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BACKGROUND: Low back pain (LBP) is a significant public health problem that can result in physical disability and financial burden for the individual and society. Physical therapy is effective for managing LBP and includes evaluation of posture and movement, interventions directed at modifying posture and movement, and prescription of exercises. However, physical therapists have limited tools for objective evaluation of low back posture and movement and monitoring of exercises, and this evaluation is limited to the time frame of a clinical encounter. There is a need for a valid tool that can be used to evaluate low back posture and movement and monitor exercises outside the clinic. To address this need, a fabric-based, wearable sensor, Motion Tape (MT), was developed and adapted for a low back use case. MT is a low-profile, disposable, self-adhesive, skin-strain sensor developed by spray coating piezoresistive graphene nanocomposites directly onto commercial kinesiology tape. OBJECTIVE: The objectives of this study were to (1) validate MT for measuring low back posture and movement and (2) assess the acceptability of MT for users. METHODS: A total of 10 participants without LBP were tested. A 3D optical motion capture system was used as a reference standard to measure low back kinematics. Retroreflective markers and a matrix of MTs were placed on the low back to measure kinematics (motion capture) and strain (MT) simultaneously during low back movements in the sagittal, frontal, and axial planes. Cross-correlation coefficients were calculated to evaluate the concurrent validity of MT strain in reference motion capture kinematics during each movement. The acceptability of MT was assessed using semistructured interviews conducted with each participant after laboratory testing. Interview data were analyzed using rapid qualitative analysis to identify themes and subthemes of user acceptability. RESULTS: Visual inspection of concurrent MT strain and kinematics of the low back indicated that MT can distinguish between different movement directions. Cross-correlation coefficients between MT strain and motion capture kinematics ranged from -0.915 to 0.983, and the strength of the correlations varied across MT placements and low back movement directions. Regarding user acceptability, participants expressed enthusiasm toward MT and believed that it would be helpful for remote interventions for LBP but provided suggestions for improvement. CONCLUSIONS: MT was able to distinguish between different low back movements, and most MTs demonstrated moderate to high correlation with motion capture kinematics. This preliminary laboratory validation of MT provides a basis for future device improvements, which will also involve testing in a free-living environment. Overall, users found MT acceptable for use in physical therapy for managing LBP.

Objective assessment of activity via accelerometry can provide valuable insights into dog health and welfare. Common activity metrics involve using acceleration cut-points to group data into intensity categories and reporting the time spent in each category. Lack of consistency and transparency in cut-point derivation makes it difficult to compare findings between studies. We present an alternative metric for use in dogs: the acceleration threshold (as a fraction of standard gravity, 1 g = 9.81 m/s2) above which the animals X most active minutes are accumulated (MXACC) over a 24-hour period. We report M2ACC, M30ACC and M60ACC data from a colony of healthy beagles (n = 6) aged 3-13 months. To ensure that reference values are applicable across a wider dog population, we incorporated labelled data from beagles and volunteer pet dogs (n = 16) of a variety of ages and breeds. The dogs normal activity patterns were recorded at 200 Hz for 24 hours using collar-based Axivity-AX3 accelerometers. We calculated acceleration vector magnitude and MXACC metrics. Using labelled data from both beagles and pet dogs, we characterize the range of acceleration outputs exhibited enabling meaningful interpretation of MXACC. These metrics will help standardize measurement of canine activity and serve as outcome measures for veterinary and translational research.

Objective Methods Results Significance This study aimed to assess whether population‐level patterns in seizure occurrence previously observed in self‐reported diaries, medical records, and electroencephalographic recordings were also present in tonic–clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration‐cleared wristband with an artificial intelligence detection algorithm and patient self‐reports. We also investigated the characteristics of patient interactions with wearable seizure alerts.We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates.Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90–1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08–26); (2) the linear relationship (slope = .79, R2 = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L‐relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long‐term cycles (31.1%).Key population‐level patterns in seizure occurrence are recapitulated in wrist‐worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long‐term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures. [ABSTRACT FROM AUTHOR]

This study introduces a compact wearable metasurface transmission line operating at 2.45 GHz, utilizing spoof surface plasmon polariton (SSPP). The design incorporates a periodic arrangement of hook unit cells and a tapering structure to enhance coupling efficiency between the microstrip feed line and the metasurface transmission line. For its implementation, a dielectric substrate composed entirely of cotton denim jeans is employed, along with e-textile conductive traces and a ground plane. Electromagnetic wave propagation along the transmission line is simulated, and the primary wave mode and associated propagation characteristics are analyzed. Both simulation and measurement results confirm the successful performance of the metasurface transmission line for on-body signal transmissions. [ABSTRACT FROM AUTHOR]

Addressing the loss of substrate conductivity after enduring numerous bending stands as a significant challenge for flexible electronics. The primary cause of reduced electrical conductivity in flexible polymer matrices is disorganization of conducting channels by mechanical stress generated during repeated stretching cycles. In this feature article, we extensively reviewed the strategies adopted to control structure formation of conducting networks by optimizing filler and matrix interactions at length scale. Studies, including our recent experiments, indicate that fractal formation of filler assemblies in polymer matrix determines the stability of conducting channels. A wide range of polymers and conducting fillers with different geometries and functional attributes have been analyzed to highlight the intra and intermolecular reinforcing effects to address the shear stresses originating in flexible substrates. Understanding non-bonded interactions between polymers and fillers may lead to the development of desired formulation of conducting inks to print on flexible surfaces for next-generation wearable electronics. [ABSTRACT FROM AUTHOR]

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system. [ABSTRACT FROM AUTHOR]

Background: Technology is poised to bridge the gap between demand for therapies to improve gait in people with Parkinson's and available resources. A wearable sensor, Heel2Toe™, a small device that attaches to the side of the shoe and gives a sound each time the person starts their step with a strong heel strike, has been developed and pre-tested by a team at McGill University. The objective of this study was to estimate feasibility and efficacy potential of the Heel2Toe™ sensor in changing walking capacity and gait pattern in people with Parkinson's. Methods: A pilot study was carried out involving 27 people with Parkinson's randomized 2:1 to train with the Heel2Toe[TM] sensor and or to train with recommendations from a gait-related workbook. Results: A total of 21 completed the 3-month evaluation, 14 trained with the Heel2Toe[TM] sensor, and 7 trained with the workbook. Thirteen of 14 people in the Heel2Toe group improved over measurement error on the primary outcome, the 6-Minute Walk Test, (mean change 66.4 m) and 0 of the 7 in the Workbook group (mean change − 19.4 m): 4 of 14 in the Heel2Toe group made reliable change and 0 of 7 in the Workbook group. Improvements in walking distance were accompanied by improvements in gait quality. Forty percent of participants in the intervention group were strongly satisfied with their technology experience and an additional 37% were satisfied. Conclusions: Despite some technological difficulties, feasibility and efficacy potential of the Heel2Toe sensor in improving gait in people with Parkinson's was supported. [ABSTRACT FROM AUTHOR]

Introduction: Wearable near-infrared spectroscopy (NIRS) can be used during dynamic exercise to reflect the balance of muscle oxygen delivery and uptake. This study describes the behaviour and reliability of postexercise reoxygenation with NIRS as a function of exercise intensity at four muscle sites during an incremental cycling test. We discuss physiological components of faster and slower reoxygenation kinetics in the context of sport science and clinical applications. We hypothesised that reoxygenation would be slower at higher intensity, and that locomotor muscles would be faster than accessory muscles. We quantified test-retest reliability and agreement for each site. Methods: Twenty-one trained cyclists performed two trials of an incremental cycling protocol with 5-min work stages and 1-min rest between stages. NIRS was recorded from the locomotor vastus lateralis and rectus femoris muscles, and accessory lumbar paraspinal and lateral deltoid muscles. Reoxygenation time course was analysed as the half-recovery time (HRT) from the end of work to half of the peak reoxygenation amplitude during rest. Coefficient of variability (CV) between participants, standard error of the measurement (SEM) within participants, and intraclass correlation coefficient (ICC) for test-retest reliability were evaluated at 50%, 75%, and 100% peak workloads. A linear mixed-effects model was used to compare differences between workloads and muscle sites. Results: HRT was slower with increasing workload in the VL, RF, and PS, but not DL. VL had the fastest reoxygenation (lowest HRT) across muscle sites at all workloads (HRT = 8, 12, 17 s at 50%, 75%, 100% workload, respectively). VL also had the greatest reliability and agreement. HRT was sequentially slower between muscle sites in the order of VL < RF < PS < DL, and reliability was lower than for the VL. Discussion: This study highlights the potential for using wearable NIRS on multiple muscle sites during exercise. Reoxygenation kinetics differ between local muscle sites with increasing intensity. Moderate-to-good reliability in the VL support its increasing use in sport science and clinical applications. Lower reliability in other muscle sites suggest they are not appropriate to be used alone, but may add information when combined to better reflect systemic intensity and fatigue during exercise at different intensities. [ABSTRACT FROM AUTHOR]

Introduction: The effect of displayed step count in smartwatches on the accuracy of daily step-count estimation and the potential underlying psychological factors have not been revealed. The study aimed for the following: (i) To investigate whether the counting and reporting of daily steps by a smartwatch increases the daily step-count estimation accuracy and (ii) to elucidating underlying psychological factors. Methods: A total of 34 healthy men and women participants wore smartwatches for 4 weeks. In week 1 (baseline), 3 (follow-up 1), and 8 (followup 2), the number of smartwatch displayed steps was blinded for each participant. In week 2 (Intervention), the number of steps was not blinded. During baseline and follow-ups 1 and 2, the participants were instructed to estimate their number of steps four times per day. During the 4-week washout period between follow-ups 1 and 2, no feedback was provided. The Body Awareness Questionnaire and the Body Responsiveness Questionnaire (BRQ) were used to elucidate the psychological facets of the assumed estimation accuracy. Results: The mean absolute percentage error between the participants' steps count estimations and measured steps counts were 29.49% (at baseline), 0.54% (intervention), 11.89% (follow-up 1), and 15.14% (follow-up 2), respectively. There was a significant effect between baseline and follow-up 1 [t (61.7) = 3.433, p < 0.001] but not between follow-up 1 and followup 2 [t (60.3) = -0.288, p = 0.774]. Only the BRQ subscale "Suppression of Bodily Sensations" appeared to be significant at the Baseline (p = 0.012; Bonferroni adjusted p = 0.048) as a factor influencing step-count estimation accuracy. Conclusion: The counting and reporting of daily steps with a smartwatch allows improving the subjective estimation accuracy of daily step counts, with a stabilizing effect for at least 6 weeks. Especially individuals who tend to suppress their bodily sensations are less accurate in their daily step-count estimation before the intervention. [ABSTRACT FROM AUTHOR]

Electrodermal activity (EDA) is a widely used psychophysiological measurement in laboratory-based studies. In recent times, these measurements have seen a transfer from the laboratory to wearable devices due to the simplicity of EDA measurement as well as modern electronics. However, proper conditions for EDA measurement are recommended once wearable devices are used, and the ambient conditions may influence such measurements. It is not completely known how different types of ambient noise impact EDA measurement and how this translates to wearable EDA measurement. Therefore, this study explored the effects of various noise disturbances on the generation of EDA responses using a system for the simultaneous recording of all measures of EDA, i.e., skin conductance responses (SCRs), skin susceptance responses (SSRs), and skin potential responses (SPRs), at the same skin site. The SCRs, SSRs, and SPRs due to five types of noise stimuli at different sound pressure levels (70, 75, 80, 85, and 90 dB) were measured from 40 participants. The obtained results showed that EDA responses were generated at all levels and that the EDA response magnitudes were significantly (p < 0.001) influenced by the increasing noise levels. Different types of environmental noise may elicit EDA responses and influence wearable recordings outside the laboratory, where such noises are more likely than in standardized laboratory tests. Depending on the application, it is recommended to prevent these types of unwanted variation, presenting a challenge for the quality of wearable EDA measurement in real-world conditions. Future developments to shorten the quality gap between standardized laboratory-based and wearable EDA measurements may include adding microphone sensors and algorithms to detect, classify, and process the noise-related EDA. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder impacting women's health and quality of life. This scoping review explores the use of the Internet of Things (IoT) in PCOS management. Results were grouped into six domains of the IoT: mobile apps, social media, wearables, machine learning, websites, and phone-based. A further domain was created to capture participants' perspectives on using the IoT in PCOS management. Mobile apps appear to be useful for menstrual cycle tracking, symptom recording, and education. Despite concerns regarding the quality and reliability of social media content, these platforms may play an important role in disseminating PCOS-related information. Wearables facilitate detailed symptom monitoring and improve communication with healthcare providers. Machine learning algorithms show promising results in PCOS diagnosis accuracy, risk prediction, and app development. Although abundant, PCOS-related content on websites may lack quality and cultural considerations. While patients express concerns about online misinformation, they consider online forums valuable for peer connection. Using text messages and phone calls to provide feedback and support to PCOS patients may help them improve lifestyle behaviors and self-management skills. Advancing evidence-based, culturally sensitive, and accessible IoT solutions can enhance their potential to transform PCOS care, address misinformation, and empower women to better manage their symptoms. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) have emerged as promising portable and sustainable energy sources in daily life, harvesting energy from human motion, water, and wind. However, they still face limitations in aspects such as contact area, deformability, wettability, and manufacturing method. Here, a wearable TENG incorporating an anisotropic domino structure based on a fluorinated elastomer blend is presented. Because of its thin, elongated structure with broad sides, the TENG achieves substantially larger contact areas and high bendability. Introducing a fluorinated elastomer into the polydimethylsiloxane matrix via a simple blending process not only enhances the triboelectric performance but also reduces surface energy and improves the stretchability of elastomers. The anisotropic arrangement of dominoes, in synergy with the fluorinated elastomer, mimics the surface physicochemical properties of natural rice leaves, resulting in anisotropic superhydrophobic wetting behavior with a self‐cleaning effect and controlled directional water flow for efficient water energy harvesting. Therefore, the TENG functions as an energy‐harvesting leaf that captures energy from wind and water droplets, as well as a wearable energy‐harvesting wristband that generates power from human motions such as touching, shaking, and hand washing. [ABSTRACT FROM AUTHOR]

Personal protective equipment (PPE) has been universally recognized for its role in protecting workers from injuries and illnesses. Smart PPE integrates Internet of Things (IoT) technologies to enable continuous monitoring of workers and their surrounding environment, preventing undesirable events, facilitating rapid emergency response, and informing rescuers of potential hazards. This work presents a smart PPE system with a sensor node architecture designed to monitor workers and their surroundings. The sensor node is equipped with various sensors and communication capabilities, enabling the monitoring of specific gases (VOC, CO2, CO, O2), particulate matter (PM), temperature, humidity, positional information, audio signals, and body gestures. The system utilizes artificial intelligence algorithms to recognize patterns in worker activity that could lead to risky situations. Gas tests were conducted in a special chamber, positioning capabilities were tested indoors and outdoors, and the remaining sensors were tested in a simulated laboratory environment. This paper presents the sensor node architecture and the results of tests on target risky scenarios. The sensor node performed well in all situations, correctly signaling all cases that could lead to risky situations. [ABSTRACT FROM AUTHOR]

The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various classifiers. The results were categorized into three groups to discern differences between general hand movements and no movement; specific movements and no movement; and specific movements and other specific movements (between five different finger movements and no movement). Testing was conducted on the sampling frequencies, trials, number of electrodes, algorithms, and their parameters. The preferred algorithm was determined to be the FastICACorr algorithm with 20 components. The optimal sampling frequency is 1 kHz to avoid adding excessive noise and to ensure efficient handling. Twenty trials are deemed sufficient for training, and the number of electrodes will range from one to three, depending on the wearable EEG's ability to handle the algorithm parameters with good performance. [ABSTRACT FROM AUTHOR]

Flexible inorganic electroluminescent devices exhibit high luminous efficiency, rapid response, low production costs and stable luminescence. These devices can be employed for flexible displays, intelligent sensing and other functions, making them promising candidates for applications in smartphones, electronic skin and smart wearables. Textile materials, with their flexibility, wearability, and well-established processing techniques, serve as excellent substrates for these flexible electroluminescent devices. In recent years, the structural design of flexible inorganic electroluminescent devices has matured significantly, driving research and development efforts across a wide range of applications, particularly in wearable electronics, displays and bionic robots. Electroluminescence primarily arises when a current passes through a substance, causing it to emit light under a strong electric field. Two main types of electroluminescence exist: injection electroluminescence and intrinsic electroluminescence. In the case of injection electroluminescence (also known as low-field electroluminescence), electrons and holes are directly injected from the electrodes into the crystal. When these charge carriers recombine within the crystal, excess energy is released in the form of light. Intrinsic electroluminescence (or high-field electroluminescence) is akin to an in vivo luminescence effect. In inorganic electroluminescent materials, electrons gain energy and collide with and excite luminescent centers under high electric fields. Consequently, the excited electrons in the luminescent centers transition to lower energy states, emitting light. Currently, the most widely used inorganic electroluminescent materials included zinc sulfide-based compounds and perovskite materials. Zinc sulfide exhibits excellent thermal stability, mechanical strength and high carrier mobility, making it a common choice for thin-film transistors and other electronic devices. Doping zinc sulfide with metal elements (such as copper, aluminum or manganese) allows for the creation of different variants, such as zinc sulfide: copper (green) and zinc sulfide: manganese (orange), enabling tunable emission colors by adjusting the frequency of the AC electric field. However, localized electric field breakdown remains a challenge, leading to device damage. Perovskite materials offer adjustable band gaps, narrow half-peak widths, high carrier mobility and efficient fluorescence. When used as the light- emitting layer in perovskite light-emitting diodes, they exhibit high color purity, brightness and a wide color gamut. By modifying the composition of halide ions and adjusting cation proportions, the optical and electrical properties of perovskite can be tailored, showing promise in lighting and display applications. Nevertheless, challenges related to stability, mechanical properties, toxicity, and large-scale manufacturing persist for flexible perovskite electroluminescent materials As demand grows for convenient solutions, electroluminescent devices are expected to serve additional functions, including health monitoring, electronic communication and aerospace applications. Among these, textile displays play a crucial role in transmitting information anytime and anywhere. Wearable electronic products integrated into shoes, clothing and watches enhance human convenience. The acquisition and display of textile electroluminescent devices rely on luminous fabrics and fibers, utilizing printing, coating, lamination and fiber weaving technologies. These methods impose stringent requirements on brightness and device lifespan, necessitating improved device structures and combination modes to achieve stable and durable performance in textile applications. At present, large-scale continuous production of flexible inorganic electroluminescent devices remains a challenge. As new display applications emerge, stricter demands are placed on flexible electroluminescent devices. In flexible inorganic electroluminescent devices, the development and application of electroluminescent devices need to be combined with other disciplines to develop a multifunctional device to meet people's needs. [ABSTRACT FROM AUTHOR]

Summary: Several stress‐related mental disorders are characterised by disturbed sleep, but objective sleep biomarkers are not routinely examined in psychiatric patients. We examined the use of wearable‐based sleep biomarkers in a psychiatric sample with headband electroencephalography (EEG) including pulse photoplethysmography (PPG), with an additional focus on microstructural elements as especially the shift from low to high frequencies appears relevant for several stress‐related mental disorders. We analysed 371 nights of sufficient quality from 83 healthy participants and those with a confirmed stress‐related mental disorder (anxiety‐affective spectrum). The median value of macrostructural, microstructural (spectral slope fitting), and heart rate variables was calculated across nights and analysed at the individual level (N = 83). The headbands were accepted well by patients and the data quality was sufficient for most nights. The macrostructural analyses revealed trends for significance regarding sleep continuity but not sleep depth variables. The spectral analyses yielded no between‐group differences except for a group × age interaction, with the normal age‐related decline in the low versus high frequency power ratio flattening in the patient group. The PPG analyses showed that the mean heart rate was higher in the patient group in pre‐sleep epochs, a difference that reduced during sleep and dissipated at wakefulness. Wearable devices that record EEG and/or PPG could be used over multiple nights to assess sleep fragmentation, spectral balance, and sympathetic drive throughout the sleep–wake cycle in patients with stress‐related mental disorders and healthy controls, although macrostructural and spectral markers did not differ between the two groups. [ABSTRACT FROM AUTHOR]

The significant impact of stress on health necessitates accurate assessment methods, where traditional questionnaires lack reliability and objectivity. Current advancements like wearables with electrocardiogram (ECG) and galvanic skin response (GSR) sensors face accuracy and artifact challenges. Molecular biosensors detecting cortisol, a critical stress hormone, present a promising solution. However, existing cortisol assays, requiring saliva, urine, or blood, are complex, expensive, and unsuitable for continuous monitoring. Our study introduces a passive, molecularly imprinted polymer-radio-frequency (MIP-RF) wearable sensing system for real-time, non-invasive sweat cortisol assessment. This system is wireless, flexible, battery-free, reusable, environmentally stable, and designed for long-term monitoring, using an inductance-capacitance transducer. The transducer translates cortisol concentrations into resonant frequency shifts with high sensitivity (∼ 160 kHz/(log (µM))) across a physiological range of 0.025–1 µM. Integrated with near-field communication (NFC) for wireless and battery-free operation, and three-dimensional (3D)-printed microfluidic channel for in-situ sweat collection, it enables daily activity cortisol level tracking. Validation of cortisol circadian rhythm through morning and evening measurements demonstrates its effectiveness in tracking and monitoring sweat cortisol levels. A 28-day stability test and the use of cost-effective 3D nanomaterials printing enhance its economic viability and reusability. This innovation paves the way for a new era in realistic, on-demand health monitoring outside the laboratory, leveraging wearable technology for molecular stress biomarker detection. [ABSTRACT FROM AUTHOR]

Polyvinyl Alcohol (PVA) has been used in various applications, including the medical health industry and electronics. It is a synthetic polymer with advantages such as being transparent, flexible, biocompatible, biodegradable, and a simpler synthesis process. These advantages make PVA a very promising material for human wearable antennae. In this research, the bending effect of an antenna using a PVA substrate is studied to analyze its durability in the wearable application. Firstly, the thin film substrate synthesis is performed using PVA 2488 with the measured average dielectric constant and tangent loss of 1.24 and 0.066, respectively, across S-Band frequency. Later, a 5G antenna is designed and fabricated using the PVA substrate. Finally, the bending effects of the fabricated antenna are measured at different bending radii. Four different antenna-bending radii are selected to represent different curvatures of human body parts. Results show that bending does not have a significant effect on the reflection coefficient of the antenna, where the frequency shifts from 2.2% up to 7.4% only for all bending conditions. Hence, in that aspect of finding, the PVA thin film is a potential candidate for flexible and wearable antenna material in various human body parts in biomedical applications. [ABSTRACT FROM AUTHOR]

Smart textile-based wearable sweat sensors have recently received a lot of attention due to their potential for use in personal medical monitoring. They have a variety of desirable qualities, including low cost, easy implementation, stretchability, flexibility, and light weight. Wearable sweat sensors are a potential approach for personalized medical devices because of these features. Moreover, real-time textile-based sweat sensors can easily monitor health by analyzing the sweat produced by the human body. We reviewed the most recent advancements in wearable sweat sensors from the fabrication, materials, and disease detection and monitoring perspectives. To integrate real-time biosensors with electronics and introduce advancements to the field of wearable technology, key chemical constituents of sweat, sweat collection technologies, and concerns of textile substrates are elaborated. Perspectives for building wearable biosensing systems based on sweat are reviewed, as well as the methods and difficulties involved in enhancing wearable sweat-sensing performance. [ABSTRACT FROM AUTHOR]

The utilisation of curved structural components is prevalent, despite their proneness to degradation over time. This research introduces a wearable and flexible electromagnetic non-destructive testing instrument that is furnished with an eddy current sensor, multi-channel excitation and acquisition circuits, amplifiers, as well as signal processing circuits. A unique design for the flexible Eddy Current Testing (ECT) sensor is suggested to ensure its suitability for diverse curved surfaces. By combining the sensor with gloves, it facilitates wearable inspection. Leveraging the self-developed instrument system, this sensor can detect defects with a minimum depth of 0.1 mm while also being capable of identifying crack orientation accurately. Furthermore, it quantitatively determines the depth of defects on curved surfaces with high signal-to-noise ratios. The instrument system exhibits high portability, flexibility, and efficiency, rendering it readily adaptable for various applications. [ABSTRACT FROM AUTHOR]

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software‐based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low‐power hardware. Organic polymer‐based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software‐based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in‐body computation of glucose prediction is investigated. [ABSTRACT FROM AUTHOR]

High‐sensitivity wearable radiation detectors are essential for personnel protection in radiation environments such as defense, nuclear facilities, and medical fields. Traditional detectors using bulk crystals lack flexibility, and emerging perovskite films suffer from lead toxicity and poor charge transport. Herein, lead‐free photoferroelectric hybrid metal halide perovskite flexible membranes for wearable detectors are presented, offering superior X‐ray response with sensitivities up to 7872 ± 517 µC Gyair−1 cm−2 at 50 V bias and 394 ± 67 µC Gyair−1 cm−2 in a self‐driven mode, detection limit of lower than 77 nGyair s−1, and excellent imaging capabilities. This exceptional performance is attributed to the spontaneous polarization that promotes efficient charge transport. Additionally, they show remarkable radiation stability, long‐term air stability, mechanical fatigue resistance, and water stability. They also exhibit efficient energy response under the Compton effect and meet the angle response requirements of the International Electrotechnical Commission standard for direct‐reading personal dose equivalent meters, paving the way for their integration into flexible, wearable dosimeters. These advancements have the potential to drive the realization of the next generation of flexible wearable radiation detectors. [ABSTRACT FROM AUTHOR]

Wearable thermoelectric generators have great potential to provide power for smart electronic wearable devices and miniature sensors by harnessing the temperature difference between the human body and the environment. However, the Thomson effect, the Joule effect, and heat conduction can cause a decrease in the temperature difference across the thermoelectric generator during operation. In this paper, phase change materials (PCMs) were employed as the heat sink for the thermoelectric generator, and the COMSOL software 6.1 was utilized to simulate and optimize the power generation processes within the heat sink. The results indicated that with a PCM height of 40 mm, phase transition temperature of 293 K, latent heat of 200 kJ/kg, phase transition temperature interval of 5 K, thermal conductivity of 50 W/(m·K), isobaric heat capacity of 2000 J/(Kg·K), density of 1000 kg/m3, and convective heat transfer coefficient of 10 W/(m·K), the device can maintain a temperature difference of 18–10 K for 1930 s when the thermoelectric leg height is 1.6 mm, and 3760 s when the thermoelectric leg height is 2.7 mm. These results demonstrate the correlation between the device's output performance and the dimensions and performance parameters of the PCM heat sink, thereby validating the feasibility of employing the PCM heat sink and the necessity for systematic investigations. [ABSTRACT FROM AUTHOR]

Sleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. In this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. In the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. The results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables. • Proposal and evaluation of a smartwatch-based solution for sleep staging. • Experimental evaluation on a large and diverse dataset of 1522 sleep recordings. • Solution based on neural networks trained on accelerometer and PPG sensor data. • 71.6 % of balanced accuracy for classification of 4 sleep stages compared with PSG. • Low correlation of performance with demographic variables and sleep apnea disorder. [ABSTRACT FROM AUTHOR]

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Candida albicans is an opportunistic fungus that becomes pathogenic and problematic under certain biological conditions. C. albicans may cause painful and uncomfortable symptoms, as well as deaths in immunocompromised patients. Therefore, early detection of C. albicans is essential. However, conventional detection methods are costly, slow, and inaccessible to women in remote or developing areas. To address these concerns, we have developed a wearable and discrete naked-eye detectable colorimetric platform for C. albicans detection. With some modification, this platform is designed to be directly adhered to existing feminine hygiene pads. Our platform is rapid, inexpensive, user-friendly, and disposable and only requires three steps: (i) the addition of vaginal fluid onto sample pads; (ii) the addition of gold nanoparticle gel and running buffer, and (iii) naked eye detection. Our platform is underpinned by selective thiolated aptamer-based recognition of 1,3-β-D glucan molecules—a hallmark of C. albicans cell walls. In the absence of C. albicans, wearable sample pads turn bright pink. In the presence of C. albicans, the wearable pads turn dark blue due to significant nanoparticle target-induced aggregation. We demonstrate naked-eye colorimetric detection of 4.4 × 106 C. albicans cells per ml and nanoparticle stability over a pH range of 3.0–8.0. We believe that this proof-of-concept platform has the potential to have a significant impact on women's health globally. [ABSTRACT FROM AUTHOR]

Hydrogels are gaining popularity for use in wearable electronics owing to their inherent biomimetic characteristics, flexible physicochemical properties, and excellent biocompatibility. Among various hydrogels, conductive polymer-based hydrogels (CP HGs) have emerged as excellent candidates for future wearable sensor designs. These hydrogels can attain desired properties through various tuning strategies extending from molecular design to microstructural configuration. However, significant challenges remain, such as the limited strain-sensing range, significant hysteresis of sensing signals, dehydration-induced functional failure, and surface/interfacial malfunction during manufacturing/processing. This review summarizes the recent developments in polymer-hydrogel-based wearable electrochemical biosensors over the past five years. Initially serving as carriers for biomolecules, polymer-hydrogel-based sensors have advanced to encompass a wider range of applications, including the development of non-enzymatic sensors facilitated by the integration of nanomaterials such as metals, metal oxides, and carbon-based materials. Beyond the numerous existing reports that primarily focus on biomolecule detection, we extend the scope to include the fabrication of nanocomposite conductive polymer hydrogels and explore their varied conductivity mechanisms in electrochemical sensing applications. This comprehensive evaluation is instrumental in determining the readiness of these polymer hydrogels for point-of-care translation and state-of-the-art applications in wearable electrochemical sensing technology. [ABSTRACT FROM AUTHOR]

Introduction: florio® HAEMO is a hemophilia treatment monitoring application (app) offering activity tracking and wearable device connectivity. Its use might support everyday activities for people with hemophilia. The aim of this study was to evaluate user satisfaction, long-term usage and the impact on data entry when pairing a wearable with a hemophilia monitoring app. Methods: This is a follow-up of a two-part user survey conducted in Central Europe. People with hemophilia and parents/caregivers of children with hemophilia using florio HAEMO and who completed part one were invited to complete a second online questionnaire at least 4 months later. Results: Fifty participants (83.3%) who completed part one of the survey continued to use the florio HAEMO app and completed part two. Of 14 participants who chose to use the app with a wearable, more than half (57.1%) were aged between 13 and 25 years. Overall, the results demonstrated that florio HAEMO is very easy or rather easy to use, especially for individuals pairing the app with a wearable. Most people using a wearable indicated that florio HAEMO was very or rather important in bringing certainty to daily activities (85.7%). Notably, 14 of 36 (38.9%) non-wearable users indicated that they would prefer to pair the app with a wearable in the future. Conclusions: Adherence to the florio HAEMO app is maintained over an extended period of use. Pairing the app with a wearable might enable easier access to app features, increase data entry motivation and provide more certainty about daily activities for people with hemophilia. [ABSTRACT FROM AUTHOR]

According to related research, different body temperatures, heart rates, and locomotor behaviors of small-tailed cold goats can represent the physical condition of the goats themselves and are used as direct evidence for evaluating the physical health status of small-tailed cold goats. In this paper, we designed and tested a system for predicting the health status of small-tailed cold sheep based on wearable information monitoring technology. To test the system, sheep wearable devices were worn on 36 small-tailed cold sheep of different ages and inconsistent health conditions at different time points from May to October. A SLBAS-BP neural network model for predicting the health condition of small-tailed cold sheep was established using the collected and processed data, which overcame the problem that the traditional gradient descent method in the BP neural network is prone to fall into local optimization leading to insufficient prediction ability. The correct prediction rates of the improved BP neural network for the four health conditions of healthy, sub-healthy, fever, and disease were 98.4%, 94.5%, 90.4%, and 98.7%, respectively, and the average correct prediction rate of the four conditions was 5.8% higher than that before the improvement, reaching 95.2%. [ABSTRACT FROM AUTHOR]

Abstract Background Technology is poised to bridge the gap between demand for therapies to improve gait in people with Parkinson’s and available resources. A wearable sensor, Heel2Toe™, a small device that attaches to the side of the shoe and gives a sound each time the person starts their step with a strong heel strike, has been developed and pre-tested by a team at McGill University. The objective of this study was to estimate feasibility and efficacy potential of the Heel2Toe™ sensor in changing walking capacity and gait pattern in people with Parkinson’s. Methods A pilot study was carried out involving 27 people with Parkinson’s randomized 2:1 to train with the Heel2Toe[TM] sensor and or to train with recommendations from a gait-related workbook. Results A total of 21 completed the 3-month evaluation, 14 trained with the Heel2Toe[TM] sensor, and 7 trained with the workbook. Thirteen of 14 people in the Heel2Toe group improved over measurement error on the primary outcome, the 6-Minute Walk Test, (mean change 66.4 m) and 0 of the 7 in the Workbook group (mean change − 19.4 m): 4 of 14 in the Heel2Toe group made reliable change and 0 of 7 in the Workbook group. Improvements in walking distance were accompanied by improvements in gait quality. Forty percent of participants in the intervention group were strongly satisfied with their technology experience and an additional 37% were satisfied. Conclusions Despite some technological difficulties, feasibility and efficacy potential of the Heel2Toe sensor in improving gait in people with Parkinson’s was supported.

Dominic Jaworski,1,2 Edward J Park1,2 1Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, V3T 0A3, Canada; 2WearTech Labs, Simon Fraser University, Surrey, BC, V3V 0C6, CanadaCorrespondence: Edward J Park; Dominic Jaworski, Surrey Centre 2 Building, 9639 - 137A Street, Unit 206, Surrey, BC, V3V 0C6, Canada, Email ed_park@sfu.ca; djaworsk@sfu.caPurpose: Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics.Patients and Methods: Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable.Results: Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages.Conclusion: Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user’s or clinic’s bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.Keywords: ballistocardiogram, wearable, heart rate variability, nonstationary signals

Field-based tibial acceleration measurements are increasingly common but sampling frequencies vary between accelerometers. The purpose of this study was to determine the minimum sampling frequency needed for reliable and accurate measurement of peak axial and resultant tibial acceleration during running in the field. Tibial acceleration was measured at 7161 Hz in 19 healthy runners on concrete and grass. Acceleration data were down sampled to approximate previously used sampling frequencies. Peak axial and resultant tibial acceleration were calculated for each sampling frequency. The within-session reliability and accuracy of peak axial and resultant tibial accelerations were evaluated using intraclass correlation coefficients, mean differences, and 95% limits of agreements. Intraclass correlation coefficients greater than.9 indicated excellent within-session reliability for both peak axial and resultant tibial acceleration measured while running on concrete and grass. Peak axial and resultant tibial accelerations were 0.5 to 1.4 g lower and minimal detectable differences were up to 0.6 g higher at 102 Hz compared with higher sampling frequencies. We recommend a minimum sampling frequency of 199 Hz for accurate and reliable measurements of peak axial and resultant tibial acceleration in the field. [ABSTRACT FROM AUTHOR]