Non-invasive Wearable Sensors Research Articles

Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure.

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry.

Biological age estimation using an eHealth system based on wearable sensors

The rapid worldwide aging makes necessary the development of advanced technologies for the objective identification of diseases and disabilities onset. Accordingly, we need to regard the biological age as an alternative and more reliable indicator of the physiological decline of individuals, respect to the simple chronological age. In this paper, we present an eHealth system for estimate the biological age of elderly people starting from the assessment of the frailty phenotype. The frailty phenotype evaluation is made possible using a standard protocol for data acquisition and a cloud application for processing and storing data. The proposed eHealth system is also equipped with wireless, small and non-invasive wearable sensors for an objective evaluation of the mobility of a subject. The eHealth system is tested on a reference population in order to have an amount of data necessary for defining a model to estimate the biological age. The use of the presented system on a reference population, and the availability of data regarding their mobility, allow the validation of the proposed model for the computation of the biological age via simple and objective frailty phenotype assessment.

The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies

BackgroundA common challenge with all opioid use disorder treatment paths is withdrawal management. When withdrawal symptoms are not effectively monitored and managed, they lead to relapse which often leads to deadly overdose. A prerequisite for effective opioid withdrawal management is early identification and assessment of withdrawal symptoms.ObjectiveThe objective of this research was to describe the type and content of opioid withdrawal monitoring methods, including surveys, scales and technology, to identify gaps in research and practice that could inform the design and development of novel withdrawal management technologies.MethodsA scoping review of literature was conducted. PubMed, EMBASE and Google Scholar were searched using a combination of search terms.ResultsWithdrawal scales are the main method of assessing and quantifying opioid withdrawal intensity. The search yielded 18 different opioid withdrawal scales used within the last 80 years. While traditional opioid withdrawal scales for patient monitoring are commonly used, most scales rely heavily on patients’ self-report and frequent observations, and generally suffer from lack of consensus on the criteria used for evaluation, mode of administration, type of reporting (e.g., scales used), frequency of administration, and assessment window.ConclusionsIt is timely to investigate how opioid withdrawal scales can be complemented or replaced with reliable monitoring technologies. Use of noninvasive wearable sensors to continuously monitor physiologic changes associated with opioid withdrawal represents a potential to extend monitoring outside clinical setting.

Highly Stretchable and Strain-Insensitive Fiber-Based Wearable Electrochemical Biosensor to Monitor Glucose in the Sweat.

Development of high-performance fiber-shaped wearable sensors is of great significance for next-generation smart textiles for real-time and out-of-clinic health monitoring. The previous focus has been mainly on monitoring physical parameters such as pressure and strains associated with human activities. Development of an enzyme-based non-invasive wearable electrochemical sensor to monitor biochemical vital signs of health such as the glucose level in sweat has attracted increasing attention recently, due to the unmet clinical needs for the diabetic patients. To achieve this, the key challenge lies in the design of a highly stretchable fiber with high conductivity, facile enzyme immobilization, and strain-insensitive properties. Herein, we demonstrate an elastic gold fiber-based three-electrode electrochemical platform that can meet the aforementioned criteria toward wearable textile glucose biosensing. The gold fiber could be functionalized with Prussian blue and glucose oxidase to obtain the working electrode and modified by Ag/AgCl to serve as the reference electrode; and the nonmodified gold fiber could serve as the counter electrode. The as-fabricated textile glucose biosensors achieved a linear range of 0-500 μM and a sensitivity of 11.7 μA mM-1 cm-2. Importantly, such sensing performance could be maintained even under a large strain of 200%, indicating the potential applications in real-world wearable biochemical diagnostics from human sweat.

The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning.

This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This approach is motivated by the goal of diagnosing gait abnormalities on a symptom-by-symptom basis, irrespective of other neuromuscular movement disorders the patients may be affected by. This could lead to improvements in treatment and offer a greater insight into movement disorders. In-shoe pressure was measured for 12 able-bodied participants, each subject to eight artificially induced gait alterations, achieved by modifying the underside of the shoe. The data were recorded at 100Hz over 2520 data channels and were analyzed using the deep learning architecture and the long term short term memory networks. Additionally, the rationale for the decision-making process of these networks was investigated. Long term short term memory networks are applicable to the classification of the gait function. The classifications can be made using only 2 s of sparse data (82.0% accuracy over 96 000 instances of test data) from participants who were not a part of the training set. This paper provides potential for the gait function to be accurately classified using non-invasive techniques, and at more regular intervals, outside of a clinical setting, without the need for healthcare professionals to be present.

Lung water assessment: from gravimetry to wearables.

Several techniques are now available to detect and quantify pulmonary edema, from the laboratory postmortem method (gravimetry) to non-invasive wearable sensors. In critically ill patients with adult respiratory distress syndrome (ARDS), computed tomography scans are often performed to visualize lung lesions and quantify lung aeration, but their value seems somewhat limited to quantify pulmonary edema on a routine basis and of course to track changes with therapy. In this context, transpulmonary thermodilution is a convenient technique. It is invasive but most patients with ARDS have a central line and an arterial catheter in place. In addition to extravascular lung water measurements, transpulmonary thermodilution enables the measurement of hemodynamic variables that are useful to guide fluid and diuretic therapy. Echo probes are about to replace the stethoscope in our pocket and, if B lines (aka comet tails) do not allow a real quantification of pulmonary edema, they are useful to detect an increase in lung water. Finally, wireless and wearable sensors are now available to monitor patients on hospital wards and beyond (home monitoring). They should enable the detection of pulmonary congestion at a very early stage, and if combined with a proactive therapeutic strategy, have potential to improve outcome.

Noninvasive wearable sensor for indirect glucometry.

A noninvasive mini-sensor for blood glucose concentration assessment has been developed. The monitoring is performed by gently pressing a wrist or fingertip onto the chemochromic mixture coating a thin glass or polymer film positioned on the back panel of a smart watch with PPG/HRM (photoplethysmographic/heart rate monitoring sensor). The various chemochromic components measure the absolute values of the following metabolites present in the sweat: acetone, acetone beta-hydroxybutirate, aceto acetate, water, carbon dioxide, lactate anion, pyruvic acid, Na and K salts. Taken together, all these parameters give information about blood glucose concentration, calculated via multivariate analysis based on neural network algorithms built into the sensor. The Clarke Error Grid shows an excellent correlation between data measured by the standard invasive glucose analyser and the present noninvasive sensor, with all points aligned along a 45-degree diagonal and contained almost exclusively in sector A. Graphs measuring glucose levels five times a day (prior, during and after breakfast and prior, during and after lunch), for different individuals (males and females) show a good correlation between the two curves of conventional, invasive meters vs. the noninvasive sensor, with an error of ±15%. This novel, noninvasive sensor for indirect glucometry is fully miniaturized, easy to use and operate and could represent a valid alternative in clinical settings and for individual, personal users, to current, invasive tools.

Wireless Wearable Magnetometer-Based Sensor for Sleep Quality Monitoring

Obstructive sleep apnoea is one of the most common sleep disorders, which is characterized by the repetitive cessations of respiratory flow during sleep. This paper describes an alternative method to detect sleep parameters by measuring a variation in the earth’s magnetic field. A magnetometer sensor, laid onto the body, detects millimeter night-time breathing movements by measuring the change in the magnetic vectors. A soft, non-invasive wearable sensor is designed to be printed on a miniature printed circuit board, packaged in a minimal invasive board, which includes a wireless Bluetooth low-energy (BLE) module and a low-power microcontroller. A very low power consumption is achieved by applying a smart processing algorithm, which is also described. The algorithm delivers respiration rate, apnoea time, and time in movement. A comparison with a well-known air-flow sensor is also discussed.

Delivering home healthcare through a Cloud-based Smart Home Environment (CoSHE)

The dramatic increase of senior population worldwide is challenging the existing healthcare and support systems. Recently, smart home environments are utilized for ubiquitous health monitoring, allowing patients to stay at the comfort of their homes. In this paper we presented a Cloud-Based Smart Home Environment (CoSHE) for home healthcare. CoSHE collects physiological, motion and audio signals through non-invasive wearable sensors and provides contextual information in terms of the resident’s daily activity and location in the home. This enables healthcare professionals to study daily activities, behavioral changes and monitor rehabilitation and recovery processes. A smart home environment is set up with environmental sensors to provide contextual information. The sensor data are processed in a smart home gateway and sent to a private cloud, which provides real time data access for remote caregivers. Our case study shows that we can successfully integrate contextual information to health data and this comprehensive information can help better understand caretaker’s health status.

Characterization of a Flexible Glucose Amperometric Sensor Obtained through Electroless Deposited Nip Electrodes

Diabetes mellitus is one of the most common chronic diseases in the world (8.5 % of the world population [1]) and current projections estimate an increase in prevalence over the coming years, especially in developing countries [2]. The management of diabetes mellitus requires frequent monitoring of blood glucose levels, using a small blood sample that is obtained through a finger prick. The traditional methods of self-testing are painful, invasive, and are associated a non-negligible risk of infection for the patient. Over the last years the technological development and market demand have led toward wearable non-invasive devices that offer considerable promise for continuous monitoring of a user’s health. Deformable electronics is a promising solution for wearable non-invasive glucose monitoring [3]. This new generation of glucose sensors would maintain the high sensing performance, achieved with the current technology of traditional rigid sensor, also under the demands of wear, which can impose mechanical deformation of the substrate, and be low cost. Flexible non-enzymatic (NEG) sensor is a low cost and simple solution to achieve these requirements for wearable glucose monitoring. In this work, a preliminary study on fabrication and characterization of flexible electrochemical glucose sensors is proposed. A Nickel Phosphorus (NiP) amperometric sensor on Polyethylene terephthalate (PET) substrate is proposed. A thin layer (166 nm) of NiP was deposited by electroless technique directly onto the polymer substrate (PET, 23 mm). Elemental and electrochemical analysis were carried on the deformable sensor. NiP electrode has proven to have good electrochemical behaviour, in accordance with literature. The background cyclic voltammogram in high alkaline medium showed a well-defined curve, with anodic peak at 0.54V vs Ag/AgCl. Voltammetric response at various concentrations of glucose (0.2 mM – 1 mM) was characterized. The sensor did show a good sensitivity, comparable to the literature [4]. The influence of mechanical stress (bending) on the electrochemical response and on the glucose sensing performance of the PET/NiP sensor was examined. As concerns the application envisaged for the NiP sensor demonstrator, this is expected to make contact with the human limbs on the concave side of the bent sheet. For this reason, the tests have been devised to induce compressive stresses on the sensing side of the electrode. Bending the PET/NiP electrode to small radii of curvature (up to 5mm) has no effect upon the electrochemical behaviour, but noteworthy it leads to an enhanced sensitivity. [1] Global report on diabetes, World Health Organization, 2016. [2] L. Guariguata, D.R. Whitingb, I. Hambletonc, J. Beagleya, U. Linnenkampa, J.E. Shawd, Global estimates of diabetes prevalence for 2013 and projections for 2035, Diabetes Research and Clinical Practice, 103, 137-149, 2014. [3] A. J. Bandodkar, J. Wang, Non-invasive wearable electrochemical sensors: a review, Trends in Biotechnology, 32 (7), 363-371, 2014. [4] K. O. Iwu, A. Lombardo, R. Sanz, S.Scir`e, S. Mirabella Facile synthesis of Ni nanofoam for flexible and low-cost non-enzymatic glucose sensing, Sensors and Actuators B, 224, 764-771, 2016.

A Survey on Biofeedback and Actuation in Wireless Body Area Networks (WBANs).

Wireless body area networks (WBANs) have attained increasing popularity as the next generation framework of wearable technologies for human monitoring. Invasive or noninvasive wearable sensors designed in a WBAN are worn to gather vital information. Biofeedback is a recent concept where collected data are used to generate actuation signals in WBANs. Applications can be seen in various areas such as sports (e.g., locomotor velocity) or medicine (e.g., blood pressure measurement). However, since the body is closely regulated, the next generation WBAN technology must be smart enough to react to monitored data. The main aim of this paper is to review the current state of biofeedback and actuation technology on WBANs in terms of its structure, applications, benefits, and control approaches. The emphasis on the specific requirements when applying biofeedback to humans will be highlighted and discussed. Challenges and open research issues will be concluded at the end.

Wearable Heart Rate Sensor Systems for Wireless Canine Health Monitoring

There is an increasing interest from dog handlers and veterinarians in an ability to continuously monitor dogs' vital signs (heart rate, heart rate variability, and respiratory rate) outside laboratory environments, with the aim of identifying physiological correlations to stress, distress, excitement, and other emotional states. We present a non-invasive wearable sensor system combining electrocardiogram (ECG), photoplethysmogram (PPG), and inertial measurement units (IMU) to remotely and continuously monitor the vital signs of dogs. To overcome the limitations imposed by the efficiently insulated skin and dense hair layers of dogs, we investigated the use of various styles of ECG electrodes and the enhancements of these by conductive polymer coatings. We also studied the incorporation of light guides and optical fibers for an efficient optical coupling of PPG sensors to the skin. Combined with our parallel efforts to use IMUs to identify dog behaviors, these physiological sensors will contribute to a canine-body area network to wirelessly and continuously collect data during canine activities with a long-term goal of effectively capturing and interpreting dogs' behavioral responses to environmental stimuli that may yield measurable benefits to handlers' interactions with their dogs.

Biosensor/Biofuel Cell System for Lactate Detection in Sweat, Targeted Towards Human Performance

Non-invasive monitoring of biomarkers in biological fluids through the use of wearable sensors gained significant interest in the past several decades [1]. Research efforts have been focused on the development of chemical and biochemical sensors for a range of metabolites, including glucose, lactate, ethanol, trace metals and various ions, targeting several bodily fluids such as tears, saliva, and sweat [2]. Work presented here will discuss the development of a sensor system, capable of real-time monitoring of lactate levels in sweat. Such a device is desired in order to correlate increasing levels of lactate to fatigue levels of a person performing physically demanding activities [3]. Most existing systems employ traditional batteries as power sources for sensors. The system described here utilizes a light weight biofuel cell as the power source giving a complete bio-friendly sensor system. The system is comprised of three main elements: a biosensor, a biofuel cell, and electronics interface. The biosensor detects lactate levels in sweat via enzymatic reaction of Lactate Dehydrogenase (LDH). Sensor patch features a three electrode design assembled onto generic athletic tape and covered with medical gauze, providing both flexibility and sweat collection capability. LDH is immobilized at the working electrode via conductive carbon-based ink. The electrode surface is then further modified by vapor-deposited tetramethyl orthosilicate (TMOS) coating in order to provide greater stability to the sensor against temperature and pH fluctuations. Sensor calibration time period was relatively quick (5 minutes) and response was immediate with addition of lactate. Test results for the sensor patch showed an open circuit potential of ~0.06V vs. Ag/AgCl and an equilibrated current of approximately 30µA when held at 0.3V vs. Ag/AgCl. The patch also demonstrated a sensitivity of 0.2µA/mM lactate when tested in a range of 5-100mM lactate. A glucose-based biofuel cell provides the power to operate the biosensor. The anode, driving glucose oxidation, is comprised of immobilized Glucose Dehydrogenase (GDH) on a high surface area carbon felt (CF) electrode. Prior to enzyme immobilization, CF electrode surface was modified with electrochemically deposited polymethylene green (PMG) as well as chemically tethered Nicotinamide Adenine Dinucleotide (NAD). An oxygen-reducing cathode was employed as counter electrode. The biofuel cell generated an open circuit potential of 0.7V vs. Ag/AgCl, a total current of 81.0mA, and total power of 16.7mW. The patch sensor is coupled to the biofuel cell via external electrical components: an energy harvester and a micropotentiostat. The energy harvester component is connected to the biofuel cell. Its primary function is to continuously provide stable output voltage to the micropotentiostat. Thus it is constantly drawing power from the biofuel cell and up-converting the voltage from 0.7 to 3V. The patch sensor is connected to the micropotentiostat. A constant potential of 0.3V vs. Ag/AgCl is needed to operate the sensor, which is supplied by the micropotentiostat. Additionally, it is used to provide a read-out of an electrical signal, i.e. current, generated during sensor operation. System performance was demonstrated, employing artificial sweat. Results showed linear sensor response with increasing lactate content of the artificial sweat solution. [1] A. J. Bandodkar and J. Wang, "Non-Invasive Wearable Electrochemical sensors: A Review," Trends in Biotechnology, vol. 32, no. 7, pp. 363-371, 2014. [2] J. Kim, W. R. de Araujo, I. A. Samek, A. J. Bandodkar, W. Jia, B. Brunetti, T. R. Paixao and J. Wang, "Wearable temporary tattoo sensor for real-time trace metal monitoring in human sweat," Electrochemistry Communications, vol. 51, pp. 41-45, 2015. [3] S. Jadoon, S. Karim, M. R. Akram, A. K. Khan, M. A. Zia, A. R. Siddqi and G. Murtaza, "recent Developments in Sweat Analysis and Its Applications," International Journal of Analytical Chemistry, pp. 1-7, 2015. Figure 1

An organic optoelectronic muscle contraction sensor for prosthetics

Non-invasive wearable sensors are desirable for a wide range of medical measurements, and especially for ubiquitous monitoring of a patient’s health. They have applications in the treatment of neurological disorders, depression, drug addiction, and even for rehabilitation. Our work in organic optoelectronic devices suggests a new class of wearable sensors for medicine and sports. Organic semiconductor devices have the attractive properties of tuneable light emission, simple fabrication on flexible substrates with scope for miniaturization, and the ability to generate and detect light.1 We have shown that organic lightemitting diodes (OLEDs) and organic photodiodes (OPDs) can be combined to make compact, non-invasive, flexible sensors for medical applications by simple solution-processingmethods. Specifically, we developed a potentially low-cost muscle contraction sensor that can measure signals from intact muscles to control the movement of active prosthetic devices such as artificial limbs.2 Moreover, we demonstrated the feasibility of this all-organic, optoelectronic sensor by controlling a robotic arm so that it mimicked the motion of a healthy volunteer’s arm for two types of muscle contractions. Our contraction sensor works by measuring light backscattered by skeletal muscle tissue.3 Specifically, our device measures the difference between the light travelling parallel and perpendicular to themuscles. Inside the device, four organic photodiodes surround an organic LED light source. When the muscle contracts, the light scattering alters and the sensor detects the change. Fabricating the sensor is simple: we used semiconducting polymers, which are flexible and can be deposited from solution. Figure 1 shows a photograph of the actual Figure 1. Photograph of the thin flexible organic optoelectronic sensor.

Monitoring of Cigarette Smoking Using Wearable Sensors and Support Vector Machines

Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker-PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers. The performance of subject-dependent (individually calibrated) models was compared to performance of subject-independent (group) classification models. The models were trained and validated on a dataset collected from 20 subjects performing 12 different activities representative of everyday living (total duration 19.5 h or 21,411 breath cycles). Precision and recall were used as the accuracy metrics. Group models obtained 87% and 80% of average precision and recall, respectively. Individual models resulted in 90% of average precision and recall, indicating a significant presence of individual traits in signal patterns. These results suggest the feasibility of monitoring cigarette smoking by means of a wearable and noninvasive sensor system in free living conditions.

Estimation of Human Internal Temperature from Wearable Physiological Sensors

We evaluated a Kalman filter (KF) approach to modeling the physiology of internal temperature viewed through “noisy” non-invasive observations of heart rate. Human core body temperature (Tcore) is an important measure of thermal state, e.g., hypo- or hyperthermia, but is difficult to measure using non-invasive wearable sensors. We estimated parameters for a discrete KF model from data collected during several Military training events and from distance runners (n=38). Model performance was evaluated in 25 physically-active subjects who participated in various laboratory and field studies involving exercise of 2-to-8 h duration at ambient temperatures of 20 to 40°C. Overall, the KF model’s estimate of Tcore had a root mean square error of 0.30±0.13 ºC from the observed Tcore, and was within ± 0.5 ºC over 85% of the time. The benefit of the KF approach is that it requires only one input while current state of the art models typically require multiple inputs including individual anthropometrics, metabolic rate, clothing characteristics, and environmental conditions. This state estimation problem in computational physiology illustrates the potential for collaboration between the artificial intelligence and ambulatory physiological monitoring communities.

Post-exercise contractility, diastolic function, and pressure: Operator-independent sensor-based intelligent monitoring for heart failure telemedicine

BackgroundNew sensors for intelligent remote monitoring of the heart should be developed. Recently, a cutaneous force-frequency relation recording system has been validated based on heart sound amplitude and timing variations at increasing heart rates.AimTo assess sensor-based post-exercise contractility, diastolic function and pressure in normal and diseased hearts as a model of a wireless telemedicine system.MethodsWe enrolled 150 patients and 22 controls referred for exercise-stress echocardiography, age 55 ± 18 years. The sensor was attached in the precordial region by an ECG electrode. Stress and recovery contractility were derived by first heart sound amplitude vibration changes; diastolic times were acquired continuously. Systemic pressure changes were quantitatively documented by second heart sound recording.ResultsInterpretable sensor recordings were obtained in all patients (feasibility = 100%). Post-exercise contractility overshoot (defined as increase > 10% of recovery contractility vs exercise value) was more frequent in patients than controls (27% vs 8%, p < 0.05). At 100 bpm stress heart rate, systolic/diastolic time ratio (normal, < 1) was > 1 in 20 patients and in none of the controls (p < 0.01); at recovery systolic/diastolic ratio was > 1 in only 3 patients (p < 0.01 vs stress). Post-exercise reduced arterial pressure was sensed.ConclusionPost-exercise contractility, diastolic time and pressure changes can be continuously measured by a cutaneous sensor. Heart disease affects not only exercise systolic performance, but also post-exercise recovery, diastolic time intervals and blood pressure changes – in our study, all of these were monitored by a non-invasive wearable sensor.