Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges

Toward flexible piezoresistive strain sensors based on polymer nanocomposites: a review on fundamentals, performance, and applications

The fundamentals, performance, and applications of piezoresistive strain sensors based on polymer nanocomposites are summarized herein. The addition of conductive nanoparticles to a flexible polymer matrix has emerged as a possible alternative to conventional strain gauges, which have limitations in detecting small strain levels and adapting to different surfaces. The evaluation of the properties or performance parameters of strain sensors such as the elongation at break, sensitivity, linearity, hysteresis, transient response, stability, and durability are explained in this review. Moreover, these nanocomposites can be exposed to different environmental conditions throughout their lifetime, including different temperature, humidity or acidity/alkalinity levels, that can affect performance parameters. The development of flexible piezoresistive sensors based on nanocomposites has emerged in recent years for applications related to the biomedical field, smart robotics, and structural health monitoring. However, there are still challenges to overcome in designing high-performance flexible sensors for practical implementation. Overall, this paper provides a comprehensive overview of the current state of research on flexible piezoresistive strain sensors based on polymer nanocomposites, which can be a viable option to address some of the major technological challenges that the future holds.

High Sensitivity and Wide Linear Range Flexible Piezoresistive Pressure Sensor with Microspheres as Spacers for Pronunciation Recognition

Flexible piezoresistive pressure sensors have received great popularity in flexible electronics due to their simple structure and promising applications in health monitoring and artificial intelligence. However, the contradiction between sensitivity and detection range limits the application of the sensors in the medium-pressure regime. Here, a flexible piezoresistive pressure sensor is fabricated by combining a hierarchical spinous microstructure sensitive layer and a periodic microsphere array spacer. The sensor achieves high sensitivity (39.1 kPa-1) and outstanding linearity (0.99, R2 coefficient) in a medium-pressure regime, as well as a wide range of detection (100 Pa-160.0 kPa), high detection precision (<0.63‰ full scale), and excellent durability (>5000 cycles). The mechanism of the microsphere array spacer in improving sensitivity and detection range was revealed through finite element analysis. Furthermore, the sensors have been utilized to detect muscle and joint movements, spatial pressure distributions, and throat movements during pronouncing words. By means of a full-connect artificial neural network for machine learning, the sensor's output of different pronounced words can be precisely distinguished and classified with an overall accuracy of 96.0%. Overall, the high-performance flexible pressure sensor based on a microsphere array spacer has great potential in health monitoring, human-machine interface, and artificial intelligence of medium-pressure regime.

Current On-Skin Flexible Sensors, Materials, Manufacturing Approaches, and Study Trends for Health Monitoring: A Review

Due to an ever-increasing amount of the population focusing more on their personal health, thanks to rising living standards, there is a pressing need to improve personal healthcare devices. These devices presently require laborious, time-consuming, and convoluted procedures that heavily rely on cumbersome equipment, causing discomfort and pain for the patients during invasive methods such as sample-gathering, blood sampling, and other traditional benchtop techniques. The solution lies in the development of new flexible sensors with temperature, humidity, strain, pressure, and sweat detection and monitoring capabilities, mimicking some of the sensory capabilities of the skin. In this review, a comprehensive presentation of the themes regarding flexible sensors, chosen materials, manufacturing processes, and trends was made. It was concluded that carbon-based composite materials, along with graphene and its derivates, have garnered significant interest due to their electromechanical stability, extraordinary electrical conductivity, high specific surface area, variety, and relatively low cost.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Skin-like and highly elastic optical fiber strain sensor based on a knot-bend shape for human motion monitoring

A simply designed, highly sensitive, stretchable, compact wearable, and skin-like optical fiber sensing instrument is designed and demonstrated for joint motion monitoring. The fiber sensing scheme comprises only a section of single-mode fiber (SMF) deformed in the knot-like configuration, which performs as a Mach-Zehnder interferometer (MZI) based on a modal coupling mechanism between the core and cladding modes of the deformed SMF section. This proposed optical fiber sensor based on a knot-like configuration is mounted onto wearable woven fabric and then garments on the limbs of a healthy human's body. As the flexion angle of the human limb is varied, the interference fringe coding based on the spectral shift difference of the periodical transmission spectra is perceived. The proposed wearable optical fiber sensor exhibits excellent sensitivities from around -0.431 to -0.614n m/∘ realized for elbow and knee joint flexion between a range of motion around 0°-90°. Additionally, this sensor also displays high repeatability and stability and a fast response time of 1.4 ms, combined with a small standard deviation of about 2.585%. The proposed sensor device possesses manufacturing simplicity, high processing accuracy, lightness, and elasticity, as well as certain improvements over other goniometers and optical fiber sensors. These attributes of the proposed sensor prove its applicability for human joint angle monitoring.

Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics

Bionic robotics, driven by advancements in artificial intelligence, new materials, and manufacturing technologies, is attracting significant attention from research and industry communities seeking breakthroughs. One of the key technologies for achieving a breakthrough in robotics is flexible sensors. This paper presents a novel approach based on wavelength and time division multiplexing (WTDM) for distributed optical waveguide shape sensing. Structurally designed optical waveguides based on color filter blocks validate the proposed approach through a cost-effective experimental setup. During data collection, it combines optical waveguide transmission loss and the way of controlling the color and intensity of the light source and detecting color and intensity variations for modeling. An artificial neural network is employed to model and demodulate a data-driven optical waveguide shape sensor. As a result, the correlation coefficient between the predicted and real bending angles reaches 0.9134 within 100 s. To show the parsing performance of the model more intuitively, a confidence accuracy curve is introduced to describe the accuracy of the data-driven model at last.

All Knitted and Integrated Soft Wearable of High Stretchability and Sensitivity for Continuous Monitoring of Human Joint Motion

E-textiles have recently gained significant traction in the development of soft wearables for healthcare applications. However, there have been limited works on wearable e-textiles with embedded stretchable circuits. Here, stretchable conductive knits with tuneable macroscopic electrical and mechanical properties are developed by varying the yarn combination and the arrangement of stitch types at the meso-scale. Highly extensible piezoresistive strain sensors are designed (>120% strain) with high sensitivity (gauge factor 8.47) and durability (>100,000 cycles), interconnects (>140% strain) and resistors (>250% strain), optimally arranged to form a highly stretchable sensing circuit. The wearable is knitted with a computer numerical control (CNC) knitting machine that offers a cost effective and scalable fabrication method with minimal post-processing. The real-time data from the wearable is transmitted wirelessly using a custom designed circuit board. In this work, an all knitted and fully integrated soft wearable is demonstrated for wireless and continuous real-time sensing of knee joint motion of multiple subjects performing various activities of daily living.

Ultralow-Latency Textile Sensors for Wearable Interfaces with a Human-in-Loop Sensing Approach

The evolution of wearable technologies has led to the development of novel types of sensors customized for a wide range of applications. Wearable sensors need to possess a low form factor and be ergonomic, causing minimal impediment of the user's natural movement. Various principles have been explored to meet these requirements, ranging from optical, magnetic, resistive flex sensing to 3D printed sensors and liquid metals such as those using eutectic gallium-indium. However, manufacturing techniques for most current wearable sensors tend to be complex and difficult to scale. Challenges also exist in achieving high sensitivity with noise resistance and robustness to false detections, especially in capacitive sensors. In this research, a novel ultralow-latency soft tactile and pressure sensor developed using off-the-shelf e-textiles is proposed, which overcomes some of these limitations. The sensor does not use any specialized equipment or materials for manufacture. A human-in-loop (HIL) sensing technique is demonstrated, which provides high sensitivity, high sensing bandwidth, as well as ultralow latency, which makes it ideal as a wearable input device. In addition, the HIL method provides other advantages such as high noise rejection and resistance to accidental triggers that could be caused by other humans or environmental factors owing to its high signal to noise ratio. Finally, two applications-a wearable keyboard and gaming input device-were demonstrated using these sensors.

Optoelectronic Pressure Sensor Based on the Bending Loss of Plastic Optical Fibers Embedded in Stretchable Polydimethylsiloxane

We report the design and testing of a sensor pad based on optical and flexible materials for the development of pressure monitoring devices. This project aims to create a flexible and low-cost pressure sensor based on a two-dimensional grid of plastic optical fibers embedded in a pad of flexible and stretchable polydimethylsiloxane (PDMS). The opposite ends of each fiber are connected to an LED and a photodiode, respectively, to excite and measure light intensity changes due to the local bending of the pressure points on the PDMS pad. Tests were performed in order to study the sensitivity and repeatability of the designed flexible pressure sensor.

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A quantitative evaluation of kinetic parameters, the joint's range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device's positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user's vital signs directly from the body in an accurate and non-invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach's subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post-operative rehabilitation and athletes' training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user's health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

Data-driven multi-joint waveguide bending sensor based on time series neural network

Due to the bulky interrogation devices, traditional fiber optic sensing system is mainly connected by wire or equipped only for large facilities. However, the advancement in neural network algorithms and flexible materials has broadened its application scenarios to bionics. In this paper, a multi-joint waveguide bending sensor based on color dyed filters is designed to detect bending angles, directions and positions. The sensors are fabricated by casting method using soft silicone rubber. Besides, required optical properties of sensor materials are characterized to better understand principles of the sensor design. Time series neural networks are utilized to predict bending position and angle quantitatively. The results confirm that the waveguide sensor demodulated by the data-driven neural network algorithm performs well and can be used for engineering applications.

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Objective

Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases.

Methods

A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection.

Results

The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance.

Conclusions

This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of 1.19 MPa−1, a wide working pressure range (<872.4 kPa), excellent stability and durability (at least 2.280 cycles), great linearity (R2=0.993), fast response/recovery time (142−160 ms), low hysteresis (DH<10%) and the ability to support a broad spectrum of gait speeds (30−70 steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications.

Recent Advances in Multifunctional Wearable Sensors and Systems: Design, Fabrication, and Applications

Multifunctional wearable sensors and systems are of growing interest over the past decades because of real-time health monitoring and disease diagnosis capability. Owing to the tremendous efforts of scientists, wearable sensors and systems with attractive advantages such as flexibility, comfort, and long-term stability have been developed, which are widely used in temperature monitoring, pulse wave detection, gait pattern analysis, etc. Due to the complexity of human physiological signals, it is necessary to measure multiple physiological information simultaneously to evaluate human health comprehensively. This review summarizes the recent advances in multifunctional wearable sensors, including single sensors with various functions, planar integrated sensors, three-dimensional assembled sensors, and stacked integrated sensors. The design strategy, manufacturing method, and potential application of each type of sensor are discussed. Finally, we offer an outlook on future developments and provide perspectives on the remaining challenges and opportunities of wearable multifunctional sensing technology.

Liquid metal injected from interstitial channels for inhibiting subcutaneous hepatoma growth and improving MRI/MAT image contrast

Objective

Liquid metal (LM) nowadays is considered a new biomedical material for medical treatment. The most common application of LM in medical therapy is taking LM as a carrier for oncology therapeutics. However, the feasibility and direct effect of LM in tumor treatment are still unknown, and how to delineate the negative resection margin (NRM) of the tumor is also a crucial problem in surgery. We aimed to inject LM into interstitial channels of extremities of mice to overlay the surface of the primary tumor to investigate the effect of LM on inhibiting tumor growth and highlight the NRM of the tumor.

Methods

In this study, all 50 BALB/c-nude female mice were used to construct the transplanted HepG2-type hepatocellular carcinoma model. One week after the establishment of the model, the mice were divided into three groups, named LM group, PBS group and Control group by injecting different liquid materials into the forelimb interstitial channel of the mice. T2WI image on MRI and Magneto-acoustic tomography (MAT) were used to show the distribution of LM and PBS in vivo. The group comparisons of tumor growth and blood tests were evaluated by one-way ANOVA and post-hoc analysis. And the biocompatibility of LM to BALB/c nude mice was evaluated by histopathological analysis of LM group and control group.

Results

The volume change ratio of tumor was significantly lower in LM group than in PBS and Control group after 10 days of grouping. Compared with PBS and Control group, the main indexes of blood tests in LM group were significantly lower and close to normal level. In addition, the distribution of LM in vivo could be clearly observed under T2WI anatomic images and the crossprofile of the tumor in MAT. LM also has a obvious contrast in MRI T2WI and enhanced the amplitude of imaging signal in MAT.

Conclusion

LM may inhibit the growth of transplanted hepatoma tumor through tumor encapsulation. In vivo, tumor imaging and LM distribution imaging were achieved by MRI T2WI, which verified that LM injected with interstitial injection made the NRM of tumor more prominent and had the potential of being MRI contrast agent. At the same time, LM could also be a new conductive medium to improve the imaging quality of MAT. Moreover, LM performed mild biocompatibility.

Recent Progress on Triboelectric Nanogenerators for Vibration Energy Harvesting and Vibration Sensing

The triboelectric nanogenerator (TENG) is a recent technology that reforms kinetic energy generation and motion sensing. A TENG comes with variety of structures and mechanisms that make it suitable for wide range of applications and working conditions. Since mechanical vibrations are abundant source of energy in the surrounding environment, the development of a TENG for vibration energy harvesting and vibration measurements has attracted a huge attention and great research interest through the past two decades. Due to the high output voltage and high-power density of a TENG, it can be used as a sustainable power supply for small electronics, smart devices, and wireless sensors. In addition, it can work as a vibration sensor with high sensitivity. This article reviews the recent progress in the development of a TENG for vibration energy harvesting and vibration measurements. Systems of only a TENG or a hybrid TENG with other transduction technologies, such as piezoelectric and electromagnetic, can be utilized for vibrations scavenging. Vibration measurement can be done by measuring either vibration displacement or vibration acceleration. Each can provide full information about the vibration amplitude and frequency. Some TENG vibration-sensing architectures may also be used for energy harvesting due to their large output power. Numerous applications can rely on TENG vibration sensors such as machine condition monitoring, structure health monitoring, and the Internet of things (IoT).

Development of Piezoresistive Sensors Based on Graphene Nanoplatelets Screen-Printed on Woven and Knitted Fabrics: Optimisation of Active Layer Formulation and Transversal/Longitudinal Textile Direction

Although the force/pressure applied onto a textile substrate through a uniaxial compression is constant and independent of the yarn direction, it should be noted that such mechanical action causes a geometric change in the substrate, which can be identified by the reduction in its lateral thickness. Therefore, the objective of this study was to investigate the influence of the fabric orientation on both knitted and woven pressure sensors, in order to generate knowledge for a better design process during textile piezoresistive sensor development. For this purpose, these distinct textile structures were doped with different concentrations of graphene nanoplatelets (GNPs), using the screen-printing technique. The chemical and physical properties of these screen-printed fabrics were analysed using Field Emission Scanning Electron Microscopy, Ground State Diffuse Reflectance and Raman Spectroscopy. Samples were subjected to tests determining linear electrical surface resistance and piezoresistive behaviour. In the results, a higher presence of conductive material was found in woven structures. For the doped samples, the electrical resistance varied between 10⁵ Ω and 10¹ Ω, for the GNPs’ percentage increase. The lowest resistance value was observed for the woven fabric with 15% GNPs (3.67 ± 8.17 × 10¹ Ω). The samples showed different electrical behaviour according to the fabric orientation. Overall, greater sensitivity in the longitudinal direction and a lower coefficient of variation CV% of the measurement was identified in the transversal direction, coursewise for knitted and weftwise for woven fabrics. The woven fabric doped with 5% GNPs assembled in the weftwise direction was shown to be the most indicated for a piezoresistive sensor, due to its most uniform response and most accurate measure of mechanical stress.

Easy-Scalable Flexible Sensors Made of Carbon Nanotube-Doped Polydimethylsiloxane: Analysis of Manufacturing Conditions and Proof of Concept

Carbon nanotube (CNT) reinforced polydimethylsiloxane (PDMS) easy-scalable sensors for human motion monitoring are proposed. First, the analysis of the dispersion procedure of nanoparticles into the polymer matrix shows that the ultrasonication (US) technique provides a higher electrical sensitivity in comparison to three-roll milling (3RM) due to the higher homogeneity of the CNT distribution induced by the cavitation forces. Furthermore, the gauge factor (GF) calculated from tensile tests decreases with increasing the CNT content, as the interparticle distance between CNTs is reduced and, thus, the contribution of the tunnelling mechanisms diminishes. Therefore, the optimum conditions were set at 0.4 CNT wt.% dispersed by US procedure, providing a GF of approximately 37 for large strains. The electrical response under cycling load was tested at 2%, 5%, and 10% strain level, indicating a high robustness of the developed sensors. Thus, this strain sensor is in a privileged position with respect to the state-of-the-art, considering all the characteristics that this type of sensor must accomplish: high GF, high flexibility, high reproducibility, easy manufacturing, and friendly operation. Finally, a proof-of-concept of human motion monitoring by placing a sensor for elbow and finger movements is carried out. The electrical resistance was found to increase, as expected, with the bending angle and it is totally recovered after stretching, indicating that there is no prevalent damage and highlighting the huge robustness and applicability of the proposed materials as wearable sensors.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

Development of a Non-Contacting Muscular Activity Measurement System for Evaluating Knee Extensors Training in Real-Time

To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors and optical motion capture system are expensive and not suitable for non-technical users and unstructured environment. For this reason, in our group we are researching methods to estimate leg muscle activity using non-contact wearable sensors, improving ease of movement and system usability. In a previous study, we developed a method to estimate muscle activity via only a single inertial measurement unit (IMU) on the shank. In this study, we describe a method to estimate muscle activity during walking via two IMU sensors, using an original sensing system and specifically developed estimation algorithms based on ANN techniques. The muscle activity estimation results, estimated by the proposed algorithm after optimization, showed a relatively high estimation accuracy with a correlation efficient of R² = 0.48 and a standard deviation STD = 0.10, with a total system average delay of 192 ms. As the average interval between different gait phases in human gait is 250–1000 ms, a 192 ms delay is still acceptable for daily walking requirements. For this reason, compared with the previous study, the newly proposed system presents a higher accuracy and is better suitable for real-time leg muscle activity estimation during walking.

Piezoelectric nanogenerators for personalized healthcare

The development of flexible piezoelectric nanogenerators has experienced rapid progress in the past decade and is serving as the technological foundation of future state-of-the-art personalized healthcare. Due to their highly efficient mechanical-to-electrical energy conversion, easy implementation, and self-powering nature, these devices permit a plethora of innovative healthcare applications in the space of active sensing, electrical stimulation therapy, as well as passive human biomechanical energy harvesting to third party power on-body devices. This article gives a comprehensive review of the piezoelectric nanogenerators for personalized healthcare. After a brief introduction to the fundamental physical science of the piezoelectric effect, material engineering strategies, device structural designs, and human-body centered energy harvesting, sensing, and therapeutics applications are also systematically discussed. In addition, the challenges and opportunities of utilizing piezoelectric nanogenerators for self-powered bioelectronics and personalized healthcare are outlined in detail.

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

Functionalized Fiber-Based Strain Sensors: Pathway to Next-Generation Wearable Electronics

Wearable strain sensors are arousing increasing research interests in recent years on account of their potentials in motion detection, personal and public healthcare, future entertainment, man-machine interaction, artificial intelligence, and so forth. Much research has focused on fiber-based sensors due to the appealing performance of fibers, including processing flexibility, wearing comfortability, outstanding lifetime and serviceability, low-cost and large-scale capacity. Herein, we review the latest advances in functionalization and device fabrication of fiber materials toward applications in fiber-based wearable strain sensors. We describe the approaches for preparing conductive fibers such as spinning, surface modification, and structural transformation. We also introduce the fabrication and sensing mechanisms of state-of-the-art sensors and analyze their merits and demerits. The applications toward motion detection, healthcare, man-machine interaction, future entertainment, and multifunctional sensing are summarized with typical examples. We finally critically analyze tough challenges and future remarks of fiber-based strain sensors, aiming to implement them in real applications.

Soft Wearable Knee Brace with Embedded Sensors for Knee Motion Monitoring

E-textiles have shown great potential for development of soft sensors in applications such as rehabilitation and soft robotics. However, existing approaches require the textile sensors to be attached externally onto a substrate or the garment surface. This paper seeks to address the issue by embedding the sensor directly into the wearable using a computer numerical control (CNC) knitting machine. First, we proposed a design of the wearable knee brace. Next, we demonstrated the capability to knit a sensor with the stretchable surrounding fabric. Subsequently, we characterized the sensor and developed a model for the sensor's electromechanical property. Lastly, the fully knitted knee brace with embedded sensor is tested, by performing three different activities: a simple Flexion-extension exercise, walking, and jogging activity with a single test subject. Results show that the knitted knee brace sensor can track the subject's knee motion well, with a Spearman's coefficient (r_s) value of 0.87 when compared to the reference standard.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Monitoring Flexions and Torsions of the Trunk via Gyroscope-Calibrated Capacitive Elastomeric Wearable Sensors

Reliable, easy-to-use, and cost-effective wearable sensors are desirable for continuous measurements of flexions and torsions of the trunk, in order to assess risks and prevent injuries related to body movements in various contexts. Piezo-capacitive stretch sensors, made of dielectric elastomer membranes coated with compliant electrodes, have recently been described as a wearable, lightweight and low-cost technology to monitor body kinematics. An increase of their capacitance upon stretching can be used to sense angular movements. Here, we report on a wearable wireless system that, using two sensing stripes arranged on shoulder straps, can detect flexions and torsions of the trunk, following a simple and fast calibration with a conventional tri-axial gyroscope on board. The piezo-capacitive sensors avoid the errors that would be introduced by continuous sensing with a gyroscope, due to its typical drift. Relative to stereophotogrammetry (non-wearable standard system for motion capture), pure flexions and pure torsions could be detected by the piezo-capacitive sensors with a root mean square error of ~8° and ~12°, respectively, whilst for flexion and torsion components in compound movements, the error was ~13° and ~15°, respectively.

Recent advances in biomedical, biosensor and clinical measurement devices for use in humans and the potential application of these technologies for the study of physiology and disease in wild animals

The goal of achieving enhanced diagnosis and continuous monitoring of human health has led to a vibrant, dynamic and well-funded field of research in medical sensing and biosensor technologies. The field has many sub-disciplines which focus on different aspects of sensor science; engaging engineers, chemists, biochemists and clinicians, often in interdisciplinary teams. The trends which dominate include the efforts to develop effective point of care tests and implantable/wearable technologies for early diagnosis and continuous monitoring. This review will outline the current state of the art in a number of relevant fields, including device engineering, chemistry, nanoscience and biomolecular detection, and suggest how these advances might be employed to develop effective systems for measuring physiology, detecting infection and monitoring biomarker status in wild animals. Special consideration is also given to the emerging threat of antimicrobial resistance and in the light of the current SARS-CoV-2 outbreak, zoonotic infections. Both of these areas involve significant crossover between animal and human health and are therefore well placed to seed technological developments with applicability to both human and animal health and, more generally, the reviewed technologies have significant potential to find use in the measurement of physiology in wild animals. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.

Wearable Detection of Trunk Flexions: Capacitive Elastomeric Sensors Compared to Inertial Sensors

Continuous monitoring of flexions of the trunk via wearable sensors could help various types of workers to reduce risks associated with incorrect postures and movements. Stretchable piezo-capacitive elastomeric sensors based on dielectric elastomers have recently been described as a wearable, lightweight and cost-effective technology to monitor human kinematics. Their stretching causes an increase of capacitance, which can be related to angular movements. Here, we describe a wearable wireless system to detect flexions of the trunk, based on such sensors. In particular, we present: (i) a comparison of different calibration strategies for the capacitive sensors, using either an accelerometer or a gyroscope as an inclinometer; (ii) a comparison of the capacitive sensors’ performance with those of the accelerometer and gyroscope; to that aim, the three types of sensors were evaluated relative to stereophotogrammetry. Compared to the gyroscope, the capacitive sensors showed a higher accuracy. Compared to the accelerometer, their performance was lower when used as quasi-static inclinometers but also higher in case of highly dynamic accelerations. This makes the capacitive sensors attractive as a complementary, rather than alternative, technology to inertial sensors.

Nanomaterials-patterned flexible electrodes for wearable health monitoring: a review

ABSTRACT

Electrodes fabricated on a flexible substrate are a revolutionary development in wearable health monitoring due to their lightweight, breathability, comfort, and flexibility to conform to the curvilinear body shape. Different metallic thin-film and plastic-based substrates lack comfort for long-term monitoring applications. However, the insulating nature of different polymer, fiber, and textile substrates requires the deposition of conductive materials to render interactive functionality to substrates. Besides, the high porosity and flexibility of fiber and textile substrates pose a great challenge for the homogenous deposition of active materials. Printing is an excellent process to produce a flexible conductive textile electrode for wearable health monitoring applications due to its low cost and scalability. This article critically reviews the current state of the art of different textile architectures as a substrate for the deposition of conductive nanomaterials. Furthermore, recent progress in various printing processes of nanomaterials, challenges of printing nanomaterials on textiles, and their health monitoring applications are described systematically.

Progress in the Applications of Smart Piezoelectric Materials for Medical Devices

Smart piezoelectric materials are of great interest due to their unique properties. Piezoelectric materials can transform mechanical energy into electricity and vice versa. There are mono and polycrystals (piezoceramics), polymers, and composites in the group of piezoelectric materials. Recent years show progress in the applications of piezoelectric materials in biomedical devices due to their biocompatibility and biodegradability. Medical devices such as actuators and sensors, energy harvesting devices, and active scaffolds for neural tissue engineering are continually explored. Sensors and actuators from piezoelectric materials can convert flow rate, pressure, etc., to generate energy or consume it. This paper consists of using smart materials to design medical devices and provide a greater understanding of the piezoelectric effect in the medical industry presently. A greater understanding of piezoelectricity is necessary regarding the future development and industry challenges.

Closing the Wearable Gap-Part VII: A Retrospective of Stretch Sensor Tool Kit Development for Benchmark Testing

This paper presents a retrospective of the benchmark testing methodologies developed and accumulated into the stretch sensor tool kit (SSTK) by the research team during the Closing the Wearable Gap series of studies. The techniques developed to validate stretchable soft robotic sensors (SRS) as a means for collecting human kinetic and kinematic data at the foot-ankle complex and at the wrist are reviewed. Lessons learned from past experiments are addressed, as well as what comprises the current SSTK based on what the researchers learned over the course of multiple studies. Three core components of the SSTK are featured: (a) material testing tools, (b) data analysis software, and (c) data collection devices. Results collected indicate that the stretch sensors are a viable means for predicting kinematic data based on the most recent gait analysis study conducted by the researchers (average root mean squared error or RMSE = 3.63°). With the aid of SSTK defined in this study summary and shared with the academic community on GitHub, researchers will be able to undergo more rigorous validation methodologies of SRS validation. A summary of the current state of the SSTK is detailed and includes insight into upcoming experiments that will utilize more sophisticated techniques for fatigue testing and gait analysis, utilizing SRS as the data collection solution.