Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents the state-of-the-art research relating to each area of the model, evaluating their strengths, weaknesses, and overall suitability for a wearable IoT healthcare system. Challenges that healthcare IoT faces including security, privacy, wearability, and low-power operation are presented, and recommendations are made for future research directions.

Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities

Recent advances in soft materials and system integration technologies have provided a unique opportunity to design various types of wearable flexible hybrid electronics (WFHE) for advanced human healthcare and human-machine interfaces. The hybrid integration of soft and biocompatible materials with miniaturized wireless wearable systems is undoubtedly an attractive prospect in the sense that the successful device performance requires high degrees of mechanical flexibility, sensing capability, and user-friendly simplicity. Here, the most up-to-date materials, sensors, and system-packaging technologies to develop advanced WFHE are provided. Details of mechanical, electrical, physicochemical, and biocompatible properties are discussed with integrated sensor applications in healthcare, energy, and environment. In addition, limitations of the current materials are discussed, as well as key challenges and the future direction of WFHE. Collectively, an all-inclusive review of the newly developed WFHE along with a summary of imperative requirements of material properties, sensor capabilities, electronics performance, and skin integrations is provided.

Advanced Soft Materials, Sensor Integrations, and Applications of Wearable Flexible Hybrid Electronics in Healthcare, Energy, and Environment.

This paper presents a machine-learning classifier where computations are performed in a standard 6T SRAM array, which stores the machine-learning model. Peripheral circuits implement mixed-signal weak classifiers via columns of the SRAM, and a training algorithm enables a strong classifier through boosting and also overcomes circuit nonidealities, by combining multiple columns. A prototype 128 $\times $ 128 SRAM array, implemented in a 130-nm CMOS process, demonstrates ten-way classification of MNIST images (using image-pixel features downsampled from 28 $\times $ 28 = 784 to 9 $\times $ 9 = 81, which yields a baseline accuracy of 90%). In SRAM mode (bit-cell read/write), the prototype operates up to 300 MHz, and in classify mode, it operates at 50 MHz, generating a classification every cycle. With accuracy equivalent to a discrete SRAM/digital-MAC system, the system achieves ten-way classification at an energy of 630 pJ per decision, 113 times lower than a discrete system with standard training algorithm and 13 times lower than a discrete system with the proposed training algorithm.

In-Memory Computation of a Machine-Learning Classifier in a Standard 6T SRAM Array

The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.

/pdf/the-use-of-photoplethysmography-for-assessing-hypertension-519udctcj9.pdf

The use of photoplethysmography for assessing hypertension

Numerous light-based diagnostic and therapeutic devices are routinely used in the clinic. These devices have a familiar look as items plugged in the wall or placed at patients' bedsides, but recently, many new ideas have been proposed for the realization of implantable or wearable functional devices. Many advances are being fuelled by the development of multifunctional materials for photonic healthcare devices. However, the finite depth of light penetration in the body is still a serious constraint for their clinical applications. In this Review, we discuss the basic concepts and some examples of state-of-the-art implantable and wearable photonic healthcare devices for diagnostic and therapeutic applications. First, we describe emerging multifunctional materials critical to the advent of next-generation implantable and wearable photonic healthcare devices and discuss the path for their clinical translation. Then, we examine implantable photonic healthcare devices in terms of their properties and diagnostic and therapeutic functions. We next describe exemplary cases of noninvasive, wearable photonic healthcare devices across different anatomical applications. Finally, we discuss the future research directions for the field, in particular regarding mobile healthcare and personalized medicine.

Multifunctional materials for implantable and wearable photonic healthcare devices.

Pulse oximetry sensors have been playing a key role as devices to monitor elemental yet critical human health states. Conventional pulse oximetry sensors, however, have relatively large power consumption, impeding their use as stand-alone, continuous monitoring systems that can easily be integrated with everyday life. Here, we exploit the design freedom offered by organic technologies to realize a reflective patch-type pulse oximetry sensor with ultralow power consumption. On the basis of flexible organic light-emitting diodes and organic photodiodes designed via an optical simulation of color-sensitive light propagation within human skin, the proposed monolithically integrated organic pulse oximetry sensor heads exhibit successful operation at electrical power as low as 24 μW on average. We thereby demonstrate that organic devices not only have form factor advantages for such applications but also hold great promise as enablers for all-day wearable health monitoring systems.

https://advances.sciencemag.org/content/advances/4/11/eaas9530.full.pdf

Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch

A mobile electrical impedance tomography (EIT) IC is proposed for early breast cancer detection personally at home. To assemble the entire system into a simple brassiere shape, EIT IC is integrated via a multi-layered fabric circuit board which includes 90 EIT electrodes and two reference electrodes for current stimulation and voltage sensing. The IC supports three operating modes; gain scanning, contact impedance monitoring, and EIT modes for the clear EIT image. A differential sinusoidal current stimulator (DSCS) is proposed for injection of low-distortion programmable current which has harmonics less than $-$ 59 dBc at a load impedance of 2 kΩ. To get high sensitivity, a 6-channel voltage sensing amplifier can adaptively control the gain up to a maximum of 60 dB, and has low input referred noise, 36 nV/ $\surd$ Hz. The 2.5 × 5 mm chip is fabricated in a 0.18 µm 1P6M CMOS process and consumes 53.4 mW on average. As a result, a sensitivity of 4.9 mΩ is achieved which enables the detection of a 5 mm cancer mass within an agar test phantom.

18.4 A 4.9mΩ-sensitivity mobile electrical impedance tomography IC for early breast-cancer detection system

An ultra-low power true random number generator (TRNG) based on a sub-ranging SAR analog-to-digital converter (ADC) is proposed. The proposed TRNG is composed of a coarse-SAR ADC with a low-power adaptive-reset comparator and a low-power dynamic amplifier. The coarse-ADC part is shared with a sub-ranging SAR ADC for area reduction. The shared coarse-ADC not only plays the role of discrete-time chaotic circuit but also reduces the overall SAR ADC energy consumption by selectively activating the fine-SAR ADC. Also, the proposed dynamic residue amplifier consumes only 48 nW and the adaptive-reset comparator generates a chaotic map with only 6-nW consumption. The proposed TRNG core occupies 0.0045 mm2 in 0.18- $\mu \text{m}$ CMOS technology and consumes 82 nW at 270-kbps throughput with 0.6-V supply. It successfully passes all of National Institute of Standards and Technology (NIST) tests, and it achieves the state-of-the-art figure-of-merit of 0.3 pJ/bit.

A 82-nW Chaotic Map True Random Number Generator Based on a Sub-Ranging SAR ADC

A low-energy 40/160 MHz dual-band full duplex body channel communication (BCC) transceiver and a 13.56 MHz R-C oscillator-based super-regenerative transceiver are integrated in 65 nm CMOS mixed mode process for both entertainment and healthcare applications. The on-chip R-C duplexer uses notch filters for full duplex communication with 40 Mb/s data rate and combined dual-band operation shows 80 Mb/s data rate with half duplex communication. 40 MHz sine wave and 160 MHz rectangular wave are adopted for modulation in the dual-band transmitter with 30 dB SNR improvement, and shared-loop BPSK receiver reduces the power consumption by 25%. The proposed super-regenerative transceiver including an OOK transmitter and an R-C oscillator-based receiver achieves ${>}60\,\text{dB}$ interference rejection with 100 kb/s data rate and $42.5\,\upmu\text{W}$ power consumption under the 0.8 V supply.

A 79 pJ/b 80 Mb/s Full-Duplex Transceiver and a $42.5\;\upmu\text{W}$ 100 kb/s Super-Regenerative Transceiver for Body Channel Communication

A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 $\,\times\,$ 2.25 mm $^{2}$ chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.

Yongsu Lee

Papers

Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch

18.4 A 4.9mΩ-sensitivity mobile electrical impedance tomography IC for early breast-cancer detection system

A 82-nW Chaotic Map True Random Number Generator Based on a Sub-Ranging SAR ADC

A 79 pJ/b 80 Mb/s Full-Duplex Transceiver and a $42.5\;\upmu\text{W}$ 100 kb/s Super-Regenerative Transceiver for Body Channel Communication

A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management