin pumps for their treatment, artificial systems that administer their insulin and consist of a glucometer and an [...] Read more.
The number of patients living with diabetes has increased significantly in recent years due to several factors. Many of these patients are choosing to use insulin pumps for their treatment, artificial systems that administer their insulin and consist of a glucometer and an automatic insulin supply working in an open loop. Currently, only a few closed-loop insulin delivery devices are commercially available. The most widespread systems among patients are what have been called the “Do-It-Yourself Hybrid Closed-Loop systems.” These systems require the use of platforms with high computing power. In this paper, we will present a novel wearable system for insulin delivery that reduces the energy and computing consumption of the platform without affecting the computation requirements. Patients’ information is obtained from a commercial continuous glucose sensor and a commercial insulin pump operating in a conventional manner. An ad-hoc embedded system will connect with the pump and the sensor to collect the glucose data and process it. That connection is accomplished through a radiofrequency channel that provides a suitable system for the patient. Thus, this system does not require to be connected to any other processor, which increases the overall stability. Using parameters configured by the patient, the control system will make automatic adjustments in the basal insulin infusion thereby bringing the patient’s glycaemia to the target set by a doctor’s prescription. The results obtained will be satisfactory as long as the configured parameters faithfully match the specific characteristics of the patient. Results from the simulation of 30 virtual patients (10 adolescents, 10 adults, and 10 children), using a python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator and a python implementation of the proposed algorithm, are presented. Full article
(This article belongs to the Special Issue Low-power Wearable Healthcare Sensors)
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Open AccessFeature PaperArticle
On the Design of Low-Cost IoT Sensor Node for e-Health Environments
Electronics 2019, 8(2), 178; https://doi.org/10.3390/electronics8020178
Received: 31 December 2018 / Revised: 24 January 2019 / Accepted: 29 January 2019 / Published: 2 February 2019
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Abstract
The proliferation of Internet of Things (IoT) devices for patient monitoring has gained much attention in clinical care performance, proficient chronic disease management, and home caregiving. This work presents the design of efficient medical IoT sensor nodes (SNs) in terms of low-cost, low [...] Read more.
The proliferation of Internet of Things (IoT) devices for patient monitoring has gained much attention in clinical care performance, proficient chronic disease management, and home caregiving. This work presents the design of efficient medical IoT sensor nodes (SNs) in terms of low-cost, low power-consumption, and increased data accuracy based on open-source platforms. The method utilizes a Sensor Controller (SC) within the IoT SN, which is capable of performing medical checks supporting a broad coverage of medical uses. A communication protocol has been developed for data and command exchange among SC, local gateways, and physicians’ or patients’ mobile devices (tablets, smart phones). The SC supports moving average window (MAW) and principle component analysis (PCA) filtering algorithms to capture data from the attached low-cost body sensors of different sampling profiles. Significant extensions in SN’s portability is achieved through energy consumption minimization based on the idle time gaps between sensors’ activations. SN’s components are either deactivated or set to low activity operation during these idle intervals. A medical case study is presented and the evaluated results show that the proposed SN can be incorporated into e-health platforms since it achieves comparable accuracy to its certified and high-cost commercial counterparts. Full article
(This article belongs to the Special Issue Low-power Wearable Healthcare Sensors)
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Open AccessFeature PaperArticle
A Comparative Study of Markerless Systems Based on Color-Depth Cameras, Polymer Optical Fiber Curvature Sensors, and Inertial Measurement Units: Towards Increasing the Accuracy in Joint Angle Estimation
Electronics 2019, 8(2), 173; https://doi.org/10.3390/electronics8020173
Received: 15 December 2018 / Revised: 23 January 2019 / Accepted: 30 January 2019 / Published: 2 February 2019
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Abstract
This paper presents a comparison between a multiple red green blue-depth (RGB-D) vision system, an intensity variation-based polymer optical fiber (POF) sensor, and inertial measurement units (IMUs) for human joint angle estimation and movement analysis. This systematic comparison aims to study the trade-off [...] Read more.
This paper presents a comparison between a multiple red green blue-depth (RGB-D) vision system, an intensity variation-based polymer optical fiber (POF) sensor, and inertial measurement units (IMUs) for human joint angle estimation and movement analysis. This systematic comparison aims to study the trade-off between the non-invasive feature of a vision system and its accuracy with wearable technologies for joint angle measurements. The multiple RGB-D vision system is composed of two camera-based sensors, in which a sensor fusion algorithm is employed to mitigate occlusion and out-range issues commonly reported in such systems. Two wearable sensors were employed for the comparison of angle estimation: (i) a POF curvature sensor to measure 1-DOF angle; and (ii) a commercially available IMUs MTw Awinda from Xsens. A protocol to evaluate elbow joints of 11 healthy volunteers was implemented and the comparison of the three systems was presented using the correlation coefficient and the root mean squared error (RMSE). Moreover, a novel approach for angle correction of markerless camera-based systems is proposed here to minimize the errors on the sagittal plane. Results show a correlation coefficient up to 0.99 between the sensors with a RMSE of 4.90 , which represents a two-fold reduction when compared with the uncompensated results (10.42 ). Thus, the RGB-D system with the proposed technique is an attractive non-invasive and low-cost option for joint angle assessment. The authors envisage the proposed vision system as a valuable tool for the development of game-based interactive environments and for assistance of healthcare professionals on the generation of functional parameters during motion analysis in physical training and therapy. Full article
(This article belongs to the Special Issue Low-power Wearable Healthcare Sensors)
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Open AccessArticle
Neural Spike Digital Detector on FPGA
Electronics 2018, 7(12), 392; https://doi.org/10.3390/electronics7120392
Received: 31 October 2018 / Revised: 28 November 2018 / Accepted: 30 November 2018 / Published: 5 December 2018
Cited by 1 | PDF Full-text (6809 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a multidisciplinary experiment where a population of neurons, dissociated from rat hippocampi, has been cultivated over a CMOS-based micro-electrode array (MEA) and its electrical activity has been detected and mapped by an advanced spike-sorting algorithm implemented on FPGA. MEAs are [...] Read more.
This paper presents a multidisciplinary experiment where a population of neurons, dissociated from rat hippocampi, has been cultivated over a CMOS-based micro-electrode array (MEA) and its electrical activity has been detected and mapped by an advanced spike-sorting algorithm implemented on FPGA. MEAs are characterized by low signal-to-noise ratios caused by both the contactless sensing of weak extracellular voltages and the high noise power coming from cells and analog electronics signal processing. This low SNR forces to utilize advanced noise rejection algorithms to separate relevant neural activity from noise, which are usually implemented via software/off-line. However, off-line detection of neural spikes cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA-based system capable to detect in real-time neural spikes from background noise. The output signals of the proposed system provide real-time spatial and temporal information about the culture electrical activity and the noise power distribution with a minimum latency of 165 ns. The output bit-stream can be further utilized to detect synchronous activity within the neural network. Full article
(This article belongs to the Special Issue Low-power Wearable Healthcare Sensors)
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Review

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Open AccessFeature PaperReview
CMOS Interfaces for Internet-of-Wearables Electrochemical Sensors: Trends and Challenges
Electronics 2019, 8(2), 150; https://doi.org/10.3390/electronics8020150
Received: 31 December 2018 / Revised: 24 January 2019 / Accepted: 28 January 2019 / Published: 31 January 2019
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Abstract
Smart wearables, among immediate future IoT devices, are creating a huge and fast growing market that will encompass all of the next decade by merging the user with the Cloud in a easy and natural way. Biological fluids, such as sweat, tears, saliva [...] Read more.
Smart wearables, among immediate future IoT devices, are creating a huge and fast growing market that will encompass all of the next decade by merging the user with the Cloud in a easy and natural way. Biological fluids, such as sweat, tears, saliva and urine offer the possibility to access molecular-level dynamics of the body in a non-invasive way and in real time, disclosing a wide range of applications: from sports tracking to military enhancement, from healthcare to safety at work, from body hacking to augmented social interactions. The term Internet of Wearables (IoW) is coined here to describe IoT devices composed by flexible smart transducers conformed around the human body and able to communicate wirelessly. In addition the biochemical transducer, an IoW-ready sensor must include a paired electronic interface, which should implement specific stimulation/acquisition cycles while being extremely compact and drain power in the microwatts range. Development of an effective readout interface is a key element for the success of an IoW device and application. This review focuses on the latest efforts in the field of Complementary Metal–Oxide–Semiconductor (CMOS) interfaces for electrochemical sensors, and analyses them under the light of the challenges of the IoW: cost, portability, integrability and connectivity. Full article
(This article belongs to the Special Issue Low-power Wearable Healthcare Sensors)
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