Wearable sensors and systems. From enabling technology to clinical applications

Flexible Biosensors for the Impedimetric Detection of Protein Targets Using Silk-Conductive Polymer Biocomposites

To expand the applications of flexible biosensors in point-of-care healthcare applications beyond monitoring of biophysical parameters, it is important to devise strategies for the detection of various proteins and biomarkers. Here, we demonstrate a flexible, fully organic, biodegradable, label-free impedimetric biosensor for the critical biomarker, vascular endothelial growth factor (VEGF). This biosensor was constructed by photolithographically patterning a conducting ink consisting of a photoreactive silk sericin coupled with a conducting polymer. These functional electrodes are printed on flexible fibroin substrates that are controllably thick and can be free-standing, or conform to soft surfaces. Detection was accomplished via the antibody to VEGF which was immobilized within the conducting matrix. The results indicated that the developed flexible biosensor was highly sensitive and selective to the target protein, even in challenging biofluids such as human serum. The biosensors themselves are biocompatible and degradable. Through this work, the developed flexible biosensor based on a simple and label-free strategy can find practical applications in the monitoring of wound healing or early disease diagnosis.

Electronic textile electrocardiogram monitoring in cardiac patients: a scoping review protocol

REVIEW QUESTION/OBJECTIVE

This scoping review aims to explore and scope the literature and research on the use of e-textile electrocardiogram (ECG) monitoring in cardiac patients and provide a unique contribution to the available evidence. The objectives of this scoping review are:The questions of this review are.

The design, fabrication, and applications of flexible biosensing devices

Flexible biosensors form part of a rapidly growing research field that take advantage of a multidisciplinary approach involving materials, fabrication and design strategies to be able to function at biological interfaces that may be soft, intrinsically curvy, irregular, or elastic. Numerous exciting advancements are being proposed and developed each year towards applications in healthcare, fundamental biomedical research, food safety and environmental monitoring. In order to place these developments in perspective, this review is intended to present an overview on field of flexible biosensor development. We endeavor to show how this subset of the broader field of flexible and wearable devices presents unique characteristics inherent in their design. Initially, a discussion on the structure of flexible biosensors is presented to address the critical issues specific to their design. We then summarize the different materials as substrates that can resist mechanical deformation while retaining their function of the bioreceptors and active elements. Several examples of flexible biosensors are presented based on the different environments in which they may be deployed or on the basis of targeted biological analytes. Challenges and future perspectives pertinent to the current and future stages of development are presented. Through these summaries and discussion, this review is expected to provide insights towards a systematic and fundamental understanding for the fabrication and utilization of flexible biosensors, as well as inspire and improve designs for smart and effective devices in the future.

Multiband ultra-thin flexible on-body transceivers for wearable health informatics

Substantial concentration has been associated to the monitoring of vital signs and human activity using wireless body area networks. However, one of the key technical challenges is to characterize an optimized transceiver geometry for desired isolation/bandwidth and specific absorption rate (SAR) characteristics, independent of transceiver chip on-body location. A microwave performance evaluation of monopole wearable transceiver was completed and results presented. A novel on-body antenna transceiver was designed, simulated and fabricated using an ultra-thin substrate RO 3010 (h = 250 µm) that ensures compactness and enhanced flexibility. The designed transceiver was evolved using very high value of dielectric constant using CST® Studio Suit and FEKO® numerical platforms. The on-body characterization for both fatty and bone tissues was experimentally verified for a bandwidth of 200 MHz. The fabricated configuration and real-time testing provides very promising microwave radiation parameters with a gain of 2.69 dBi, S₁₁ < - 13 dB at an operational frequency of 2.46 GHz. Multi-banding was achieved by introducing fractals in the design of the printed monopole. SAR calculations for feet, head and arm at microwave power levels ranging from 100 to 800 mW are incorporated. Furthermore, the real time data acquisition using developed transceiver and its experimental verification is illustrated.

Physical Extraction and Feature Fusion for Multi-Mode Signals in a Measurement System for Patients in Rehabilitation Exoskeleton

Measurement system of exoskeleton robots can reflect the state of the patient. In this study, we combined an inertial measurement unit and a visual measurement unit to obtain a repeatable fusion measurement system to compensate for the deficiencies of the single data acquisition mode used by exoskeletons. Inertial measurement unit is comprised four distributed angle sensors. Triaxial acceleration and angular velocity information were transmitted to an upper computer by Bluetooth. The data sent to the control center were processed by a Kalman filter to eliminate any noise. Visual measurement unit uses camera to acquire real time images and related data information. The two data acquisition methods were fused and have its weight. Comparisons of the fusion results with individual measurement results demonstrated that the data fusion method could effectively improve the accuracy of system. It provides a set of accurate real-time measurements for patients in rehabilitation exoskeleton and data support for effective control of exoskeleton robot.

Closing the Wearable Gap: Mobile Systems for Kinematic Signal Monitoring of the Foot and Ankle

Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data “from the ground up” and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers’ hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements.

A flexible wearable sensor for knee flexion assessment during gait

BACKGROUND

Gait analysis plays an important role in the diagnosis and management of patients with movement disorders but it is usually performed within a laboratory. Recently interest has shifted towards the possibility of conducting gait assessments in everyday environments thus facilitating long-term monitoring. This is possible by using wearable technologies rather than laboratory based equipment.

RESEARCH QUESTION

This study aims to validate a novel wearable sensor system's ability to measure peak knee sagittal angles during gait.

METHODS

The proposed system comprises a flexible conductive polymer unit interfaced with a wireless acquisition node attached over the knee on a pair of leggings. Sixteen healthy volunteers participated to two gait assessments on separate occasions. Data was simultaneously collected from the novel sensor and a gold standard 10 camera motion capture system. The relationship between sensor signal and reference knee flexion angles was defined for each subject to allow the transformation of sensor voltage outputs to angular measures (degrees). The knee peak flexion angle from the sensor and reference system were compared by means of root mean square error (RMSE), absolute error, Bland-Altman plots and intra-class correlation coefficients (ICCs) to assess test-retest reliability.

RESULTS

Comparisons of knee peak flexion angles calculated from the sensor and gold standard yielded an absolute error of 0.35(±2.9°) and RMSE of 1.2(±0.4)°. Good agreement was found between the two systems with the majority of data lying within the limits of agreement. The sensor demonstrated high test-retest reliability (ICCs>0.8).

SIGNIFICANCE

These results show the ability of the sensor to monitor knee peak sagittal angles with small margins of error and in agreement with the gold standard system. The sensor has potential to be used in clinical settings as a discreet, unobtrusive wearable device allowing for long-term gait analysis.

Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals

OBJECTIVE

To gather information on the (a) types of wearable sensors, particularly personal activity monitors, currently used by occupational safety and health (OSH) professionals; (b) potential benefits of using such technologies in the workplace; and (c) perceived barriers preventing the widespread adoption of wearable sensors in industry.

BACKGROUND

Wearable sensors are increasingly being promoted as a means to improve employee health and well-being, and there is mounting evidence supporting their use as exposure assessment and personal health tools. Despite this, many workplaces have been hesitant to adopt these technologies.

METHODS

An electronic survey was emailed to 28,428 registered members of the American Society of Safety Engineers (ASSE) and 1,302 professionals certified by the Board of Certification in Professional Ergonomics (BCPE).

RESULTS

A total of 952 valid responses were returned. Over half of respondents described being in favor of using wearable sensors to track OSH-related risk factors and relevant exposure metrics at their respective workplaces. However, barriers including concerns regarding employee privacy/confidentiality of collected data, employee compliance, sensor durability, the cost/benefit ratio of using wearables, and good manufacturing practice requirements were described as challenges precluding adoption.

CONCLUSION

The broad adoption of wearable technologies appears to depend largely on the scientific community's ability to successfully address the identified barriers.

APPLICATION

Investigators may use the information provided to develop research studies that better address OSH practitioner concerns and help technology developers operationalize wearable sensors to improve employee health and well-being.

Wearable Heading Estimation for Motion Tracking in Health Care by Adaptive Fusion of Visual-Inertial Measurements

The increasing demand for health informatics has become a far-reaching trend in the ageing society. The utilization of wearable sensors enables monitoring senior people daily activities in free-living environments, conveniently and effectively. Among the primary health-care sensing categories, the wearable visual-inertial modality for human motion tracking gradually exerts promising potentials. In this paper, we present a novel wearable heading estimation strategy to track the movements of human limbs. It adaptively fuses inertial measurements with visual features following locality constraints. Body movements are classified into two types: general motion (which consists of both rotation and translation). or degenerate motion (which consists of only rotation). A specific number of feature correspondences between camera frames are adaptively chosen to satisfy both the feature descriptor similarity constraint and the locality constraint. The selected feature correspondences and inertial quaternions are employed to calculate the initial pose, followed by the coarse-to-fine procedure to iteratively remove visual outliers. Eventually, the ultimate heading is optimized using the correct feature matches. The proposed method has been thoroughly evaluated on the straight-line, rotatory and ambulatory movement scenarios. As the system is lightweight and requires small computational resources, it enables effective and unobtrusive human motion monitoring, especially for the senior citizens in the long-term rehabilitation.

A 3D-Printed Sensor for Monitoring Biosignals in Small Animals

Although additive manufacturing technologies, also known as 3D printing, were first introduced in the 1980s, they have recently gained remarkable popularity owing to decreased costs. 3D printing has already emerged as a viable technology in many industries; in particular, it is a good replacement for microfabrication technology. Microfabrication technology usually requires expensive clean room equipment and skilled engineers; however, 3D printing can reduce both cost and time dramatically. Although 3D printing technology has started to emerge into microfabrication manufacturing and medical applications, it is typically limited to creating mechanical structures such as hip prosthesis or dental implants. There have been increased interests in wearable devices and the critical part of such wearable devices is the sensing part to detect biosignals noninvasively. In this paper, we have built a 3D-printed sensor that can measure electroencephalogram and electrocardiogram from zebrafish. Despite measuring biosignals noninvasively from zebrafish has been known to be difficult due to that it is an underwater creature, we were able to successfully obtain electrophysiological information using the 3D-printed sensor. This 3D printing technique can accelerate the development of simple noninvasive sensors using affordable equipment and provide an economical solution to physiologists who are unfamiliar with complicated microfabrication techniques.

A data-driven approach to modeling physical fatigue in the workplace using wearable sensors

Wearable sensors are currently being used to manage fatigue in professional athletics, transportation and mining industries. In manufacturing, physical fatigue is a challenging ergonomic/safety "issue" since it lowers productivity and increases the incidence of accidents. Therefore, physical fatigue must be managed. There are two main goals for this study. First, we examine the use of wearable sensors to detect physical fatigue occurrence in simulated manufacturing tasks. The second goal is to estimate the physical fatigue level over time. In order to achieve these goals, sensory data were recorded for eight healthy participants. Penalized logistic and multiple linear regression models were used for physical fatigue detection and level estimation, respectively. Important features from the five sensors locations were selected using Least Absolute Shrinkage and Selection Operator (LASSO), a popular variable selection methodology. The results show that the LASSO model performed well for both physical fatigue detection and modeling. The modeling approach is not participant and/or workload regime specific and thus can be adopted for other applications.

Development of a wearable plantar force measurement device for gait analysis in remote conditions

The pressure field that exists between the foot and the supporting surface is identified as the foot plantar pressure. The information obtained from foot plantar pressure measurements has useful applications that include diagnosis of gait disturbances, optimization of footwear design, sport biomechanics and prevention of injury. Using wearable technology to measure foot plantar pressure continuously allows the collection of comprehensive real-life data sets while interfering minimally with the subject's daily activities. This paper presents the design of a wearable device to measure foot plantar pressure. Mechanical and electrical design considerations as well as data analysis are discussed. A pilot study involving 20 physically fit volunteers (15 males and 5 females, ageing from 20 - 45) performing a variety of physical activities (such as standing, walking, jumping and climbing up and down stairs) illustrate the potential of the device in terms of its wearability, and suitability for unobtrusive long-term monitoring.

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.

Wearable technology for spine movement assessment: A systematic review

Continuous monitoring of spine movement function could enhance our understanding of low back pain development. Wearable technologies have gained popularity as promising alternative to laboratory systems in allowing ambulatory movement analysis. This paper aims to review the state of art of current use of wearable technology to assess spine kinematics and kinetics. Four electronic databases and reference lists of relevant articles were searched to find studies employing wearable technologies to assess the spine in adults performing dynamic movements. Two reviewers independently identified relevant papers. Customised data extraction and quality appraisal form were developed to extrapolate key details and identify risk of biases of each study. Twenty-two articles were retrieved that met the inclusion criteria: 12 were deemed of medium quality (score 33.4-66.7%), and 10 of high quality (score >66.8%). The majority of articles (19/22) reported validation type studies. Only 6 reported data collection in real-life environments. Multiple sensors type were used: electrogoniometers (3/22), strain gauges based sensors (3/22), textile piezoresistive sensor (1/22) and accelerometers often used with gyroscopes and magnetometers (15/22). Two sensors units were mainly used and placing was commonly reported on the spine lumbar and sacral regions. The sensors were often wired to data transmitter/logger resulting in cumbersome systems. Outcomes were mostly reported relative to the lumbar segment and in the sagittal plane, including angles, range of motion, angular velocity, joint moments and forces. This review demonstrates the applicability of wearable technology to assess the spine, although this technique is still at an early stage of development.

Compensation for Magnetic Disturbances in Motion Estimation to Provide Feedback to Wearable Robotic Systems

The direction of the Earth's magnetic field is used as a reference vector to determine the heading in orientation estimation with wearable sensors. However, the magnetic field strength is weak and can be easily disturbed in the vicinity of ferromagnetic materials, which may result in inaccurate estimate of orientation. This paper presents a novel method for estimating and compensating for magnetic disturbances. The compensation algorithm is implemented within a kinematic-based extended Kalman filter and is based on an assessment of the magnetic disturbance and the change of orientation in each time step. The proposed algorithm was experimentally validated by measuring the orientation of a simple mechanical system with three degrees of freedom in an artificially disturbed magnetic field. The results of the experimental evaluation show that an Kalman filter algorithm that incorporates compensating for magnetic disturbances is capable of estimating the orientation with moderate error (the absolute median errors , ) when the Earth's magnetic field is disturbed by magnetic disturbance with a magnitude equal to twice the magnitude of the Earth's own magnetic field in different directions.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology.

OBJECTIVES

This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring).

DATA SOURCES

The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016.

STUDY ELIGIBILITY CRITERIA

Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers.

RESULTS

Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

Learning new movements after paralysis: Results from a home-based study

Body-machine interfaces (BMIs) decode upper-body motion for operating devices, such as computers and wheelchairs. We developed a low-cost portable BMI for survivors of cervical spinal cord injury and investigated it as a means to support personalized assistance and therapy within the home environment. Depending on the specific impairment of each participant, we modified the interface gains to restore a higher level of upper body mobility. The use of the BMI over one month led to increased range of motion and force at the shoulders in chronic survivors. Concurrently, subjects learned to reorganize their body motions as they practiced the control of a computer cursor to perform different tasks and games. The BMI allowed subjects to generate any movement of the cursor with different motions of their body. Through practice subjects demonstrated a tendency to increase the similarity between the body motions used to control the cursor in distinct tasks. Nevertheless, by the end of learning, some significant and persistent differences appeared to persist. This suggests the ability of the central nervous system to concurrently learn operating the BMI while exploiting the possibility to adapt the available mobility to the specific spatio-temporal requirements of each task.

Nanogenerators for Human Body Energy Harvesting

Humans generate remarkable quantities of energy while performing daily activities, but this energy usually dissipates into the environment. Here, we address recent progress in the development of nanogenerators (NGs): devices that are able to harvest such body-produced biomechanical and thermal energies by exploiting piezoelectric, triboelectric, and thermoelectric physical effects. In designing NGs, the end-user's comfort is a primary concern. Therefore, we focus on recently developed materials giving flexibility and stretchability to NGs. In addition, we summarize common fabrics for NG design. Finally, the mid-2020s market forecasts for these promising technologies highlight the potential for the commercialization of NGs because they may help contribute to the route of innovation for developing self-powered systems.

Common-mode noise cancellation circuit for wearable ECG

Wearable electrocardiogram (ECG) is attracting much attention for monitoring heart diseases in healthcare and medical applications. However, an imbalance usually exists between the contact resistances of sensing electrodes, so that a common mode noise caused by external electromagnetic field can be converted into the ECG detection circuit as a differential mode interference voltage. In this study, after explaining the mechanism of how the common mode noise is converted to a differential mode interference voltage, the authors propose a circuit with cadmium sulphide photo-resistors for cancelling the imbalance between the contact resistances and confirm its validity by simulation experiment. As a result, the authors found that the interference voltage generated at the wearable ECG can be effectively reduced to a sufficient small level.

Current and Emerging Technology for Continuous Glucose Monitoring

Diabetes has become a leading cause of death worldwide. Although there is no cure for diabetes, blood glucose monitoring combined with appropriate medication can enhance treatment efficiency, alleviate the symptoms, as well as diminish the complications. For point-of-care purposes, continuous glucose monitoring (CGM) devices are considered to be the best candidates for diabetes therapy. This review focuses on current growth areas of CGM technologies, specifically focusing on subcutaneous implantable electrochemical glucose sensors. The superiority of CGM systems is introduced firstly, and then the strategies for fabrication of minimally-invasive and non-invasive CGM biosensors are discussed, respectively. Finally, we briefly outline the current status and future perspective for CGM systems.

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions.

Increasing trend of wearables and multimodal interface for human activity monitoring: A review

Activity recognition technology is one of the most important technologies for life-logging and for the care of elderly persons. Elderly people prefer to live in their own houses, within their own locality. If, they are capable to do so, several benefits can follow in terms of society and economy. However, living alone may have high risks. Wearable sensors have been developed to overcome these risks and these sensors are supposed to be ready for medical uses. It can help in monitoring the wellness of elderly persons living alone by unobtrusively monitoring their daily activities. The study aims to review the increasing trends of wearable devices and need of multimodal recognition for continuous or discontinuous monitoring of human activity, biological signals such as Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG) and parameters along with other symptoms. This can provide necessary assistance in times of ominous need, which is crucial for the advancement of disease-diagnosis and treatment. Shared control architecture with multimodal interface can be used for application in more complex environment where more number of commands is to be used to control with better results in terms of controlling.

Development of an eyewear to measure eye and body movements

To enable precise detection of mental and physical states of users in a daily life, we have been developing an eyewear to measure eye and body movement in a unrestricted way. The horizontal and vertical EOG (electrooculogram) signals are measured and amplified with three metal dry electrodes placed near nasion and both sides of rhinion, of which positions correspond to the bridge and nose pads of eyewear, respectively. The user's mental states like drowsiness, sleepiness, fatigue, or interest to objects can be identified by the movements and blinking of the eyes extracted from the measured EOG. And the six-axis motion sensor (three-axis accelerometer and three-axis gyroscope) mounted in the eyewear measures the body motion. As the sensor located near the head is on the body axis, this eyewear is suitable to measure user's movement or shift of center of gravity during physical exercise with a high precision. The measured signals are used to extract various events of eye and body movement by the mounted microcontroller chip, or can be transmitted to the external devices via Bluetooth communication. This device can enable you to look into "yourself", as well as outer scenes. In this presentation, the outline of the eyewear is introduced and some possible applications are shown.

A Smart Wearable Sensor System for Counter-Fighting Overweight in Teenagers

PEGASO is a FP7-funded project whose goal is to develop an ICT and mobile-based platform together with an appropriate strategy to tackle the diffusion of obesity and other lifestyle-related illnesses among teenagers. Indeed, the design of an engaging strategy, leveraging a complementary set of technologies, is the approach proposed by the project to promote the adoption of healthy habits such as active lifestyle and balanced nutrition and to effectively counter-fight the emergence of overweight and obesity in the younger population. A technological key element of such a strategy sees the adoption of wearable sensors to monitor teenagers’ activities, which is at the basis of developing awareness about the current lifestyle. This paper describes the experience carried out in the framework of the PEGASO project in developing and evaluating wearable monitoring systems addressed to adolescents. The paper describes the methodological approach based on the co-designing of such a wearable system and the main results that, in the first phase, involved a total of 407 adolescents across Europe in a series of focus groups conducted in three countries for the requirements definition phase. Moreover, it describes an evaluation process of signal reliability during the usage of the wearable system. The main results described here are: (a) a prototype of the standardized experimental protocol that has been developed and applied to test signal reliability in smart garments; (b) the requirements definition methodology through a co-design activity and approach to address user requirements and preferences and not only technological specifications. Such co-design approach is able to support a higher system acceptance and usability together with a sustained adoption of the solution with respect to the traditional technology push system development strategy.

Conformal electronics for longitudinal bio-sensing in at-home assistive and rehabilitative devices

Wearable electronics are revolutionizing personalized and preventative healthcare by allowing the easy, unobtrusive, and long term monitoring of a range of body parameters. Conformal electronics which attach directly to the skin in a very robust and long term manner are envisioned as the next generation of highly portable miniaturized computing devices, beyond wearables. In this paper we overview the state-of-the-art in conformal electronics created using silver nanoparticle inkjet printed techniques for home assistive and rehabilitative devices. The barriers to wider adaption, particularly the challenges of high performance antenna design when placed close to the body, are discussed in detail.

Performance of human body communication-based wearable ECG with capacitive coupling electrodes

Wearable electrocardiogram (ECG) is attracting much attention in daily healthcare applications, and human body communication (HBC) technology provides an evident advantage in making the sensing electrodes of ECG also working for transmission through the human body. In view of actual usage in daily life, however, non-contact electrodes to the human body are desirable. In this Letter, the authors discussed the ECG circuit structure in the HBC-based wearable ECG for removing the common mode noise when employing non-contact capacitive coupling electrodes. Through the comparison of experimental results, they have shown that the authors' proposed circuit structure with the third electrode directly connected to signal ground can provide an effect on common mode noise reduction similar to the usual drive-right-leg circuit, and a sufficiently good acquisition performance of ECG signals.

Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice.

Technology in Parkinson's disease: Challenges and opportunities

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society.

An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram

Over 100 algorithms have been proposed to estimate respiratory rate (RR) from the electrocardiogram (ECG) and photoplethysmogram (PPG). As they have never been compared systematically it is unclear which algorithm performs the best. Our primary aim was to determine how closely algorithms agreed with a gold standard RR measure when operating under ideal conditions. Secondary aims were: (i) to compare algorithm performance with IP, the clinical standard for continuous respiratory rate measurement in spontaneously breathing patients; (ii) to compare algorithm performance when using ECG and PPG; and (iii) to provide a toolbox of algorithms and data to allow future researchers to conduct reproducible comparisons of algorithms. Algorithms were divided into three stages: extraction of respiratory signals, estimation of RR, and fusion of estimates. Several interchangeable techniques were implemented for each stage. Algorithms were assembled using all possible combinations of techniques, many of which were novel. After verification on simulated data, algorithms were tested on data from healthy participants. RRs derived from ECG, PPG and IP were compared to reference RRs obtained using a nasal-oral pressure sensor using the limits of agreement (LOA) technique. 314 algorithms were assessed. Of these, 270 could operate on either ECG or PPG, and 44 on only ECG. The best algorithm had 95% LOAs of  -4.7 to 4.7 bpm and a bias of 0.0 bpm when using the ECG, and  -5.1 to 7.2 bpm and 1.0 bpm when using PPG. IP had 95% LOAs of  -5.6 to 5.2 bpm and a bias of  -0.2 bpm. Four algorithms operating on ECG performed better than IP. All high-performing algorithms consisted of novel combinations of time domain RR estimation and modulation fusion techniques. Algorithms performed better when using ECG than PPG. The toolbox of algorithms and data used in this study are publicly available.

Impact of wearable technology on psychosocial factors of osteoarthritis management: a qualitative study

OBJECTIVES

To identify the impact the use of wearable technology could have in patients with osteoarthritis in terms of communication with healthcare providers and patients' empowerment to manage their condition.

DESIGN

Qualitative study using focus groups with patients with osteoarthritis; data from patients' responses were analysed using Framework Methodology.

PARTICIPANTS

21 patients with knee osteoarthritis from the London area (age range 45-65 years) participated in a total of four focus groups. Recruitment continued until data saturation.

SETTING

The study was conducted in a university setting.

RESULTS

Patients' responses suggested a positive attitude on the impact wearable technology could have on the management of osteoarthritis. It was perceived that the use of wearable devices would benefit patients in terms of feeling in control of their condition, providing them with awareness of their progress, empowering in terms of self-management and improving communication with their clinician.

CONCLUSIONS

This paper suggests positive patient perspectives on the perceived benefits wearable technology could have on the management of osteoarthritis. The data that could be collected with the use of wearable technology could be beneficial both to patients and clinicians. The information obtained from this study suggests that introducing wearable technology into patient-centred care could enhance patient experience in the field of osteoarthritis and beyond.

Wearable technologies in osteoarthritis: a qualitative study of clinicians' preferences

OBJECTIVE

This study investigates clinicians' views of health-related wearable technologies in the context of supporting osteoarthritis (OA) long-term management. Clinicians' preferences are critical in identifying realistic implementation strategies for such technologies.

DESIGN

Qualitative study incorporating an inductive thematic analysis applied to identify key themes from clinicians' responses.

PARTICIPANTS

Clinicians, including 4 general practitioners, 4 physiotherapists and 5 orthopaedic surgeons were interviewed.

SETTING

The study was conducted in a University setting.

RESULTS

Participants all agreed wearable technologies could positively complement their role and enhance their relationship with patients. Perceived benefits of wearable technologies included monitoring patients' progress, treatment evaluation, monitoring compliance and informing clinical decision-making. The device should be designed to provide objective data of patients' locomotion capability in an easy and timely fashion via a simple interface. Data should be available to both clinicians and patients to provide them with the motivation to achieve clinical goals and allow them to take ownership of their treatment. The use of technology was also seen as a way to more effectively plan treatment and manage patients' contact time saving time and cost.

CONCLUSIONS

Findings support the use of wearable technologies to enhance current OA management and suggest clinical uses. Adoption of technologies could have implications on the effectiveness of treatment provided overcoming current barriers, in particular compliance with treatment.

A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington's Disease Patients

Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington’s disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist. By extracting features from group-specific Hidden Markov Models (HMMs) and signal information in time and frequency domain, a Support Vector Machines classifier (SVM) was designed and validated. The 90.5% of subjects was assigned to the right group after leave-one-subject–out cross validation and majority voting. The long-term goal we point to is the gait assessment in everyday life to early detect gait alterations.

Context aware falls risk assessment: A case study comparison

This paper describes three retrospective case studies to illustrate the potential clinical value of a system capable of capturing objective gait metrics and environment data from older adults with a history of falls while they go about their daily lives. Participants in this study wore an inertial sensor above each ankle and a wearable camera around their neck for seven consecutive days. Selected metrics are presented to illustrate scenarios where the data collected by the system could be of clinical value. Evidence suggests that obtaining objective gait metrics and environment data from older adults may not only allow healthcare professionals to assess gait more accurately, but also to design treatment plans and falls prevention strategies that are more specifically tailored to each individual.

Context focused older adult mobility and gait assessment

This paper presents an initial overview of insights gained into how older adults mobilize in the home and community, based on data from inertial sensors which were worn by study participants over a 7-day period. The addition of a wearable camera provided additional contextual information which can be used to assess mobility and understand the factors that influence it in the free living environment. Seven days of data collected from a group of older adults who had experienced one or more falls in the previous six months was compared to that of a control group with no history of falling. Results showed that both groups spent relatively little time walking in challenging environmental conditions, and that the fallers spent significantly less time walking under regular conditions (no effect on gait) and outdoors. Analysis of gait metrics showed that the fallers were slightly slower in general, and more noticeable differences were observed when the participants were regrouped according to mobility levels determined from baseline assessments using traditional methods.

Wearable ECG Based on Impulse-Radio-Type Human Body Communication

Human body communication (HBC) provides a promising physical layer for wireless body area networks (BANs) in healthcare and medical applications, because of its low propagation loss and high security characteristics. In this study, we have developed a wearable electrocardiogram (ECG) which employs impulse radio (IR)-type HBC technology for transmitting vital signals on the human body in a wearable BAN scenario. The HBC-based wearable ECG has two excellent features. First, the wideband performance of the IR scheme contributed to very low radiation power so that the transceiver is easy to satisfy the extremely weak radio laws, which does not need a license. This feature can provide big convenience in the use and spread of the wearable ECG. Second, the realization of common use of sensing and transmitting electrodes based on time sharing and capacitive coupling largely simplified the HBC-based ECG structure and contributed to its miniaturization. To verify the validity of the HBC-based ECG, we evaluated its communication performance and ECG acquisition performance. The measured bit error rate, smaller than 10 ^-3 at 1.25 Mb/s, showed a good physical layer communication performance, and the acquired ECG waveform and various heart-rate variability parameters in time and frequency domains exhibited good agreement with a commercially available radio-frequency ECG and a Holter ECG. These results sufficiently showed the validity and feasibility of the HBC-based ECG for healthcare applications. This should be the first time to have realized a real-time ECG transmission by using the HBC technology.

Experimentally Derived Kinetic Model for Sensor-Based Gait Monitoring

A method for estimating gait parameters (shank, thigh, and stance leg angles) from a single, in situ, scalar acceleration measurement is presented. A method for minimizing the impact of errors due to unpredictable variations in muscle actuation and acceleration measurement biases is developed. This is done by determining the most probable gait progression by minimization of a cost function that reflects the size of errors in the gait parameters. In addition, a model for gait patterns that takes into account their variations due to walking speed is introduced and used. The approach is tested on data collected from subjects in a gait study. The approach can estimate limb angles with errors less than 6 deg (one standard deviation) and, thus, is suitable for many envisioned gait monitoring applications in nonlaboratory settings.

A reliability study of the new sensors for movement analysis (SHARIF-HMIS)

AIM

SHARIF-HMIS is a new inertial sensor designed for movement analysis. The aim of the present study was to assess the inter-tester and intra-tester reliability of some kinematic parameters in different lumbar motions making use of this sensor.

MATERIALS AND METHODS

24 healthy persons and 28 patients with low back pain participated in the current reliability study. The test was performed in five different lumbar motions consisting of lumbar flexion in 0, 15, and 30° in the right and left directions. For measuring inter-tester reliability, all the tests were carried out twice on the same day separately by two physiotherapists. Intra-tester reliability was assessed by reproducing the tests after 3 days by the same physiotherapist.

FINDINGS

The present study revealed satisfactory inter- and intra-tester reliability indices in different positions. ICCs for intra-tester reliability ranged from 0.65 to 0.98 and 0.59 to 0.81 for healthy and patient participants, respectively. Also, ICCs for inter-tester reliability ranged from 0.65 to 0.92 for the healthy and 0.65 to 0.87 for patient participants.

CONCLUSION

In general, it can be inferred from the results that measuring the kinematic parameters in lumbar movements using inertial sensors enjoys acceptable reliability.

A Survey of Mobile Phone Sensing, Self-Reporting, and Social Sharing for Pervasive Healthcare

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e., the provision of healthcare services to individuals anytime anywhere, has become a major focus for the research community. In this paper, we map out the current state of pervasive healthcare research by presenting an overview of three emerging areas in personalized health monitoring, namely: 1) mobile phone sensing via in-built or external sensors, 2) self-reporting for manually captured health information, such as symptoms and behaviors, and 3) social sharing of health information within the individual's community. Systems deployed in a real-life setting as well as proofs-of-concept for achieving pervasive health are presented, in order to identify shortcomings and increase our understanding of the requirements for the next generation of pervasive healthcare systems addressing these three areas.

Objective Assessment of Upper-Limb Mobility for Poststroke Rehabilitation

The assessment of the limb mobility of stroke patients is an essential part of poststroke rehabilitation. Conventionally, the assessment is manually performed by clinicians using chart-based ordinal scales, which can be subjective and inefficient. By introducing quantitative evaluation measures, the sensitivity and efficiency of the assessment process can be significantly improved. In this paper, a novel single-index-based assessment approach for quantitative upper-limb mobility evaluation has been proposed for poststroke rehabilitation. Instead of the traditional human-observation-based measures, the proposed assessment system utilizes the kinematic information automatically collected during a regular rehabilitation training exercise using a wearable inertial measurement unit. By calculating a single index, the system can efficiently generate objective and consistent quantitative results that can reflect the stroke patient's upper-limb mobility. In order to verify and validate the proposed assessment system, experiments have been conducted using 145 motion samples collected from 21 stroke patients (12 males, nine females, mean age 58.7±19.3) and eight healthy participants. The results have suggested that the proposed assessment index can not only differentiate the levels of limb function impairment clearly (p < 0.001, two-tailed Welch's t-test), but also strongly correlate with the Brunnstrom stages of recovery (r = 0.86, p < 0.001). The assessment index is also proven to have great potential in automatic Brunnstrom stage classification application with an 82.1% classification accuracy, while using a K-nearest-neighbor classifier.

A knee monitoring device and the preferences of patients living with osteoarthritis: a qualitative study

OBJECTIVES

To identify perspective of patients with osteoarthritis, in particular design requirements and mode of use, of wearable technology to support the rehabilitation pathway. This study is part of a user-centred design approach adopted to develop a rehabilitation tool for patients with osteoarthritis.

DESIGN

Qualitative study using a focus group approach; data management via a thematic analysis of patients' responses.

PARTICIPANTS

21 patients with osteoarthritis (age range 45-65 years) participated in 1 of the 4 focus groups. Recruitment continued until data saturation.

SETTING

The study was conducted in a university setting.

RESULTS

Main determinants of user acceptance of a wearable technology were appearance and comfort during use. Patients were supportive of the use of wearable technologies during rehabilitation and could recognise their benefit as monitors for their progress, incentives to adhere to exercise, and tools for more informed interaction with clinicians.

CONCLUSIONS

This paper should encourage adoption and development of wearable technology to support rehabilitation of patients with osteoarthritis. It is pivotal that technological development takes into account patients' views in that it should be small, light, discrete, not 'appear medical' or challenge the identity of the user. Derived data should be available to patients and clinicians. Furthermore, wearable technologies should be developed to operate in two modes: for exercise guidance and assessment only, and for unobtrusive everyday monitoring. The information obtained from this study should guide the design of new technologies and support their use in clinical practice.

Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

Human Activity Recognition by Combining a Small Number of Classifiers

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

A primer on precision medicine informatics

In this review, we describe key components of a computational infrastructure for a precision medicine program that is based on clinical-grade genomic sequencing. Specific aspects covered in this review include software components and hardware infrastructure, reporting, integration into Electronic Health Records for routine clinical use and regulatory aspects. We emphasize informatics components related to reproducibility and reliability in genomic testing, regulatory compliance, traceability and documentation of processes, integration into clinical workflows, privacy requirements, prioritization and interpretation of results to report based on clinical needs, rapidly evolving knowledge base of genomic alterations and clinical treatments and return of results in a timely and predictable fashion. We also seek to differentiate between the use of precision medicine in germline and cancer.

Detecting Elementary Arm Movements by Tracking Upper Limb Joint Angles With MARG Sensors

This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close ( ±6%) to the average value, for different task durations further attesting to the algorithms robustness.

Design, fabrication and metrological evaluation of wearable pressure sensors

Pressure sensors are valuable transducers that are necessary in a huge number of medical application. However, the state of the art of compact and lightweight pressure sensors with the capability of measuring the contact pressure between two surfaces (contact pressure sensors) is very poor. In this work, several types of wearable contact pressure sensors are fabricated using different conductive textile materials and piezo-resistive films. The fabricated sensors differ in size, the textile conductor used and/or the number of layers of the sandwiched piezo-resistive film. The intention is to study, through the obtaining of their calibration curves, their metrological properties (repeatability, sensitivity and range) and determine which physical characteristics improve their ability for measuring contact pressures. It has been found that it is possible to obtain wearable contact pressure sensors through the proposed fabrication process with satisfactory repeatability, range and sensitivity; and that some of these properties can be improved by the physical characteristics of the sensors.