Wearable medical systems for p-Health

A voltage mode grounded capacitance multiplier with widely tunable gain for ultra-low cutoff frequency filter

A voltage mode capacitance multiplier for ultra-low frequency physiological signal processing is designed with a circuit model. With the proposed multiplier, a filter can achieve a cutoff frequency of 12 mHz with a 1 pF basic capacitance and a 10 kΩ resistor. The corresponding multiplication factor will be 1.35 × 109. By changing the controlling terminal, the multiplication factor can be widely tuned from 1950 to 1.35 × 109 and the corresponding filter cutoff frequency will be from 12 mHz to 8.15 kHz. According to the circuit model, to further increase the multiplication factor to decrease the chip area, more multiplication stages can be added to the feedback loop.

Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting

BACKGROUND

Wearable devices represent a new approach for monitoring key clinical parameters, such as ECG signals, for research and health purposes. These devices could outcompete medical devices in terms of affordability and use in out-clinic settings, allowing remote monitoring. The major limitation, especially when compared to implantable devices, is the presence of artifacts. Several authors reported a relevant percentage of recording time with poor/unusable traces for ECG, potentially hampering the use of these devices for this purpose. For this reason, it is of the utmost importance to develop a simple and inexpensive system enabling the user of the wearable devices to have immediate feedback on the quality of the acquired signal, allowing for real-time correction.

METHODS

A simple algorithm that can work in real time to verify the quality of the ECG signal (acceptable and unacceptable) was validated. Based on simple statistical parameters, the algorithm was blindly tested by comparison with ECG tracings previously classified by two expert cardiologists.

RESULTS

The classifications of 7200 10s-signal samples acquired on 20 patients with a commercial wearable ECG monitor were compared. The algorithm has an overall efficiency of approximately 95%, with a sensitivity of 94.7% and a specificity of 95.3%.

CONCLUSIONS

The results demonstrate that even a simple algorithm can be used to classify signal coarseness, and this could allow real-time intervention by the subject or the technician.

Optical Microfiber Intelligent Sensor: Wearable Cardiorespiratory and Behavior Monitoring with a Flexible Wave-Shaped Polymer Optical Microfiber

With the advantages of high flexibility, strong real-time monitoring capabilities, and convenience, wearable devices have shown increasingly powerful application potential in medical rehabilitation, health monitoring, the Internet of Things, and human-computer interaction. In this paper, we propose a novel and wearable optical microfiber intelligent sensor based on a wavy-shaped polymer optical microfiber (WPOMF) for cardiorespiratory and behavioral monitoring of humans. The optical fibers based on polymer materials are prepared into optical microfibers, fully using the advantages of the polymer material and optical microfibers. The prepared polymer optical microfiber is designed into a flexible wave-shaped structure, which enables the WPOMF sensor to have higher tensile properties and detection sensitivity. Cardiorespiratory and behavioral detection experiments based on the WPOMF sensor are successfully performed, which demonstrates the high sensitivity and stability potential of the WPOMF sensor when performing wearable tasks. Further, the success of the AI-assisted medical keyword pronunciation recognition experiment fully demonstrates the feasibility of integrating AI technology with the WPOMF sensor, which can effectively improve the intelligence of the sensor as a wearable device. As an optical microfiber intelligent sensor, the WPOMF sensor offers broad application prospects in disease monitoring, rehabilitation medicine, the Internet of Things, and other fields.

Effects of heat and personal protective equipment on thermal strain in healthcare workers: part B-application of wearable sensors to observe heat strain among healthcare workers under controlled conditions

PURPOSE

As climate change accelerates, healthcare workers (HCW) are expected to be more frequently exposed to heat at work. Heat stress can be exacerbated by physical activity and unfavorable working requirements, such as wearing personal protective equipment (PPE). Thus, understanding its potential negative effects on HCW´s health and working performance is becoming crucial. Using wearable sensors, this study investigated the physiological effects of heat stress due to HCW-related activities.

METHODS

Eighteen participants performed four experimental sessions in a controlled climatic environment following a standardized protocol. The conditions were (a) 22 °C, (b) 22 °C and PPE, (c) 27 °C and (d) 27 °C and PPE. An ear sensor (body temperature, heart rate) and a skin sensor (skin temperature) were used to record the participants´ physiological parameters.

RESULTS

Heat and PPE had a significant effect on the measured physiological parameters. When wearing PPE, the median participants' body temperature was 0.1 °C higher compared to not wearing PPE. At 27 °C, the median body temperature was 0.5 °C higher than at 22 °C. For median skin temperature, wearing PPE resulted in a 0.4 °C increase and higher temperatures in a 1.0 °C increase. An increase in median heart rate was also observed for PPE (+ 2/min) and heat (+ 3/min).

CONCLUSION

Long-term health and productivity risks can be further aggravated by the predicted temperature rise due to climate change. Further physiological studies with a well-designed intervention are needed to strengthen the evidence for developing comprehensive policies to protect workers in the healthcare sector.

Electrically Tunable Defect-Mode Wavelengths in a Liquid-Crystal-in-Cavity Hybrid Structure in the Near-Infrared Range

This work proposes a novel approach to developing a core component for a near-infrared (NIR) spectrometer with wavelength tunability, which is based on a liquid crystal (LC)-in-cavity structure as a hybrid photonic crystal (PC). By electrically altering the tilt angle of the LC molecules under applied voltage, the proposed PC/LC photonic structure consisting of an LC layer sandwiched between two multilayer films generates transmitted photons at specific wavelengths as defect modes within the photonic bandgap (PBG). The relationship between the number of defect-mode peaks and the cell thickness is investigated using a simulated approach based on the 4 × 4 Berreman numerical method. Furthermore, the defect-mode wavelength shifts driven by various applied voltages are studied experimentally. To minimize the power consumption of the optical module for spectrometric application, cells of different thicknesses are explored for the wavelength-tunability performance of the defect modes scanning through the entire free spectral ranges to the wavelengths of their next higher orders at null voltage. A 7.9 μm thick PC/LC cell is verified to attain the low operating voltage of merely 2.5 V_rms required to successfully cover the entire NIR spectral range between 1250 and 1650 nm. The proposed PBG structure is thus an excellent candidate for application in monochromator or spectrometer development.

On the development of low power wearable devices for assessment of physiological vital parameters: a systematic review

Aim

Smart wearable devices for continuous monitoring of health conditions have bbecome very important in the healthcare sector to acquire and assess the different physiological parameters. This paper reviews the nature of physiological signals, desired vital parameters, role of smart wearable devices, choices of wearable devices and design considerations for wearable devices for early detection of health conditions.

Subject and methods

This article provides designers with information to identify and develop smart wearable devices based on the data extracted from a literature survey on previously published research articles in the field of wearable devices for monitoring vital parameters.

Results

The key information available in this article indicates that quality signal acquisition, processing and longtime monitoring of vital parameters requires smart wearable devices. The development of smart wearable devices with the listed design criteria supports the developer to design a low power wearable device for continuous monitoring of patient health conditions.

Conclusion

The wide range of information gathered from the review indicates that there is a huge demand for smart wearable devices for monitoring health conditions at home. It further supports tracking heath status in the long term via monitoring the vital parameters with the support of wireless communication principles.

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

Conductive Textiles for Signal Sensing and Technical Applications

Conductive textiles have found notable applications as electrodes and sensors capable of detecting biosignals like the electrocardiogram (ECG), electrogastrogram (EGG), electroencephalogram (EEG), and electromyogram (EMG), etc; other applications include electromagnetic shielding, supercapacitors, and soft robotics. There are several classes of materials that impart conductivity, including polymers, metals, and non-metals. The most significant materials are Polypyrrole (PPy), Polyaniline (PANI), Poly(3,4-ethylenedioxythiophene) (PEDOT), carbon, and metallic nanoparticles. The processes of making conductive textiles include various deposition methods, polymerization, coating, and printing. The parameters, such as conductivity and electromagnetic shielding, are prerequisites that set the benchmark for the performance of conductive textile materials. This review paper focuses on the raw materials that are used for conductive textiles, various approaches that impart conductivity, the fabrication of conductive materials, testing methods of electrical parameters, and key technical applications, challenges, and future potential.

Towards Self-Adaptability of Instrumented Electromagnetic Energy Harvesters

Motion-driven electromagnetic energy harvesting is a well-suited technological solution to autonomously power a broad range of autonomous devices. Although different harvester configurations and mechanisms have been already proposed to perform effective tuning and broadband harvesting, no methodology has proven to be effective to maximize the harvester performance for unknown and time-varying patterns of mechanical power sources externally exciting the harvesters. This paper provides, for the first time, a radically new concept of energy harvester to maximize the harvested energy for time-varying excitations: the self-adaptive electromagnetic energy harvester. This research work aims to analyze the electric energy harvesting gain when self-adaptive electromagnetic harvesters, using magnetic levitation architectures, are able to autonomously adapt their architecture as variations in the excitation patterns occur. This was accomplished by identifying the optimal harvester length for different excitation patterns and load resistances. Gains related to electric current and power exceeding 100 can be achieved for small-scale harvesters. The paper also describes comprehensive case studies to verify the feasibility of the self-adaptive harvester, considering the energy demand from the adaptive mechanism, namely the sensing, processing and actuation systems. These successful results highlight the potential of this innovative methodology to design highly sophisticated energy harvesters, both for a small- and large-scale power supply.

Ergonomic Assessment of Physical Load in Slovak Industry Using Wearable Technologies

The physical tasks of workers are demanding, particularly when performed long-term in unsuitable working position, with high frequency, heavy load, after injury, with developing damage of health or reduced performance due to advanced age. Work-related musculoskeletal disorders (WMSDs) result from overuse or develop over time. Work activities, which are frequent and repetitive, or activities with awkward postures, cause disorders that may be painful during work or at rest. There is a new technology in the market, occupational exoskeletons, which have the prerequisites for minimizing the negative consequences of workload on WMSDs. We provided pilot quantitative measurements of the ergonomic risk at one selected workplace in a Slovak automotive company with four different workers to prove our methodology using wearable wireless multi-sensor systems Captiv and Actigraph. At first, the test was performed in standard conditions without an exoskeleton. The unacceptable physical load was identified in considerable evaluated body areas—neck, hip, and shoulder. Next, the passive chair exoskeleton Chairless Chair 2.0 was used in trials as an ergonomic measure. Our intention was to determine whether an exoskeleton would be an effective tool for optimizing the workload in selected workplaces and whether the proposed unique quantitative measurement system would give reliable and quick results.

Non-contact electrocardiogram measuring method based on capacitance coupling electrodes with ultra-high input impedance

With the development of remote cardiac healthcare, wearable devices for electrocardiogram (ECG) monitoring are stringent requirements to cope with this rapid growth of demands. Due to the advantages of no-contact ECG measuring methods in safety, convenience, and comfortableness, it is more suitable for wearable long-term ECG monitoring than the conventional Ag/AgCl electrodes. The capacitance coupling printed circuit board (PCB) electrode with ultra-high input impedance proposed in this paper can realize non-contact ECG measurement through a multi-layer insulating medium. Then, an eight-channel ECG signal processing circuit is also designed and fabricated. In addition, the following important performance properties of the non-contact ECG measuring system, such as the input impedance, the phase-frequency characteristic, the amplitude-frequency characteristic, the coupling coefficient, and the input short-circuit input noise, were all experimentally measured. The synchronous comparison between the Ag/AgCl electrode and the PCB electrode was also conducted to verify the accuracy of the non-contact measuring method. Finally, the influence of the lead positions, coupling medium parameters, and the body motion states was also experimentally studied. The results demonstrate that the proposed non-contact ECG measuring method based on capacitance coupling PCB electrodes can effectively collect the main components of ECG signals and cardiac rhythm in various situations.

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates

The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.

Multi-Channel Biopotential Acquisition System Using Frequency-Division Multiplexing With Cable Motion Artifact Suppression

A multi-channel, CMOS-based biopotential acquisition system is presented that uses amplitude modulated, frequency division multiplexing (AM-FDM) to decrease wire count and provide resilience against motion artifacts and cable noise. Differential active electrode (AE) pairs capture surface biopotential signals, each modulated by a different carrier frequency and combined via current-domain summing. The presented approach requires only a single wire for signal transmission between AEs and back-end readout, along with clock and ground wires, to support multiple active electrodes using a 3-wire cable. Frequency modulation prior to transmission mitigates the effect of low-frequency cable motion artifacts and 50/60 Hz mains interference in the cable. A prototype FDM-based biopotential acquisition system was implemented in a 180 nm CMOS process, including a four-channel front-end active electrode IC for signal conditioning and modulation, and a back-end IC for demodulation and digitization. Each channel occupies 0.75 mm [Formula: see text] and consumes 43.8 μ W, inclusive of ADC power. Using both AE and BE ICs, a four-channel biopotential recording system is demonstrated using a 3-wire interface, where the system achieves attenuation of low-frequency cable motion artifacts by 15X and 60 Hz mains noise coupled into the cable by 62X.

Smart Textiles for Improved Quality of Life and Cognitive Assessment

Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs.

Scientific Developments and New Technological Trajectories in Sensor Research

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

Design and Implementation of a Wearable Accelerometer-Based Motion/Tilt Sensing Internet of Things Module and Its Application to Bed Fall Prevention

Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.

Wholesome Coin: A pHealth Solution to Reduce High Obesity Rates in Gulf Cooperation Council Countries Using Cryptocurrency

Obesity is considered one of the leading causes of chronic and noncommunicable diseases; these include diabetes, cardiovascular disease, and cancer. The obesity prevalence is threefold higher in the Arab Gulf Cooperation Council (GCC) population than the rest of the world and leaves healthcare providers within the region with no alternative than to offer continuous and sustainable healthcare services. Obesity prevention would be more economical for governments than providing treatment. Preventing obesity is challenging because it requires motivating individuals to live a healthy lifestyle. Personal health (pHealth) has recently been actively involved in finding solutions to encourage healthy living. However, pHealth does not address the high percentage of people lacking the desire to maintain healthy living plans, which could have a negative effect on attempts aimed at reducing obesity prevalence. This study sheds light on the challenges faced by the GCC governments in reducing high obesity rates using pHealth; we propose a solution, Wholesome Coin, which incorporates advanced technologies to help governments reduce high obesity rates. Wholesome Coin has two components: one uses wearable IoT (WIoT) to help patients manage their behavior by tracking their physical activities and diet, and the other utilizes blockchain technology to help healthcare payers to incentify patients to maintain a healthy living plan by awarding digital coins that can be redeemed for real goods and services. GCC governments’ adoption of Wholesome Coin could improve the quality of life of obese patients in a seamless, secure, and self-motivated manner, resulting in a healthier tomorrow, especially amid challenging times featuring global social distance campaigns.

eHealthChain-a blockchain-based personal health information management system

Medical IoT devices that use miniature sensors to collect patient's bio-signals and connected medical applications are playing a crucial role in providing pervasive and personalized healthcare. This technological improvement has also created opportunities for the better management of personal health information. The Personal Health Information Management System (PHIMS) supports activities such as acquisition, storage, organization, integration, and privacy-sensitive retrieval of consumer's health information. For usability and wide acceptance, the PHIMS should follow the design principles that guarantee privacy-aware health information sharing, individual information control, integration of information obtained from multiple medical IoT devices, health information security, and flexibility. Recently, blockchain technology has emerged as a lucrative option for the management of personal health information. In this paper, we propose eHealthChain-a blockchain-based PHIMS for managing health data originating from medical IoT devices and connected applications. The eHealthChain architecture consists of four layers, which are a blockchain layer for hosting a blockchain database, an IoT device layer for obtaining personal health data, an application layer for facilitating health data sharing, and an adapter layer, which interfaces the blockchain layer with an application layer. Compared to existing systems, eHealthChain provides complete control to the user in terms of personal health data acquisition, sharing, and self-management. We also present a detailed implementation of a Proof of Concept (PoC) prototype of eHealthChain system built using Hyperledger Fabric platform.

Digital Health and the Care of the Patient With Arrhythmia

The field of cardiac electrophysiology has been on the cutting edge of advanced digital technologies for many years. More recently, medical device development through traditional clinical trials has been supplemented by direct to consumer products with advancement of wearables and health care apps. The rapid growth of innovation along with the mega-data generated has created challenges and opportunities. This review summarizes the regulatory landscape, applications to clinical practice, opportunities for virtual clinical trials, the use of artificial intelligence to streamline and interpret data, and integration into the electronic medical records and medical practice. Preparation of the new generation of physicians, guidance and promotion by professional societies, and advancement of research in the interpretation and application of big data and the impact of digital technologies on health outcomes will help to advance the adoption and the future of digital health care.

Adapting the stage-based model of personal informatics for low-resource communities in the context of type 2 diabetes

Growing availability of self-monitoring technologies creates new opportunities for collection of personal health data and their use in personalized health informatics interventions. However, much of the previous empirical research and existing theories of individuals' engagement with personal data focused on early adopters and data enthusiasts. Less is understood regarding ways individuals from medically underserved low-income communities who live with chronic diseases engage with self-monitoring in health. In this research, we adapted a widely used theoretical framework, the stage-based model of personal informatics, to the unique attitudes, needs, and constraints of low-income communities. We conducted a qualitative study of attitudes and perceptions regarding tracking and planning in health and other contexts (e.g., finances) among low-income adults living with type 2 diabetes. This study showed distinct differences in participants' attitudes and behaviors around tracking and planning, as well as wide variability in their sense of being in charge of different areas of one's life. Ultimately, we found a strong connection between these two: perceptions of being in charge seems to be strongly connected to an individual's proactive or reactive tracking and planning in that area. Whereas individuals with a greater sense of being in charge of their health were more proactive, meaning they were likely to engage with all the stages of personal informatics model on their own, those with less of a sense of being in charge were more likely to be reactive-relying on their healthcare providers for several critical stages of self-monitoring (deciding what data to collect, integrating data from multiple sources, reflecting over patterns in collected data, and arriving at conclusions and implications for action). Perhaps as a result, these individuals were less likely to experience increases in self-awareness and self-knowledge, common motivating factors to engaging in self-monitoring in the future. We argue that adapting this framework in a way that highlights gaps in individuals' engagement has a number of important implications for future research in biomedical informatics and for the design of new interventions that promote engagement with self-monitoring, and that are robust in light of fragmented engagement.

Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study

Background

New wearable devices (for example, AliveCor or Zio patch) offer promise in detecting arrhythmia and monitoring cardiac health status, among other clinically useful parameters in older adults. However, the clinical utility and usability from the perspectives of clinicians is largely unexplored.

Objective

This study aimed to explore clinician perspectives on the use of wearable cardiac monitoring technology for older adults.

Methods

A descriptive qualitative study was conducted using semistructured focus group interviews. Clinicians were recruited through purposive sampling of physicians, nurses, and allied health staff working in 3 tertiary-level hospitals. Verbatim transcripts were analyzed using thematic content analysis to identify themes.

Results

Clinicians representing physicians, nurses, and allied health staff working in 3 tertiary-level hospitals completed 4 focus group interviews between May 2019 and July 2019. There were 50 participants (28 men and 22 women), including cardiologists, geriatricians, nurses, and allied health staff. The focus groups generated the following 3 overarching, interrelated themes: (1) the current state of play, understanding the perceived challenges of patient cardiac monitoring in hospitals, (2) priorities in cardiac monitoring, what parameters new technologies should measure, and (3) cardiac monitoring of the future, “the ideal device.”

Conclusions

There remain pitfalls related to the design of wearable cardiac technology for older adults that present clinical challenges. These pitfalls and challenges likely negatively impact the uptake of wearable cardiac monitoring in routine clinical care. Partnering with clinicians and patients in the co-design of new wearable cardiac monitoring technologies is critical to optimize the use of these devices and their uptake in clinical care.

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.

Wave of Wearables: Clinical Management of Patients and the Future of Connected Medicine

The future of connected health care will involve the collection of patient data or enhancement of clinician workflows through various biosensors and displays found on wearable electronic devices, many of which are marketed directly to consumers. The adoption of wearables in health care is being driven by efforts to reduce health care costs, improve care quality, and increase clinician efficiency. Wearables have significant potential to achieve these goals but are currently limited by lack of widespread integrations into electronic health records, biosensor data collection types, and a lack of scientifically rigorous literature showing benefit.

Home health monitoring around the time of surgery: qualitative study of patients' experiences before and after joint replacement

OBJECTIVES

Hip and knee replacements are common major elective surgical interventions with over 200 000 performed annually in the UK. Not all patients achieve optimal outcomes or experience problems or delays in recovery. The number of patients needing these operations is set to increase, and routine clinical monitoring is time-consuming and resource-consuming for patients and healthcare providers; therefore, innovative evaluation of surgical outcomes is needed. The aim of this qualitative study was to capture the patient experience of living with a novel home monitoring sensing system during the period around joint replacement.

SETTING

One secondary care hospital in the South West, UK.

PARTICIPANTS

13 patients (8 female, 63-89 years) undergoing total hip or knee replacement enrolled into the study.

DESIGN

Qualitative study with thematic analysis. The system remained in situ for up to 12 weeks after their surgery and comprised a group of low-powered sensors monitoring the environment (temperature, light and humidity) and activity of people within the home. Patients were interviewed at two timepoints: before and after surgery. Interviews explored views about living with the technology, its acceptability, as well as attitudes towards health technology.

RESULTS

Three main themes emerged: installation of home-sensing technology on the journey to surgery, the home space and defining unobtrusiveness and pivotal role of social support networks.

CONCLUSIONS

Patients who agreed to the technology found living with it acceptable. A home-sensing system that monitors the environment and activity of the people in the home could provide an innovative way of assessing patients' surgical outcomes. At a time characterised by reduced mobility, functional limitations and increased pain, patients in this study relied on informal and formal supportive networks to help maintain the system through the busy trajectory of the perioperative period.

Toward Wearable Healthcare: A Miniaturized 3D Imager With Coherent Frequency-Domain Photoacoustics

Medical monitoring is undergoing a translation from the hospital-based system to the personalized home-based system. With the aim of wearable application of photoacoustic technique, we propose a miniaturized photoacoustic 3D imager for superficial medical imaging. By employing the compact continuous-wave laser diode based optical irradiation and an ultrathin 2D matrix array based photoacoustic detection in the coherent frequency domain, a wearable imaging probe with a size of about 80 × 25 × 24 mm³ and a weight of 21 g is developed. At the backend, an FPGA controlled Howland current source drives the laser diodes to excite linear frequency modulated optical irradiation. Recorded by a portable multichannel data acquisition system, the generated photoacoustic responses are firstly compressed with the coherent frequency domain photoacoustic method and then extrapolated in the wavenumber-frequency domain for fast image reconstruction. With three-wavelength (450 nm, 638 nm, and 808 nm) laser irradiation, photoacoustic imaging can be operated multispectrally, endowing the developed imager with functional imaging capability in 3D space. With the imager worn on the human forearm, hemoglobin oxygen saturation level in superficial arm vasculature can be long-term monitored with high stability. When the imager is applied for imaging in a relatively large area (e.g., early melanoma detection in the human breast), flexible scanning in a handheld manner can be performed. This work opens the application potential of photoacoustic technique in a broad range of areas, including personalized healthcare, home health monitoring, and long-term physiologic monitoring.

Non-Invasive Flexible and Stretchable Wearable Sensors With Nano-Based Enhancement for Chronic Disease Care

Advances in flexible and stretchable electronics, functional nanomaterials, and micro/nano manufacturing have been made in recent years. These advances have accelerated the development of wearable sensors. Wearable sensors, with excellent flexibility, stretchability, durability, and sensitivity, have attractive application prospects in the next generation of personal devices for chronic disease care. Flexible and stretchable wearable sensors play an important role in endowing chronic disease care systems with the capability of long-term and real-time tracking of biomedical signals. These signals are closely associated with human body chronic conditions, such as heart rate, wrist/neck pulse, blood pressure, body temperature, and biofluids information. Monitoring these signals with wearable sensors provides a convenient and non-invasive way for chronic disease diagnoses and health monitoring. In this review, the applications of wearable sensors in chronic disease care are introduced. In addition, this review exploits a comprehensive investigation of requirements for flexibility and stretchability, and methods of nano-based enhancement. Furthermore, recent progress in wearable sensors-including pressure, strain, electrophysiological, electrochemical, temperature, and multifunctional sensors-is presented. Finally, opening research challenges and future directions of flexible and stretchable sensors are discussed.

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients' physical conditions based on few but long-term measurements.

Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers

Wearable devices are gaining considerable attention owing to the ease with which they can collect crucial information in real-time, both continuously and noninvasively, regarding a wearer's health. A concise summary is given of the three main elements that enable autonomous detection and monitoring of the likelihood or the existence of a health-risk state in continuous and real-time modes, with an emphasis on emerging materials and fabrication techniques in the relevant fields. The first element is the sensing technology used in the noninvasive detection of physiological markers relevant to the state of health. The second element is self-powered devices for longer periods of use by drawing energy from bodily movement and temperature. The third element is the self-healing properties of the materials used in the wearable devices to extended usage if they become scratched or cut. Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further improvement of skin-based wearable devices are also presented and discussed.

Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies

Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

Technology-Enabled Assessment of Functional Health

The maturation of pervasive computing technologies has dramatically altered the face of healthcare. With the introduction of mobile devices, body area networks, and embedded computing systems, care providers can use continuous, ecologically valid information to overcome geographic and temporal barriers and thus provide more effective and timely health assessments. In this paper, we review recent technological developments that can be harnessed to replicate, enhance, or create methods for assessment of functional performance. Enabling technologies in wearable sensors, ambient sensors, mobile technologies, and virtual reality make it possible to quantify real-time functional performance and changes in cognitive health. These technologies, their uses for functional health assessment, and their challenges for adoption are presented in this paper.

Electronics and orthopaedic surgery

Over the past few decades, sensors have been gaining a lot of popularity in the medical field. These sensors have helped shift the paradigm in medicine from having things done manually to digitalizing them. In the medical field, sensors have been manufactured in different forms and shapes including wearable and implantable wireless devices. With the aid of these sensors, healthcare professionals hope to revolutionize the system in a cost-effective way. In fact, this is already evident in most healthcare systems with the use of sensors for blood pressure, oxygen saturation, and arrhythmias on a daily basis. Also, more sophisticated sensors have made way into the medical field with a feedback loop, such as insulin pumps. On the other hand, similar technologies have been introduced in the orthopaedics world in the past decade. In this paper we summarize some of the sensors used in the medical field in general, and in orthopaedics in particular.

Wearable Chemosensors: A Review of Recent Progress

In recent years, there has been growing demand for wearable chemosensors for their important potential applications in mobile and electronic healthcare, patient self-assessment, human motion monitoring, and so on. Innovations in wearable chemosensors are revolutionizing the modern lifestyle, especially the involvement of both doctors and patients in the modern healthcare system. The facile interaction of wearable chemosensors with the human body makes them favorable and convenient tools for the detection and long-term monitoring of the chemical, biological, and physical status of the human body at a low cost with high performance. In this Minireview, we give a brief overview of the recent advances and developments in the field of wearable chemosensors, summarize the basic types of wearable chemosensors, and discuss their main functions and fabrication methods. At the end of this paper, the future development direction of wearable chemosensors is prospected. With continued interest and attention to this field, new exciting progress is expected in the development of innovative wearable chemosensors.

Survey on Monitoring and Quality Controlling of the Mobile Biosignal Delivery

A Mobile Patient Monitoring System (MPMS) acquires patient's biosignals and transmits them using wireless network connection to the decision-making module or healthcare professional for the assessment of patient's condition. A variety of wireless network technologies such as wireless personal area networks (e.g., Bluetooth), mobile ad-hoc networks (MANET), and infrastructure-based networks (e.g., WLAN and cellular networks) are in practice for biosignals delivery. The wireless network quality-of-service (QoS) requirements of biosignals delivery are mainly specified in terms of required bandwidth, acceptable delay, and tolerable error rate. An important research challenge in the MPMS is how to satisfy QoS requirements of biosignals delivery in the environment characterized by patient mobility, deployment of multiple wireless network technologies, and variable QoS characteristics of the wireless networks. QoS requirements are mainly application specific, while available QoS is largely dependent on QoS provided by wireless network in use. QoS provisioning refers to providing support for improving QoS experience of networked applications. In resource poor conditions, application adaptation may also be required to make maximum use of available wireless network QoS. This survey paper presents a survey of recent developments in the area of QoS provisioning for MPMS. In particular, our contributions are as follows: (1) overview of wireless networks and network QoS requirements of biosignals delivery; (2) survey of wireless networks' QoS performance evaluation for the transmission of biosignals; and (3) survey of QoS provisioning mechanisms for biosignals delivery in MPMS. We also propose integrating end-to-end QoS monitoring and QoS provisioning strategies in a mobile patient monitoring system infrastructure to support optimal delivery of biosignals to the healthcare professionals.

Multi-Sense CardioPatch: A Wearable Patch for Remote Monitoring of Electro-Mechanical Cardiac Activity

This study describes the conceptual design and the first prototype implementation of the Multi-Sense CardioPatch, a wearable multi-sensor patch for remote heart monitoring aimed at providing a more detailed and comprehensive heart status diagnostics. The system integrates multiple sensors in a single patch for detection of both electrical (electrocardiogram, ECG) and mechanical (heart sounds, HS) cardiac activity, in addition to physical activity (PA). The prototypal system also comprises a microcontroller board with a radio communication unit and it is powered by a Li-Ion rechargeable battery. Results from preliminary evaluations on healthy subjects have shown that the prototype can successfully measure electro-mechanical cardiac activity, providing useful cardiac indexes. The system has potential to improve remote monitoring of cardiac function in chronically diseased patients undergoing home-based cardiac rehabilitation programs.

Stretchable Dual-Capacitor Multi-Sensor for Touch-Curvature-Pressure-Strain Sensing

We introduce a new type of multi-functional capacitive sensor that can sense several different external stimuli. It is fabricated only with polydimethylsiloxane (PDMS) films and silver nanowire electrodes by using selective oxygen plasma treatment method without photolithography and etching processes. Differently from the conventional single-capacitor multi-functional sensors, our new multi-functional sensor is composed of two vertically-stacked capacitors (dual-capacitor). The unique dual-capacitor structure can detect the type and strength of external stimuli including curvature, pressure, strain, and touch with clear distinction, and it can also detect the surface-normal directionality of curvature, pressure, and touch. Meanwhile, the conventional single-capacitor sensor has ambiguity in distinguishing curvature and pressure and it can detect only the strength of external stimulus. The type, directionality, and strength of external stimulus can be determined based on the relative capacitance changes of the two stacked capacitors. Additionally, the logical flow reflected on a tree structure with its branches reaching the direction and strength of the corresponding external stimulus unambiguously is devised. This logical flow can be readily implemented in the sensor driving circuit if the dual-capacitor sensor is commercialized actually in the future.

A wearable textile for respiratory monitoring: Feasibility assessment and analysis of sensors position on system response

The interest on wearable textiles to monitor vital signs is growing in the research field and clinical scenario related to the increasing demands of long-term monitoring. Despite several smart textile-based solutions have been proposed for assessing the respiratory status, only a limited number of devices allow the respiratory monitoring in a harsh environment or in different positions of the human body. In this paper, we investigated the performances of a smart textile for respiratory rate monitoring characterized by 12 fiber optic sensors (i.e., fiber Bragg grating) placed on specific landmarks for compartmental analysis of the chest wall movements during quiet breathing. We focused on the analysis of the influence of sensor position on both peak-to-peak amplitude of sensors output and accuracy of respiratory rate measurements. This analysis was performed on two participants, who wore the textile in two positions (i.e., standing and supine). Bland-Altman analysis on respiratory rate showed promising results (better than 0.3 breaths per minute). Referring to the peak-to-peak output amplitude, the abdomen compartment showed the highest excursions in both the enrolled participants and positions. Our findings open up new approaches to design and develop smart textile for respiratory rate monitoring.

Stability of Enzymatic Biosensors for Wearable Applications

Technological evolution in wearable sensors accounts for major growth and transformation in a multitude of industries, ranging from healthcare to computing and informatics to communication and biomedical sciences. The major driver for this transformation is the new-found ability to continuously monitor and analyze the patients' physiology in patients' natural setting. Numerous wearable sensors are already on the market and are summarized. Most of the current technologies have focused on electrophysiological, electromechanical, or acoustic measurements. Wearable biochemical sensing devices are in their infancy. Traditional challenges in biochemical sensing such as reliability, repeatability, stability, and drift are amplified in wearable sensing systems due to variabilities in operating environment, sample/sensor handling, and motion artifacts. Enzymatic sensing technologies, due to reduced fluidic challenges, continue to be forerunners for converting into wearable sensors. This paper reviews the recent developments in wearable enzymatic sensors. The wearable sensors have been classified in three major groups based on sensor embodiment and placement relative to the human body: 1) on-body, 2) clothing/textile-based biosensors, and 3) biosensor accessories. The sensors, which come in the forms of stickers and tattoos, are categorized as on-body biosensors. The fabric-based biosensor comes in different models such as smart-shirts, socks, gloves, and smart undergarments with printed sensors for continuous monitoring.

Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology

Active electrodes (AEs), i.e., electrodes with built-in readout circuitry, are increasingly being implemented in wearable healthcare and lifestyle applications due to AEs' robustness to environmental interference. An AE locally amplifies and buffers μV-level EEG signals before driving any cabling. The low output impedance of an AE mitigates cable motion artifacts, thus enabling the use of high-impedance dry electrodes for greater user comfort. However, developing a wearable EEG system, with medical grade signal quality on noise, electrode offset tolerance, common-mode rejection ratio, input impedance, and power dissipation, remains a challenging task. This paper reviews state-of-the-art bio-amplifier architectures and low-power analog circuits design techniques intended for wearable EEG acquisition, with a special focus on an AE system interfaced with dry electrodes.

Detection of different types of noise in lung sounds

Lung sound signal processing has proven to be a great improvement to the traditional acoustic interpretation of lung sounds. However, that analysis can be seriously hindered by the presence of different types of noise originated in the acquisition environment or caused by physiological processes. Consequently, the diagnostic accuracy of pulmonary diseases can be severely affected, especially if the implementation of telemonitoring systems is considered. The present study is focused on the implementation of an algorithm able to identify noisy periods, either voluntarily (vocalizations, chest movement and background voices) or involuntarily produced during acquisitions of lung sounds. The developed approach also had to deal with the presence of simulated cough events, that carry important diagnostic information regarding several pulmonary diseases. Features such as Katz fractal dimension, Teager-Kaiser energy operator and normalized mutual information, were extracted from the time domain of healthy and a pathological lung signals. Noise detection was the result of a good discrimination between uncontaminated lung sounds and both cough and noise episodes and a slightly worse classification of cough events. In fact, detection of cough periods carrying diagnostic information was influenced by the presence of two other types of noise having similar signal characteristics.

Pulse wave detection method based on the bio-impedance of the wrist

The real-time monitoring of pulse rate can evaluate the heart health to some extent, and the measurement of bio-impedance has the potential in wearable health monitoring system. In this paper, an effective method, which contains self-balancing bridge, flexible electrode, and high-speed digital lock-in algorithm (DLIA) with over-sampling, was designed to detect the impedance pulse wave at the wrist. By applying the self-balancing bridge, the basic impedance can be compensated as much as possible, and the low amplitude of impedance variation related to heart pulse can be obtained more easily. And the flexible conductive rubber electrode used in our experiment is human-friendly. Besides, the over-sampling method and high-speed DLIA are used to enhance the effective resolution of the existing data sampled by analog to digital converter. With the high-speed data process and simple circuit above, this proposed method has the potential in wrist-band wearable systems and it can satisfy quests of small volume and low power consumption.

Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review

Diabetic individuals need to tightly control their blood glucose concentration. Several methods have been developed for this purpose, such as the finger-prick or continuous glucose monitoring systems (CGMs). However, these methods present the disadvantage of being invasive. Moreover, CGMs have limited accuracy, notably to detect hypoglycemia. It is also known that physical exercise, and even daily activity, disrupt glucose dynamics and can generate problems with blood glucose regulation during and after exercise. In order to deal with these challenges, devices for monitoring patients’ physical activity are currently under development. This review focuses on non-invasive sensors using physiological parameters related to physical exercise that were used to improve glucose monitoring in type 1 diabetes (T1DM) patients. These devices are promising for diabetes management. Indeed they permit to estimate glucose concentration either based solely on physical activity parameters or in conjunction with CGM or non-invasive CGM (NI-CGM) systems. In these last cases, the vital signals are used to modulate glucose estimations provided by the CGM and NI-CGM devices. Finally, this review indicates possible limitations of these new biosensors and outlines directions for future technologic developments.

An Internet-based program for depression using activity and physiological sensors: efficacy, expectations, satisfaction, and ease of use

PURPOSE

Computerized cognitive behavioral therapy (CCBT) has been shown to be efficacious. Moreover, CCBT can be enhanced by using physiological and activity sensors, but there is no evidence about the acceptability of all these tools. The objective of this study is to examine the efficacy, expectations, satisfaction, and ease of use of an Internet-based CCBT program for preventing depression, with and without sensors (electroencephalography, electrocardiograhpy ECG, and actigraphy), in a high-risk population (unemployed men).

PATIENTS AND METHODS

Sixty participants at risk of depression (unemployed men) were randomly assigned to three experimental conditions: 1) intervention program (N=22), 2) intervention program plus sensors (N=19), and 3) control group (N=19). Participants completed depression, anxiety, positive and negative affect, and perceived stress measures. Furthermore, they also completed the measures for expectation, satisfaction, and the ease of use of the program.

RESULTS

Results showed that the two intervention groups improved significantly more than the control group on the clinical variables, and the improvements were greater in the group that used sensors than in the group that did not use them. Furthermore, participants in both intervention groups scored high on expectations and satisfaction with the CCBT program (with and without sensors). The mean score for usability was 88 out of 100 (standard deviation =12.32). No significant differences were found between groups on any of these variables.

CONCLUSION

This is the first study to analyze the efficacy, expectations, satisfaction, and ease of use of an Internet-based program using physiological and activity sensors. These results suggest that an Internet program for depression with or without physiological and activity sensors is effective, satisfactory, and easy to use.