Robust and Environmentally Friendly MXene-Based Electronic Skin Enabling the Three Essential Functions of Natural Skin: Perception, Protection, and Thermoregulation.

The development of electronic skin (e-skin) emulating the human skin's three essential functions (perception, protection, and thermoregulation) has great potential for human-machine interfaces and intelligent robotics. However, existing studies mainly focus on perception. This study presents a novel, eco-friendly, mechanically robust e-skin replicating human skin's three essential functions. The e-skin is composed of Ti3C2Tx MXene, polypyrrole, and bacterial cellulose nanofibers, where the MXene nanoflakes form the matrix, the bacterial cellulose nanofibers act as the filler, and the polypyrrole serves as a conductive "cross-linker". This design allows customization of the electrical conductivity, microarchitecture, and mechanical properties, integrating sensing (perception), EMI shielding (protection), and thermal management (thermoregulation). The optimal e-skin can effectively sense various motions (including minuscule artery pulses), achieve an EMI shielding efficiency of 63.32 dB at 78 µm thickness, and regulate temperature up to 129 °C in 30 s at 2.4 V, demonstrating its potential for smart robotics in complex scenarios.

Wood Cell Wall Nanoengineering toward Anisotropic, Strong, and Flexible Cellulosic Hydrogel Sensors.

Achieving highly ionic conductive hydrogels from natural wood remains challenging owing to their insufficient surface area and low number of active sites on the cell wall. This study proposes a viable strategy to design a strong and anisotropic wood-based hydrogel through cell wall nanoengineering. By manipulating the microstructure of the wood cell wall, a flexible cellulosic hydrogel is achieved through Schiff base bonding via the polyacrylamide and cellulose molecular chains. This results in excellent flexibility and mechanical properties of the wood hydrogel with tensile strengths of 22.3 and 6.1 MPa in the longitudinal and transverse directions, respectively. Moreover, confining aqueous salt electrolytes within the porous structure gives anisotropic ionic conductivities (19.5 and 6.02 S/m in the longitudinal and transverse directions, respectively). The wood-based hydrogel sensor has a favorable sensitivity and a stable working performance at a low temperature of -25 °C in monitoring human motions, thereby demonstrating great potential applications in wearable sensor devices.

Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment.

BACKGROUND: Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. METHODS: We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation. RESULTS: The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. CONCLUSIONS: Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

Advances in pH Sensing: From Traditional Approaches to Next-Generation Sensors in Biological Contexts.

pH has been considered one of the paramount factors in bodily functions because most cellular tasks exclusively rely on precise pH values. In this context, the current techniques for pH sensing provide us with the futuristic insight to further design therapeutic and diagnostic tools. Thus, pH-sensing (electrochemically and optically) is rapidly evolving toward exciting new applications and expanding researchers' interests in many chemical contexts, especially in biomedical applications. The adaptation of cutting-edge technology is subsequently producing the modest form of these biosensors as wearable devices, which are providing us the opportunity to target the real-time collection of vital parameters, including pH for improved healthcare systems. The motif of this review is to provide insight into trending tech-based systems employed in real-time or in-vivo pH-responsive monitoring. Herein, we briefly go through the pH regulation in the human body to help the beginners and scientific community with quick background knowledge, recent advances in the field, and pH detection in real-time biological applications. In the end, we summarize our review by providing an outlook; challenges that need to be addressed, and prospective integration of various pH in vivo platforms with modern electronics that can open new avenues of cutting-edge techniques for disease diagnostics and prevention.

Objectively measured prolonged sleep is associated with plasma cytokines in older adults with mild cognitive impairment.

This study aimed to determine whether objective sleep time is associated with the concentrations of various plasma cytokines in older adults with mild cognitive impairment (MCI). In total, 118 adults with MCI (66 women; mean age: 75.7 years) participated in this prospective cohort study. All participants were required to wear a wristband sensor for 7.8 days, on average, every 3 months for 1 year and undergo measurement of 27 plasma cytokines using multiplex immunoassays. After adjusting for potential confounders, the associations of total sleep time with cytokine concentrations were assessed by multiple linear regression analysis. The total sleep time was significantly correlated with plasma interleukin (IL)-9 and macrophage inflammatory protein (MIP)-1ß levels (r = 0.239, p = 0.009, and r = 0.242, p = 0.008, respectively). Moreover, these associations remained significant after adjusting for covariates, including demographic characteristics, lifestyle-related diseases, and apolipoprotein E status (ß = 0.272, 95% confidence interval: 0.095-0.448, p = 0.003, and ß = 0.27, 95% confidence interval: 0.092-0.449, p = 0.003, respectively). Thus, this study is the first to demonstrate the association between objective prolonged sleep and higher plasma IL-9 and MIP-1ß levels in older adults with MCI.

Screen-printed wearable skin surface pH sensor for real-time monitoring of the buffering capacity of human skin.

This study demonstrated for the first time that skin surface pH can be monitored in real-time, using a screen-printed wearable pH sensor, to evaluate the buffering capacity of the human skin. The screen-printed pH sensor was composed of a polyaniline-based pH-sensitive electrode and a nitrocellulose membrane-based liquid junction type of Ag/AgCl reference electrode. This sensor showed a reliable and reversible potentiometric response to pH with long-term potential stability. Intermittent monitoring of the buffering capacity of skin surface pH demonstrated the reliability of the proposed wearable pH sensor, which was comparable to that of a commercially available flat-tip pH sensor. We found that contact of the wearable pH sensor with the subject's skin via aqueous electrolyte solutions was necessary for the sensor to continuously monitor the skin surface pH while sustaining the natural buffer capacity of the human skin surface.

A Fluorescent Hydrogel with AIE Emission for Dehydration-Visualizable Wearable Sensors.

Hydrogel-based wearable sensors eventually experience dehydration, which negatively impacts their function, leading to decreased sensitivity. Monitoring the real-time water retention rate and sensing performance of wearable flexible sensors without dismantling them remains a significant difficulty. In this study, a molecule having aggregation-induced emission (AIE) properties in an aqueous environment has been developed and produced, which can combine with anionic guar gum and acrylic acid to create an AIE hydrogel. Wearable sensing electronic devices have the capability to track motion signals at various joints of the human body. Additionally, they can effectively and visually monitor dehydration status during extended periods of operation. The fluorescence intensity of the hydrogel is primarily influenced by the level of aggregation of luminous monomers inside the network. This level of aggregation is predominantly governed by the hydrogel's water retention rate. Hence, the extended duration of hydrogel dehydration can be manifested through alterations in their fluorescence characteristics, which are employed for strain sensing. This approach enables users to assess the water retention of hydrogels with greater efficiency, eliminating the requirement for disassembling them from the completed electrical gadget. In summary, the use of AIE-based fluorescent hydrogels will advance the progress of intelligent wearable electronics.

Beyond timing and step counting in 360° turning-in-place assessment: a scoping review.

BACKGROUND: Turning in place is a challenging motor task and is used as a brief assessment test of lower limb function and dynamic balance. This review aims to examine how research of instrumented analysis of turning in place is implemented. In addition to reporting the studied population, we covered acquisition systems, turn detection methods, quantitative parameters, and how these parameters are computed. METHODS: Following the development of a rigorous search strategy, the Web of Science and Scopus were systematically searched for studies involving the use of turning-in-place. From the selected articles, the study population, types of instruments used, turn detection method, and how the turning-in-place characteristics were calculated. RESULTS: Twenty-one papers met the inclusion criteria. The subject groups involved in the reviewed studies included young, middle-aged, and older adults, stroke, multiple sclerosis and Parkinson's disease patients. Inertial measurement units (16 studies) and motion camera systems (5 studies) were employed for gathering measurement data, force platforms were rarely used (2 studies). Two studies used commercial software for turn detection, six studies referenced previously published algorithms, two studies developed a custom detector, and eight studies did not provide any details about the turn detection method. The most frequently used parameters were mean angular velocity (14 cases, 7 studies), turn duration (13 cases, 13 studies), peak angular velocity (8 cases, 8 studies), jerkiness (6 cases, 5 studies) and freezing-of-gait ratios (5 cases, 5 studies). Angular velocities were derived from sensors placed on the lower back (7 cases, 4 studies), trunk (4 cases, 2 studies), and shank (2 cases, 1 study). The rest (9 cases, 8 studies) did not report sensor placement. Calculation of the freezing-of-gait ratio was based on the acceleration of the lower limbs in all cases. Jerkiness computation employed acceleration in the medio-lateral (4 cases) and antero-posterior (1 case) direction. One study did not reported any details about jerkiness computation. CONCLUSION: This review identified the capabilities of turning-in-place assessment in identifying movement differences between the various subject groups. The results, based on data acquired by inertial measurement units across studies, are comparable. A more in-depth analysis of tests developed for gait, which has been adopted in turning-in-place, is needed to examine their validity and accuracy.

Self-powered freestanding multifunctional microneedle-based extended gate device for personalized health monitoring.

Online monitoring of prognostic biomarkers is critically important when diagnosing disorders and assessing individuals' health, especially for chronic and infectious diseases. Despite this, current diagnosis techniques are time-consuming, labor-intensive, and performed offline. In this context, developing wearable devices for continuous measurements of multiple biomarkers from body fluids has considerable advantages including availability, rapidity, convenience, and minimal invasiveness over the conventional painful and time-consuming tools. However, there is still a significant challenge in powering these devices over an extended period, especially for applications that require continuous and long-term health monitoring. Herein, a new freestanding, wearable, multifunctional microneedle-based extended gate field effect transistor biosensor is fabricated for online detection of multiple biomarkers from the interstitial fluid including sodium, calcium, potassium, and pH along with excellent electrical response, reversibility, and precision. In addition, a hybrid powering system of triboelectric nanogenerator and solar cell was developed for creating a freestanding, closed-loop platform for continuous charging of the device's battery and integrated with an Internet of Things technology to broadcast the measurements online, suggesting a stand-alone, stable multifunctional tool which paves the way for advanced practical personalized health monitoring and diagnosis.

Sarcopenia screening based on the assessment of gait with inertial measurement units: a systematic review.

BACKGROUND: Gait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia. METHODS: Six electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included. RESULTS: A total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia. CONCLUSIONS: IMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems.

Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study.

BACKGROUND: Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected. OBJECTIVE: This study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI. METHODS: This prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data. Data were collected from the Home Testing of Respiratory Illness (HTRI) study and FluStudy2020, both conducted between December 2019 and October 2020. The model was developed using the FluStudy2020 data and tested on the HTRI data. Analyses included participants in these studies with an at-home influenza diagnostic test result. Fitbit (Google LLC) devices were used to measure participants' steps, heart rate, and sleep parameters. Participants detailed their ILI symptoms, health care-seeking behaviors, and quality of life. Model performance was assessed by area under the curve (AUC), balanced accuracy, recall (sensitivity), specificity, precision (positive predictive value), negative predictive value, and weighted harmonic mean of precision and recall (F2) score. RESULTS: An influenza diagnostic test result was available for 953 and 925 participants in HTRI and FluStudy2020, respectively, of whom 848 (89%) and 840 (90.8%) had activity data. For the training and validation sets, the highest performing model was trained on the combined symptom and activity data (training AUC=0.77; validation AUC=0.74) versus symptom-only (training AUC=0.73; validation AUC=0.72) and activity-only (training AUC=0.68; validation AUC=0.65) data. For the FluStudy2020 test set, the performance of the model trained on combined symptom and activity data was closely aligned with that of the symptom-only model (combined symptom and activity test AUC=0.74; symptom-only test AUC=0.74). These results were validated using independent HTRI data (combined symptom and activity evaluation AUC=0.75; symptom-only evaluation AUC=0.74). The top features guiding influenza detection were cough; mean resting heart rate during main sleep; fever; total minutes in bed for the combined model; and fever, cough, and sore throat for the symptom-only model. CONCLUSIONS: Machine-learning algorithms had moderate accuracy in detecting influenza, suggesting that previous findings from research-grade sensors tested in highly controlled experimental settings may not easily translate to scalable commercial-grade sensors. In the future, more advanced wearable sensors may improve their performance in the early detection and discrimination of viral respiratory infections.

The effect of safety modifications on head kinematics experienced during common skills in women's artistic gymnastics.

The objective of this study was to evaluate the effect of skill modifications on head motion experienced during women's artistic gymnastics skills. Nine gymnasts (four beginner and five advanced) completed three trials of up to 24 skill progressions, each consisting of a skill and two progressive safety modifications. Gymnasts were instrumented with mouthpiece sensors embedded with an accelerometer and gyroscope collecting motion data at 200, 300, and 500 Hz during each skill performance. Peak-to-peak linear and rotational kinematics during contact phases and peak rotational kinematics during non-contact phases were computed. A mixed-effects model was used to compare differences in modification status nested within skill categories. Timer skills (i.e. drills that simulate performance of a gymnastics skill) resulted in the highest median ΔLA and ΔRA of all skill categories, and 132 skill performances exceeded 10 g ΔLA during a contact phase. Modifications were associated with significant reductions in head kinematics during contact phases of timers, floor skills, bar releases, and vault skills. Gymnasts can be exposed to direct and indirect head accelerations at magnitudes consistent with other youth contact sports, and common safety modifications may be effective at reducing head motion during contact and non-contact phases of gymnastics skills.

Advances in microneedles for transdermal diagnostics and sensing applications.

Microneedles, the miniaturized needles, which can pierce the skin with minimal invasiveness open up new possibilities for constructing personalized Point-of-Care (POC) diagnostic platforms. Recent advances in microneedle-based POC diagnostic systems, especially their successful implementation with wearable technologies, enable biochemical detection and physiological recordings in a user-friendly manner. This review presents an overview of the current advances in microneedle-based sensor devices, with emphasis on the biological basis of transdermal sensing, fabrication, and application of different types of microneedles, and a summary of microneedle devices based on various sensing strategies. It concludes with the challenges and future prospects of this swiftly growing field. The aim is to present a critical and thorough analysis of the state-of-the-art development of transdermal diagnostics and sensing devices based on microneedles, and to bridge the gap between microneedle technology and pragmatic applications.

Prototyping a wearable and stretchable graphene-on-PDMS sensor for strain detection on human body physiological and joint movements.

In the era of wearable electronic devices, which are quite popular nowadays, our research is focused on flexible as well as stretchable strain sensors, which are gaining humongous popularity because of recent advances in nanocomposites and their microstructures. Sensors that are stretchable and flexible based on graphene can be a prospective 'gateway' over the considerable biomedical speciality. The scientific community still faces a great problem in developing versatile and user-friendly graphene-based wearable strain sensors that satisfy the prerequisites of susceptible, ample range of sensing, and recoverable structural deformations. In this paper, we report the fabrication, development, detailed experimental analysis and electronic interfacing of a robust but simple PDMS/graphene/PDMS (PGP) multilayer strain sensor by drop casting conductive graphene ink as the sensing material onto a PDMS substrate. Electrochemical exfoliation of graphite leads to the production of abundant, fast and economical graphene. The PGP sensor selective to strain has a broad strain range of ⁓60%, with a maximum gauge factor of 850, detection of human physiological motion and personalized health monitoring, and the versatility to detect stretching with great sensitivity, recovery and repeatability. Additionally, recoverable structural deformation is demonstrated by the PGP strain sensors, and the sensor response is quite rapid for various ranges of frequency disturbances. The structural designation of graphene's overlap and crack structure is responsible for the resistance variations that give rise to the remarkable strain detection properties of this sensor. The comprehensive detection of resistance change resulting from different human body joints and physiological movements demonstrates that the PGP strain sensor is an effective choice for advanced biomedical and therapeutic electronic device utility.

Validity and Reliability of Wearable Sensors for Continuous Postoperative Vital Signs Monitoring in Patients Recovering from Trauma Surgery.

(1) Background: Wearable sensors support healthcare professionals in clinical decision-making by measuring vital parameters such as heart rate (HR), respiration rate (RR), and blood oxygenation saturation (SpO2). This study assessed the validity and reliability of two types of wearable sensors, based on electrocardiogram or photoplethysmography, compared with continuous monitoring of patients recovering from trauma surgery at the postanesthesia care unit. (2) Methods: In a prospective observational study, HR, RR, SpO2, and temperature of patients were simultaneously recorded with the VitalPatch and Radius PPG and compared with reference monitoring. Outcome measures were formulated as correlation coefficient for validity and mean difference with 95% limits of agreement for reliability for four random data pairs and 30-min pairs per vital sign per patient. (3) Results: Included were 60 patients. Correlation coefficients for VitalPatch were 0.57 to 0.85 for HR and 0.08 to 0.16 for RR, and for Radius PPG, correlation coefficients were 0.60 to 0.83 for HR, 0.20 to 0.12 for RR, and 0.57 to 0.61 for SpO2. Both sensors presented mean differences within the cutoff values of acceptable difference. (4) Conclusions: Moderate to strong correlations for HR and SpO2 were demonstrated. Although mean differences were within acceptable cutoff values for all vital signs, only limits of agreement for HR measured by electrocardiography were considered clinically acceptable.

Clinical Effect Analysis of Wearable Sensor Technology-Based Gait Function Analysis in Post-Transcranial Magnetic Stimulation Stroke Patients.

(1) Background: This study evaluates the effectiveness of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) in improving gait in post-stroke hemiplegic patients, using wearable sensor technology for objective gait analysis. (2) Methods: A total of 72 stroke patients were randomized into control, sham stimulation, and LF-rTMS groups, with all receiving standard medical treatment. The LF-rTMS group underwent stimulation on the unaffected hemisphere for 6 weeks. Key metrics including the Fugl-Meyer Assessment Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and gait parameters were measured before and after treatment. (3) Results: The LF-rTMS group showed significant improvements in the FMA-LE, BBS, MBI, and various gait parameters compared to the control and sham groups (p < 0.05). Specifically, the FMA-LE scores improved by an average of 5 points (from 15 ± 3 to 20 ± 2), the BBS scores increased by 8 points (from 35 ± 5 to 43 ± 4), the MBI scores rose by 10 points (from 50 ± 8 to 60 ± 7), and notable enhancements in gait parameters were observed: the gait cycle time was reduced from 2.05 ± 0.51 s to 1.02 ± 0.11 s, the stride length increased from 0.56 ± 0.04 m to 0.97 ± 0.08 m, and the walking speed improved from 35.95 ± 7.14 cm/s to 75.03 ± 11.36 cm/s (all p < 0.001). No adverse events were reported. The control and sham groups exhibited improvements but were not as significant. (4) Conclusions: LF-rTMS on the unaffected hemisphere significantly enhances lower-limb function, balance, and daily living activities in subacute stroke patients, with the gait parameters showing a notable improvement. Wearable sensor technology proves effective in providing detailed, objective gait analysis, offering valuable insights for clinical applications in stroke rehabilitation.

Design of Wearable Textile Electrodes for the Monitorization of Patients with Heart Failure.

Heart failure is a severe medical condition with an important worldwide incidence that occurs when the heart is unable to efficiently pump the patient's blood throughout the body. The monitoring of edema in the lower limbs is one of the most efficient ways to control the evolution of the condition. Impedance spectroscopy has been proposed as an efficient technique to monitor body volume in patients with heart failure. It is necessary to research new wearable devices for remote patient monitoring, which can be easily worn by patients in a continuous way. In this work, we design and implement new wearable textile electrodes for the monitoring of edema evolution in patients with heart failure. Impedance spectroscopy measurements were carried out in 5 healthy controls and 2 patients with heart failure using our wearable electrodes for 3 days. The results show the appropriateness of impedance spectroscopy and our wearable electrodes to monitor body volume evolution. Impedance spectroscopy is shown to be an efficient marker of the presence of edema in heart failure patients. Initial patient positive feedback was obtained for the use of the wearable device.

In-Shoe Sensor Measures of Loading Asymmetry during Gait as a Predictor of Frailty Development in Community-Dwelling Older Adults.

Clinical walk tests may not predict the development of frailty in healthy older adults. With advancements in wearable technology, it may be possible to predict the development of frailty using loading asymmetry parameters during clinical walk tests. This prospective cohort study aimed to test the hypothesis that increased limb loading asymmetry predicts frailty risk in community-living older adults. Sixty-three independently ambulant community-living adults aged ≥ 65 years were recruited, and forty-seven subjects completed the ten-month follow-up after baseline. Loading asymmetry index of net and regional (forefoot, midfoot, and rearfoot) plantar forces were collected using force sensing insoles during a 10 m walk test with their maximum speed. Development of frailty was defined if the participant progressed from baseline at least one grading group of frailty at the follow-up period using the Kihon Checklist. Fourteen subjects developed frailty during the follow-up period. Increased risk of frailty was associated with each 1% increase in loading asymmetry of net impulse (Odds ratio 1.153, 95%CI 1.001 to 1.329). Net impulse asymmetry significantly correlated with asymmetry of peak force in midfoot force. These results indicate the feasibility of measuring plantar forces of gait during clinical walking tests and underscore the potential of using load asymmetry as a tool to augment frailty risk assessment in community-dwelling older adults.

Mouthguard-Type Wearable Sensor for Monitoring Salivary Turbidity to Assess Oral Hygiene.

Salivary turbidity is a promising indicator for evaluating oral hygiene. This study proposed a wearable mouthguard-type sensor for continuous and unconstrained measurement of salivary turbidity. The sensor evaluated turbidity by measuring the light transmittance of saliva with an LED and a phototransistor sealed inside a double-layered mouthguard. The sensor was also embedded with a Bluetooth wireless module, enabling the wireless measurement of turbidity. The mouthguard materials (polyethylene terephthalate-glycol and ethylene-vinyl acetate) and the wavelength of the LED (405 nm) were experimentally determined to achieve high sensitivity in salivary turbidity measurement. The turbidity quantification characteristic of the proposed sensor was evaluated using a turbidity standard solution, and the sensor was capable of turbidity quantification over a wide dynamic range of 1-4000 FTU (formazine turbidity unit), including reported salivary turbidity (400-800 FTU). In vitro turbidity measurement using a saliva sample showed 553 FTU, which is equivalent to the same sample measured with a spectrophotometer (576 FTU). Moreover, in vivo experiments also showed results equivalent to that measured with a spectrophotometer, and wireless measurement of salivary turbidity was realized using the mouthguard-type sensor. Based on these results, the proposed mouthguard-type sensor has promising potential for the unconstrained continuous evaluation of oral hygiene.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.