Wearable Sensing Systems for Multi-Modal Body Fluid Monitoring: Sensing-Combination Strategy, Platform-Integration Mechanism, and Data-Processing Pattern.

PurposeWearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users.Design/methodology/approachMA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored.FindingsAccording to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems.Originality/valueThis study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

A wireless wearable physiological parameter monitoring system for home health care is studied in this paper. Based on the wireless wearable physiological parameter monitoring system of human research, we introduced realization of the ZigBee in wearable physiological monitoring system parameter acquisition module.

Unobtrusive sensors are an important element in wearable systems. One approach in constructing unobtrusive transducers is to use smart materials and then integrate them into smart unobtrusive fabric structures. These types of transducers are called fabric transducers (Fibre-Meshed Transducers or FMTs). Fabrication is carried out via textiles manufacturing processes. In this paper we have discussed three types of FMTs and their applications. Also the discussion is further extended towards real time physiological monitoring system. We have constructed resistive, inductive and capacitive transducers with electronic flatbed knitting technology. The resistive FMTs that were developed inherited with limitations. The inductive FMTs are used for motion and gesture capturing of the kinematical joints of the human body, and capacitive FMTs are used as bio-potential electrodes, which were used to measure ECG. Also we have used the capacitive FMTs as switches. Further we have developed a PDA based wearable physiological monitoring system by using these novel FMTs.

Wearable remote health monitoring systems have gained significant prominence in the recent years due to their growth in technological advances. One form of the Wearable Physiological Monitoring System (WPMS) is the Wearable Body Area Networks (WBAN) used to monitor the health status of the wearer for long durations. The paper discusses a prototype WBAN based wearable physiological monitoring system to monitor physiological parameters such as Electrocardiogram (ECG) and Electroencephalogram (EEG) acquired using a textile electrode, Photoplethysmogram (PPG), Galvanic Skin Response (GSR), Blood Pressure derived from analysis of Pulse Transmit Time (PTT) and body temperature. The WBAN consists of three sensor nodes that are placed strategically to acquire the physiological signals and the sensor nodes communicate to a chest/wrist worn sink node also known as wearable data acquisition hardware. The sink node receives physiological data from the sensor nodes and is transmitted to a remote monitoring station. The remote monitoring station receives the raw data and it is processed to remove noises, such as power line interference, baseline wander and tremor in the signals and the information is extracted and displayed. The WBANs are designed using the ZigBee wireless communication modules to transmit and receive the data. At the remote monitoring station the physiological parameters such as heart rate, pulse rate, systolic, diastolic blood pressure, GSR and body temperature are continuously monitored from the wearer. The data acquired from the wearable monitoring system is statically validated using a qualified medical device on 34 subjects.

Respiratory rate, a sensitive indicator of respiratory status, is rarely measured during the field walking test. Our objective was to develop and validate a non-invasive, wearable monitoring system using stretchable strain sensors and an accompanying algorithm capable of providing real-time measurements of respiration during exercise. Twenty-four healthy volunteers wore stretchable sensors during a walking test protocol that included standing, sitting, walking, and walking with a stick. Sensors were placed on the ribcage and abdomen. The Bland-Altman method was used to assess the accuracy and precision of breath counts; total respiration time and inspiration time ratio were determined by custom algorithms and compared with measurements obtained with the standard flow sensor. The output signal from the stretchable sensor was highly synchronized with flow signals. The limits of agreement were within 3 breaths/min throughout the test protocol. Differences between sensors for total respiration time and inspiration time ratio were less than 14% and 26%, respectively. The agreement was maintained regardless of respiratory rate or volume. The wearable respiratory monitoring system yielded accurate and precise breath counts and total duration of respiratory cycle during moderate exercise in healthy young individuals, suggesting that it might be useful in clinical practice. Graphical abstract.

In order to rapidly promote the application of wearable plantar pressure monitoring system, the physiological structure of human foot, the source of plantar pressure and exercise step frequency are introduced. Based on the current research status of wearable plantar pressure monitoring systems, the fabrication materials and response principles of the fabric sensor-based integrated pressure monitoring socks are explored, the principle of selecting the features of the wearable plantar pressure monitoring system and its application in the field of the pressure monitoring system is explained. The principle of selecting the features of wearable plantar pressure monitoring system and its application in fall detection, foot disease diagnosis, and plantar pressure database are explained. Finally, we discussed the problems in the industrialization of wearable plantar pressure monitoring system at this stage. The problems of poor material performance and short wireless transmission distance in the industrialization of wearable plantar pressure monitoring systems are discussed, and a better integrated system based on biomechanics, textile materials and electronic communication is proposed. A better application prospect based on the cross-fusion integration of biomechanics, textile materials and electronic communication is proposed.

This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed by the researchers to date. One of the earliest wearable recorders, named “a silicon locket for ECG monitoring”, was developed at the Indian Institute of Technology, Bombay, in 2003. Thus, the wearable health monitoring systems, started with the acquisition of a single signal/ parameter to the present generation smart and affordable multi-parameter recording/monitoring systems, have evolved manifolds in these two decades. Wearable systems have dramatically changed in terms of size, cost, functionality, and accuracy. The early-day wearable recorders were with limited functionalities against today’s systems, e.g., Apple’s iWatch which comprises abundant health monitoring features like heart rate monitoring, breathing app, accelerometers, smart walking/ activity monitoring, and alerts. Most of the present-day smartphones are not only capable of recording various health features like body temperature, heart rate, photoplethysmograph (PPG) signal, calory consumption, smart activity monitoring, stress measurement, etc. through different apps, but they also help the user to get monitored by a family physician via GSM or even internet of things (IoT). One of the latest, state-of-the-art real-time personal health monitoring systems, Wearable IoT-cloud-based health monitoring system (WISE), is a beautiful amalgamation of body area sensor network (BASN) and IoT framework for ubiquitous health monitoring. The future of wearable health monitoring systems will be far beyond the IoT and BASN.

IoT-empowered wearable multimodal monitoring system (IEMS), an IEMS for neurocritical care is developed to perform simultaneous monitoring of 8-channel electroencephalogram (EEG), 2-channel regional cerebral oxygen saturation (rSO2) based on near-infrared spectrum (NIRS), body surface temperature, electrocardiogram (ECG), photoplethysmography (PPG), and bioimpedance (Bio-Z). The IoT platform and wireless devices enable the patients’ signals available for remote diagnosis. Besides, analysis functions and artificial intelligence (AI) algorithms could be embedded in both the bedside platform and the cloud server to support physicians with clinical decisions. In the multimodal neural monitoring device, the following designs are adopted to face the neurological intensive care unit (NICU) application. Active electrodes and preamplifying free topology provide better signal quality and a larger dynamic range (DR). Dedicated low-power designs ensure the device lasts 10 h of operation. A nonwoven headset improves long-term wearability, which is also quick and easy to install. In the cardiovascular patch, the disposable electrode patch based on elastic materials ensures tight and comfortable contact with skin. Besides, the reusable wireless sensing module is tiny (20 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times16$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times9$ </tex-math></inline-formula> mm) but could measure ECG, PPG, and Bio-Z simultaneously. Electrical tests and human subject (healthy volunteers and NICU patients) studies were conducted to examine the performance. The EEG channels show 130.75-dB DR and 0.84- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu {}\text{V}_{\mathrm{ RMS}}$ </tex-math></inline-formula> input-referred noise, which also yields signals with high quality during human EEG monitoring. The NIRS channels exhibit good linearity and are able to operate under severe ambient interference. The temperature sensors show ±0.2 °C accuracy. Moreover, the system complies with mandatory standards for medical equipment. Overall, an IEMS can meet the requirements for NICU applications and could provide better comfort during long-term wearing.

Motion monitoring systems are often designed and researched to detect the movement of human lower limbs, and play an important role in the field of exoskeleton control. However, current wearable devices can still be improved to be more convenient or accurate in motion recognition. In this work, a comfortable smart wearable gait monitoring system was designed and tested. Inertial measurement units (IMUs) and flexible membrane compression sensors were implemented, integrated to a comfortable sport pant and insoles of both feet, respectively. Data acquisition module was designed, while software with user interface for data collection and storage was realized based on LABVIEW. Experiments were conducted to evaluate the recognition performance of the smart wearable gait monitoring system among nine common actions. Results show that the combined data set of IMUs and compression sensor provided by the system can highly improve classification performance. Based on the self-designed sensing network and the K-nearest neighbor machine learning algorithm, the recognition rate of nine motion patterns can reach as high as 99.96%, showing that the multi-channel wearable gait monitoring system is more effective for motion detection and prediction compared to that with single-type sensors.

To achieve continuously physiological monitoring on hospital inpatients, a ubiquitous and wearable physiological monitoring system SensEcho was developed. The whole system consists of three parts: a wearable physiological monitoring unit, a wireless network and communication unit and a central monitoring system. The wearable physiological monitoring unit is an elastic shirt with respiratory inductive plethysmography sensor and textile electrocardiogram (ECG) electrodes embedded in, to collect physiological signals of ECG, respiration and posture/activity continuously and ubiquitously. The wireless network and communication unit is based on WiFi networking technology to transmit data from each physiological monitoring unit to the central monitoring system. A protocol of multiple data re-transmission and data integrity verification was implemented to reduce packet dropouts during the wireless communication. The central monitoring system displays data collected by the wearable system from each inpatient and monitors the status of each patient. An architecture of data server and algorithm server was established, supporting further data mining and analysis for big medical data. The performance of the whole system was validated. Three kinds of tests were conducted: validation of physiological monitoring algorithms, reliability of the monitoring system on volunteers, and reliability of data transmission. The results show that the whole system can achieve good performance in both physiological monitoring and wireless data transmission. The application of this system in clinical settings has the potential to establish a new model for individualized hospital inpatients monitoring, and provide more precision medicine to the patients with information derived from the continuously collected physiological parameters.

Health and long-term care is a growth area for wearable heath monitoring systems. Wearable diagnostic and therapeutic systems can contribute to timely point-of-care (POC) for patients with chronic health conditions, especially chronic neurological disorders, cardiovascular diseases and strokes that are leading causes of mortality worldwide. Diagnostics and therapeutics for patients under timely POC can save thousands of lives. However, lack of access to minimally intrusive monitoring systems makes timely diagnosis difficult and sometimes impossible. Existing ambulatory recording equipment is incapable of performing continuous remote patient monitoring (RPM) because of the inability for conventional silver-silver-chloride-gel-electrodes to perform long-term monitoring, non-reusability, lack of scalable-standardized wireless communication platforms, and user-friendly design. Recent progress in nanotextile biosensors and mobile platforms has resulted in novel wearable health monitoring systems for neurological and cardiovascular disorders. This chapter discusses nanostructured-textile-based dry electrodes that are better suited for long-term measurement of electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), and bioimpedance with very low baseline noise, improved sensitivity, and seamless integration into garments of daily use. It discusses bioelectromagnetic principles of origination and propagation of bioelectric signals and nanosensor functioning, which provide a unique perspective on the development of novel wearable systems that harness their potential. Combined with state-of-the-art embedded wireless network devices and printable fractal antenna to communicate with smartphone, laptop, or directly to remote server through mobile network (GSM, 4G-LTE, GPRS), they can function as wearable wireless health diagnostic systems that are more intuitive to use.

Comprehensive and quantitative assessment of human physical activity in daily life is valuable for healthcare, especially for those who suffer from obesity and neurological disorders or are at high risk of dementia. Common wearable devices, e.g., smartwatches, are insufficient and inaccurate for monitoring highly dynamic limb movements and assessing human motion. Here, we report a new wearable leg movement monitoring system incorporating a custom-made motion sensor with machine learning algorithm to perceive human motion accurately and comprehensively during diverse walking and running actions. The system enables real-time multimodal perceptions of personal identity, motion state, locomotion speed, and energy expenditure for wearers. A general law of extracting real-time metabolic energy from leg movements is verified although individual gaits show differences. In addition, we propose a novel sensing configuration combining unilateral lower leg movement velocity with its angular rate to achieve high accuracy and good generalizability while simplifying the wearable system. Advanced performances in personal identification (accuracy of 98.7%) and motion-state recognition (accuracy of 93.7%) are demonstrated. The wearable system also exhibites high-precision real-time estimations of locomotion speed (error of 3.04% to 9.68%) and metabolic energy (error of 4.18% to 14.71%) for new subjects across various time-varying conditions. The wearable system allows reliable leg movement monitoring and quantitative assessment of bodily kinematic and kinetic behaviors during daily activities, as well as safe identity authentication by gait parameters, which would greatly facilitate smart life, personal healthcare, and rehabilitation training.

Pulse wave monitoring is critical for the evaluation of human health. In this paper, a wearable multi-sensor pulse wave monitoring system is proposed and demonstrated. The monitoring system consists of a measuring unit and an analog circuit processing unit. The main part of the measuring unit is a flexible printed circuit board (PCB) with a thickness of 0.15 mm, which includes three micro-electromechanical system (MEMS) pressure sensors softly packaged by polydimethylsiloxane (PDMS), a blood oxygen detector and a MEMS three-axis accelerometer. The MEMS pressure sensors，the blood oxygen detector and the accelerometer are fixed on the expected locations of the flexible PCB. The analog circuit processing unit includes a power supply module, a filter and an amplifier. The pulse waves of two volunteers are detected by the monitoring system in this study. The output signals of the analog circuit processing module are processed and analyzed. In the preliminary test, the time delay of the three pressure pulse waves has been detected and the calculated pulse wave velocities (PWVs) are 12.50 and 11.36 m/s, respectively. The K value, related to the area of the pulse wave, can be obtained. Both the PWV and K value meet the health parameter standards.

Continuous monitoring of pulse waves plays a significant role in reflecting physical conditions and disease diagnosis. However, the current collection equipment cannot simultaneously achieve wearable and continuous monitoring under varying pressure and provide personalized pulse wave monitoring targeted different human bodies. To solve the above problems, this paper proposed a novel wearable and real-time pulse wave monitoring system based on a novel flexible compound sensor. Firstly, a custom-packaged pressure sensor, a signal stabilization structure, and a micro pressurization system make up the flexible compound sensor to complete the stable acquisition of pulse wave signals under continuously varying pressure. Secondly, a real-time algorithm completes the analysis of the trend of the pulse wave peak, which can quickly and accurately locate the best pulse wave for different individuals. Finally, the experimental results show that the wearable system can both realize continuous monitoring and reflecting trend differences and quickly locate the best pulse wave for different individuals with the 95% accuracy. The weight of the whole system is only 52.775 g, the working current is 46 mA, and the power consumption is 160 mW. Its small size and low power consumption meet wearable and portable scenarios, which has significant research value and commercialization prospects.

Wearable biosensors are playing an increasingly important role in society. Compared with traditional wearable biosensors for detecting blood pressure, blood oxygen, or pulse conditions, which can only access information from the physical level, biosensors for testing body fluids can provide more details on health conditions through the analysis of biochemical criteria. Sweat secreted from glands distributed throughout the body contains abundant biochemical information and is an indicator of the physical conditions. Because of the noninvasive and safe sampling method, wearable sweat monitoring systems have the potential to realize long‐term and wearable detection. In this review, the current situation of wearable sweat monitoring systems is summarized from three critical parts: the sweat collection method, the sweat analysis method, and the energy supply. Finally, based on the existing drawbacks of wearable sweat monitoring systems in previous studies, the authors creatively propose droplet‐based detection, triboelectric nanogenerator‐based detection, and self‐powered systems as new directions for future research. The proposed approaches are expected to promote the commercialization process of wearable sweat monitoring systems.