Sensors

BackgroundParkinsons Disease (PD) is often reduced to its most visible motor symptoms, yet it is a systemic neurodegenerative disorder with a highly heterogeneous presentation. While cardinal motor signs such as bradykinesia, rigidity, and tremor arise from the loss of dopaminergic neurons in the substantia nigra, they typically manifest only after substantial neurodegeneration (approximately 50-70% loss) has already occurred, inevitably leading to delayed detection [1] PD significantly impacts non-motor and fine-motor domains as well that are frequently overlooked. Research indicates that hypokinetic dysarthria (voice impairment) affects approximately 89% of PD patients, often as an early prodromal sign [18]. Similarly, micrographia (handwriting impairment) is observed in up to 63% of cases, while non-motor symptoms such as hyposmia (loss of smell) and REM sleep behavior disorder occur in over 70% and 40% of patients, respectively--often years before clinical diagnosis [19, 20]. Consequently, diagnostic systems that rely on a single modality fail to capture this complexity, leading to missed detections in patients whose primary symptoms fall outside that specific domain. To address this, we propose a holistic, multimodal AI framework that explicitly targets these diverse pathological vectors--Voice, Gait, Handwriting, and Non-Motor Symptoms--to ensure robust and early detection across the full spectrum of the disease. MethodsWe propose a modular multimodal AI framework that integrates five complementary inputs: voice recordings, signals captured with the help of a smart pen during drawing spiral/meander, hand-drawn spiral/meander images, wearable sensor-driven gait data, and MDS-UPDRS questionnaire-derived symptom scores. Each modality undergoes an independent preprocessing and specialized modeling pipeline. Outputs from these specialized models are combined using a weighted aggregation engine, which allows for customizable contribution of each modality to the final classification. ResultsPreliminary experiments show that the unimodal pipelines achieved high accuracy, with the Random Forest (Voice) achieving 89%, XGBoost (Drawing Signal) up to 93%, and ResNet-18 (Drawing Image) up to 92%. Incorporating the Transformer model for gait data, which achieved 86% accuracy, significantly boosts the detection of subtle motor deficits. The proposed approach is expected to improve the overall diagnostic sensitivity and specificity relative to any unimodal baseline, offering transparent score breakdowns for clinical use. ConclusionThis study validates a comprehensive, multimodal Machine Learning framework designed to capture the holistic nature of clinical Parkinsons Disease. Our results indicate that fine motor control--analyzed through both dynamic handwriting signals and static imagery--serves as a highly discriminative biomarker, offering superior detection of subtle kinematic tremors. Furthermore, the integration of vocal analysis and spatiotemporal gait modeling ensures that the system captures the full spectrum of pathology, distinguishing between phonatory deficits and gross motor irregularities. By synthesizing these diverse clinical indicators, the proposed architecture overcomes the sensitivity limitations of single-modality systems, establishing a robust, non-invasive foundation for objective early screening and longitudinal patient monitoring in real-world settings.

This article introduces the Emteqs OCOsense smart glasses equipped with a novel non-contact OCO sensor technology for measuring facial muscle activation and expressions based on high resolution tracking of skin movement. We demonstrate that the OCO sensor technology based on optomyography is a sensitive and accurate approach for assessing skin movement in 3 dimensions, providing a means for measuring the facial expressions used to assess emotional valence such as smile, frown, and eyebrow raise. We propose that glasses-based optomyography sensing has the potential to herald a paradigm shift in real-world facial expression monitoring, thus enabling real-time emotional analytics with healthcare and research applications.

Pressure injuries are largely preventable, yet they affect one in four Canadians across all healthcare settings. A key best practice to prevent and treat pressure injuries is to minimize prolonged tissue deformation by ensuring at-risk individuals are repositioned regularly (typically every 2 hours). However, adherence to repositioning is poor in clinical settings and expected to be even worse in homecare settings. Our team has designed a position detection system for home use that uses machine learning approaches to predict a patients position in bed using data from load cells under the bed legs. The system predicts the patients position as one of three position categories: left-side lying, right-side lying, or supine. The objectives of this project were to: i) determine if measuring ground truth patient position with an inertial measurement unit can improve our system accuracy (predicting left-side lying, right-side lying, or supine) ii) to determine the range of transverse pelvis angles (TPA) that fully offloaded each of the great trochanters and sacrum and iii) evaluate the potential benefit of being able to predict the individuals position with higher precision (classifying position into more than three categories) by taking into account a potential drop in prediction accuracy as well as the range of TPA for which the greater trochanters and sacrum were fully offloaded. Data from 18 participants was combined with previous data sets to train and evaluate classifiers to predict the participants TPA using four different position bin sizes ([~]70{degrees}, 45{degrees}, [~]30{degrees}, and 15{degrees}) and the effects of increasing precision on performance, where patients are left side-lying at -90{degrees}, right side-lying at 90{degrees} and supine at 0{degrees}). A leave-one-participant-out cross validation approach was used to select the best performing classifier, which was found to have an accuracy of 84.03% with an F1 score of 0.8399. Skin-bed interface forces were measured using force sensitive resistors placed on the greater trochanters and sacrum. Complete offloading for the sacrum was only achieved for the positions with TPA angles <-90{degrees} or >90{degrees}, indicating there was no benefit to predicting with greater precision than with three categories: left, right, and supine.

The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI). The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset obtained from the Institute of Neural Engineering at Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover strong neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed models robustness against variations in the data is validated using 10-fold cross-validation (CV). Furthermore, the study explores the potential for subject-specific adaptation in an online paradigm, extending the proof-of-concept for classifying movement attempts. In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies.

We have recently demonstrated that humans with motor-and-sensory complete cervical spinal cord injury (SCI) can modulate the activity of spared motor neurons that control the movements of paralyzed muscles. These motor neurons still receive highly functional cortical inputs that proportionally control flexion and extension movements of the paralyzed hand digits. In this study, we report a series of longitudinal experiments in which subjects with motor complete SCI received motor unit feedback from NeurOne. NeurOne is a software that realizes super-fast digitalization of motor neuron spiking activity (32 frames/s) and control of these neural ensembles through a physiological motor unit twitch model that enables intuitive brain-computer interactions closely matching the voluntary force modulation of healthy hand digits. We asked the subjects (n=3, 3-4 laboratory visits) to match a target displayed on a monitor through a cursor that was controlled by the modulation of the recruitment and rate coding of the spared motor units using a motor unit twitch model. The attempted movements of the paralyzed hands involved grasping and hand digit extension/flexion. The target cursor was scaled in a way that the subjects could increase or decrease feedback by either recruiting or derecruiting motor units, or by modulating the instantaneous discharge rate. The subjects learned to control the motor unit output with high levels of accuracy across different target intensities up to the maximal achievable discharge rate. Indeed, the high-performance motor output was surprisingly stable in a similar way as healthy subjects modulated the muscle force output recorded by a dynamometer. Therefore, NeurOne enables tetraplegic individuals an intuitive control of the paralyzed muscles through a digital neuromuscular system. Significance StatementOur study demonstrates the remarkable ability of individuals with complete cervical spinal cord injuries to modulate spared motor neurons and control paralyzed muscles. Utilizing NeurOne, a software, we enabled intuitive brain-computer interactions by digitalizing motor neuron spiking activity and employing a motor unit twitch model. Through this interface, tetraplegic individuals achieved high levels of accuracy and proportional control which closely resembled motor function in intact humans. NeurOne provides a promising digital neuromuscular interface, empowering individuals to control assistive devices super-fast and intuitive. This study signifies an important advancement in enhancing motor function and improving the quality of life for those with spinal cord injuries.

Continuous and automatic monitoring of an individuals physical activity using wearable devices provides valuable insights into their daily habits and patterns. This information can be used to promote healthier lifestyles, prevent chronic diseases, and improve overall well-being. Smart glasses are an emerging technology that can be worn comfortably and continuously. Their wearable nature and hands-free operation make them well suited for long-term monitoring of physical activity and other real-world applications. To this end, we investigated the ability of the multi-sensor OCOsense smart glasses to recognize everyday activities. We evaluated three end-to-end deep learning architectures that showed promising results when working with IMU (accelerometer, gyroscope, and magnetometer) data in the past. The data used in the experiments was collected from 18 participants who performed pre-defined activities while wearing the glasses. The best architecture achieved an F1 score of 0.81, demonstrating its ability to effectively recognize activities, with the most problematic categories being standing vs. sitting.

The increasing use of accelerometers as digital health technologies in clinical trials and clinical care is driving the need for data processing to meet medical standards. The aim of this study was to create and test a modular pipeline for the pre-processing of high-resolution accelerometry that assures the quality, transparency and traceability of digital measures from sensor-level data. The objective is for the pipeline to be a foundational layer in the development, implementation and comparison of measures. The study developed the open GENEAcore package to meet the requirements of regulators, verifying the engineering implementation and analytically validating outputs against reference datasets. Early stages included the optimisation of calibration and non-wear detection. Data-driven detection of behavioural transitions was then validated to give direct bout outputs without the need to identify rules for epoch aggregation and interruptions. The utility for measure development was shown by comparing two algorithms for the characterisation of activity intensity in both the epoch and bout paradigms. Non-wear was detected with a balanced accuracy of 92.3% and the commonly used 13mg acceleration standard deviation threshold was empirically validated for the first time. The detection of transitions proved reliable with 99% detected, on average, within 2 seconds of their occurrence to give a mean expected event duration of 68.6s from a log-normal distribution. The different activity intensity algorithms were more than 99% concordant during movement but their outputs diverged in low movement conditions. Importantly, variable duration bouts created 31% higher daily activity durations compared to 1-second epochs. This evaluation of pre-processing steps has confirmed the attention to detail required to create robust and reproducible results for later clinical validation where small changes in an algorithm or its implementation may have clinically meaningful consequences.

The opportunity to collect movement data from smartphones for prolonged periods has opened new perspectives in the field of clinical movement analysis. However, when monitoring peoples mobility in free-living conditions, smartphone placement can influence the validity of the extracted digital mobility outcome. This study aimed to develop and validate an automatic smartphone placement recognition classifier and to investigate potential critical factors that can influence performance. The classifier was trained on data from 15 healthy participants using inertial signals collected from smartphones placed at six body placements during free-living walking and externally validated on over 3,000 individuals from external datasets, including blind participants and patients with cardiovascular or Parkinsons disease. A decision-tree ensemble model was developed using feature subsets of increasing dimensionality, with the optimal subset comprising 50 features. Classification accuracy increased consistently when front and back pocket placements were aggregated (81.1%) and further improved when coat pocket was also included in the pocket class (88.5%), underscoring the challenge of distinguishing between fine-grained pocket placements. The best-recognized placements across the external datasets were lower back (precision: 100%, recall: 72.5%), hand (precision: 94.2%, recall: 94.5%), and the aggregated pocket class (precision: 86.7%, recall: 90.2%). Recognition accuracy changed across cohorts (0.73 - 0.85), activities (0.63 - 0.94) and speed (0.79 - 0.87), however it stayed consistent across various technological and environmental factors. Overall, this study demonstrates the feasibility of robust placement recognition in walking and underscores the importance of accounting for key influencing factors when designing frameworks intended for deployment in heterogeneous real-world or clinical contexts. HighlightsO_LIMachine learning accurately identifies smartphone placement during real-world gait C_LIO_LISix on-body placements recognized, including pockets, hand, bag, and lower-back C_LIO_LIFree-living data used for training, ensuring robust performance across conditions C_LIO_LIFeature selection and hyperparameter tuning optimize classification accuracy C_LIO_LIExternal validation confirms generalizability across >3,000 healthy and diseased adults C_LI

High movement velocities are among the primary risk factors for work-related musculoskeletal disorders (MSDs). Ergonomists have commonly used two methods to calculate angular movement velocities of the upper arms using inertial measurement units (accelerometers and gyroscopes). Generalized velocity is the speed of movement traveled on the unit sphere per unit time. Inclination velocity is the derivative of the postural inclination angle relative to gravity with respect to time. Neither method captures the full extent of upper arm angular velocity. We propose a new method, the gyroscope vector magnitude (GVM), and demonstrate how GVM captures angular velocities around all motion axes and more accurately represents the true angular velocities of the upper arm. We use optical motion capture data to demonstrate that the previous methods for calculating angular velocities capture 89% and 77% relative to our proposed method. We propose GVM as the standard metric for reporting angular arm velocities in future research.

AO_SCPLOWBSTRACTC_SCPLOWThe sudden increase of wearable devices has led to the generation of an abundance of data. As a result, researchers can use such data to perform analyses and generate recommendations. A crucial factor in the research field of nutrition and dietetics is the accurate measurement of metabolic rate, as it can be used to estimate several other variables, e.g. calorie expenditure. Nonetheless, limited studies have been conducted to examine the use of machine learning models for metabolic rate prediction based on data generated from wearable devices. Therefore, in this paper, a neural network architecture is proposed, able to predict a subjects metabolic rate exploiting only such data. Experimental results demonstrated that the proposed methodology can outperform conventional algorithms in prediction accuracy of real-world data. Furthermore, results indicated that the trivial time taken for the network to predict the metabolic rate makes it suitable for wearable devices deployment.

IntroductionThigh-worn accelerometry is becoming increasingly popular in large-scale cohort studies for quantifying movement behaviour. Gait characteristics are associated with various health conditions and can be used to predict fall risk, monitor disease progression and evaluate rehabilitation outcomes. However, accurate gait assessment typically requires controlled laboratory conditions, that may not reflect real-world mobility. In this context, data-driven algorithms and machine learning approaches hold promise for extracting accurate gait parameters from raw accelerometer data. ObjectiveWe developed and evaluated a machine learning-based processing pipeline that uses activity classification to detect walking sequences, estimate walking speed, and identify gait events from raw thigh-worn accelerometer data, enabling accurate assessment of free-living gait. MethodsWe integrated an existing activity classification algorithm into the pipeline and evaluated its performance in free-living conditions. Walking speed was estimated based on stride frequency and body height. We then trained a temporal convolutional network model to predict the probability of gait events (i.e. initial and final contact) in healthy adults walking at various speeds on different inclines. All three components of the data processing pipeline were evaluated externally using various independent datasets. ResultsThe activity classification model achieved F1 scores [&ge;] 0.95 for walking in both adults and older adults. Walking speed was estimated with a mean absolute percentage error of 11.5%, and with a bias of 0.02 m/s. The gait event detection model demonstrated high accuracy, with a mean recall [&ge;] 0.94, precision [&ge;] 0.98, and mean absolute errors of 20 ms and 31 ms for initial and final contacts, respectively. ConclusionAccurate gait analysis in free-living conditions can be achieved by combining data-driven and machine learning approaches with thigh-worn accelerometer data. The developed pipeline can support the analysis of existing thigh-worn accelerometer datasets and enable continuous gait monitoring outside laboratory settings over several days. However, the developed methods for estimating walking speed and gait events require validation in a more diverse sample and in truly unrestricted free-living conditions.

Introductionautomatic movement recognition is often used to support various fields such as clinical, sports, and security. To date, there is a lack of a classification feature that is both interpretable and not movement-specific, characteristics that would enhance generalization and adaptability. Previous studies on motion analysis have shown that coordination properties extracted from full-body movement using network theory can describe specific movement characteristics, making coordination a potential feature for classification. Methodstherefore, we leveraged kinematic data from 168 individuals performing 30 different movements, published in an online dataset. Using network theory, we reduced data dimensionality, obtaining a coordination matrix called the kinectome. By applying support vector machine algorithms, we compared the classification performance of the kinectome with that of principal component analysis, used as an alternative data reduction method. Resultsthe classification accuracy of the kinectome (0.99 {+/-} 0.01) was significantly higher (pFDR < 0.001) than that of PCA (0.96 {+/-} 0.04). Moreover, unlike PCA, the kinectome demonstrated resilience to data loss, robustness to derived measures, independence from the classification algorithm, and clear interpretability of features. Discussionour results suggest that kinnectome-based features could capture interpretable changes between movements that could pave the way to new automatic movement recognition approaches dedicated to a wide range of applications, in particular sport training and physical readaptation, and designed for non-data scientists experts.

This narrative review examines the theoretical foundations of mental workload, evaluates biometric monitoring methods, addresses ethical and privacy issues, and highlights future directions for longitudinal research. We focus on the integration of wearable sensors, multimodal data, artificial intelligence (AI), and machine learning (ML) frameworks to enhance adaptive task scheduling and safety in cognitively demanding professions. Continuous, real-time monitoring through wearable devices and multidimensional data analysis show promise for identifying and managing cognitive overload before it degrades performance. Despite this potential, significant challenges exist, including data protection, sensor reliability, calibration consistency, information processing, network capabilities, and variability in individuals responses. Physiological and behavioral measures as well as subjective and performance indicators offer valuable insights into the early signs of cognitive strain, suggesting that biometric monitoring could help organizations detect performance decline sooner. Evidence shows that these technologies are feasible in professions that require high precision, rapid decision-making, and sustained attention. However, only sparse longitudinal comparisons exist regarding the effectiveness of different biometric tools in real-world operational contexts, particularly with respect to data security and standardization. Integrating physiological and behavioral data with subjective assessments analyzed through AI and ML may enable early warning signs for overload in both individuals and teams working in high-stress, time-critical settings. Such approaches could inform work-recovery cycles, reduce error rates, and sustain cognitive performance. Further empirical research is necessary to confirm sensor accuracy in applied environments and to validate AI and ML predictions before large-scale deployment in sectors such as air traffic control, public safety, healthcare, and industrial operations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/25334668v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@b2c603org.highwire.dtl.DTLVardef@e61f87org.highwire.dtl.DTLVardef@1fee11org.highwire.dtl.DTLVardef@46d485_HPS_FORMAT_FIGEXP M_FIG C_FIG

Parkinsons disease (PD) is one of the most common neurodegenerative disorders of the central nervous system, which predominantly affects patients motor functions, movement, and stability. Monitoring movement in patients with PD is crucial for inferring motor state fluctuations throughout daily life activities, which aids in disease progression analysis and assessing how patients respond to medications over time. In recent years, there has been an increase in the usage of wearable sensors for PD symptom monitoring. In this study, we present a preliminary analysis of smart glasses equipped with IMU sensors to provide objective information on the motor state in patients with PD. Data were collected from seven Parkinsons patients with varying levels of symptom severity. The patients performed the Timed-Up-and-Go (TUG) Test while wearing IMU-equipped glasses. Our analysis indicates that smart glasses can provide information about patients gait that can be used to assess the severity level of the PD as measured by two standardized questionnaires. Furthermore, patient-specific clusters can be easily detected in the sensor data, hinting at the development of personalized models for patient-specific monitoring of symptom progression. Therefore, smart glasses have the potential to be used as an unobtrusive and continuous screening tool for PD patients gait, enhancing medical assessment and treatment. CCS CONCEPTS * Applied computing * Life and medical sciences * Health informatics ACM Reference FormatFirst Authors Name, Initials, and Last Name, Second Authors Name, Initials, and Last Name, and Third Authors Name, Initials, and Last Name. 2022. The Title of the Paper: ACM Conference Proceedings Manuscript Submission Template: This is the subtitle of the paper, this document both explains and embodies the submission format for authors using Word. In Woodstock 18: ACM Symposium on Neural Gaze Detection, June 03-05, 2018, Woodstock, NY. ACM, New York, NY, USA, 10 pages. NOTE: This block will be automatically generated when manuscripts are processed after acceptance.

The rapid global urbanisation has intensified the need to understand the complex interactions and impacts that city environments have on human physical or mental health and well-being. Traditional indoor laboratory-based approaches conduct experiments in well controlled settings but, while advantageous for their controlled conditions, they often lack ecological validity. To address this gap, we present the "eMOTIONAL Cities Walker Backpack" -- a wearable unit developed for synchronously collecting multi-modal data in dynamic real-world settings. Designed for both indoor and outdoor use, the backpack integrates environmental (for microclimate, air pollution and noise) and physiological sensors (including electroencephalography, eye-tracking and wrist-based biosensors for cardiovascular monitoring) to enable the study of human experience in naturalistic urban environments. In this paper, we describe the technical specifications and implementation of this technology during outdoor acquisitions across selected urban locations in the city of Lisbon. We also highlight its potential for methodological comparison with traditional lab-based tasks (particularly through the use of equivalent sensing technologies), and thus advancing the field of translational research in mental health and urban studies.

Range of motion (ROM) serves as a crucial metric for assessing movement impairments. Traditionally, clinicians use goniometers to measure the ROM, but this method relies on the clinicians skill, in particular for difficult joints such as the shoulder and neck joints. Recent studies have explored the use of wearable inertial measurement units (IMUs) as an alternative. IMUs exhibit excellent agreement with goniometers, but the lack of affordable, accessible, and clinically validated tools remains an issue. This paper introduces the Romiumeter, a single IMU-based device designed to measure the ROM of the neck and shoulder movements. To validate its accuracy, the Romiumeter was tested on 34 asymptomatic individuals for shoulder and neck movements, using an optical motion capture system as the ground truth. The device demonstrated good accuracy, with a maximum absolute error of less than 5{degrees} with moderate to good reliable measurements(inter-rater reliability: 0.69 - 0.87 and intra-rater reliability: 0.76 - 0.87). Additionally, the Romiumeter underwent validation for different algorithms, including the complementary and Madgwick filters. Interestingly, no significant differences were found between the algorithms. Overall, the Romiumeter provides reliable measurements for assessing shoulder and neck ROM in asymptomatic individuals.

Oral cancer is a significant public-health issue and the existing methods of its detection are not as simple or fast as to be applicable by a wide population, particularly by those living in underserved communities. Our team has a proposed solution to address this problem by involving the concept of Convolutional Neural Networks (CNNs) to classify smartphone images into normal or malignant categorization in real time. We defined the model training to use a set of 1071 smartphone camera photos which were then pre-processed to convert them to HSV, normalize and resample the images. The CNN had an accuracy of 94.29 %, precision of 95.45%, recall/sensitivity of 93.33%, and F1-score of 94.38 after training. The overall predictive performance evaluation was calculated with an area under the receiver operating characteristic curve (AUC) of 0.99 with an average inference time of less than 5 sec so the clinicians or patients can send their images and get results in a short time. In contrast to other available methods, the EfficientNetB0 model is quicker and computationally less demanding, which is more suitable to be used on a mobile platform. The primary drawbacks which were big obstacles at the beginning of the project were the variance in image quality, the absence of annotated data, and changing the dataset to a larger and more diverse one, along with the application of advanced preprocessing enhanced the performance of models. The next step will be to focus on a large-scale clinical validation and additional model improvement. To sum up, the system is an AI-based method of scalable, cheap, and fast front-end screening that has a potential to significantly improve the outcomes of oral-cancer by early identification.

Continuous, noninvasive blood pressure monitoring remains an unmet clinical need, particularly in the intensive care unit (ICU) where hemodynamically unstable patients need high-frequency monitoring. Invasive arterial catheterization represents the current standard of care for continuous blood pressure (BP) monitoring, but it carries risks and limits patient mobility. In this study, we evaluate the MOSAIC system, a novel multi-modal, multi-nodal wearable, wireless sensor system placed on multiple locations on the body, for continuous noninvasive BP estimation in a cohort of ICU patients. Unlike existing continuous BP sensors, the MOSAIC system offers an ideal form factor for continuous BP monitoring, enabling a fully untethered setup which minimally impacts activities of daily living. Leveraging sensor-derived biosignals to compute continuous BP, we determine the accuracy of our BP regression models using arterial line-derived blood pressure reading as a ground truth. Using a Light gradient boosted machine (LGBM)-based regression model, we demonstrate strong beat-to-beat agreement with a mean absolute error (MAE) of 5.66 +/- 5.94 mmHg for systolic BP (SBP) prediction and 2.45 +/- 2.87 mmHg for diastolic BP (DBP) prediction, and average ratio variability (ARV) of 0.527 +/- 0.185 and 0.489 +/- 0.170 for SBP and DBP, respectively, compared to linear and deep-learning regression baselines. Our findings demonstrate strong agreement between the predicted BP values and invasive, arterial-line BP measurements, supporting the feasibility of wearable, wireless, and cuffless blood pressure monitoring in high-acuity clinical settings.

This study aimed to evaluate the use of novel optomyography (OMG) based smart glasses, OCOsense, for the monitoring and recognition of facial expressions. Experiments were conducted on data gathered from 27 young adult participants, who performed facial expressions varying in intensity, duration, and head movement. The facial expressions included smiling, frowning, raising the eyebrows, and squeezing the eyes. The statistical analysis demonstrated that: (i) OCO sensors based on the principles of OMG can capture distinct variations in cheek and brow movements with a high degree of accuracy and specificity; (ii) Head movement does not have a significant impact on how well these facial expressions are detected. The collected data were also used to train a machine learning model to recognise the four facial expressions and when the face enters a neutral state. We evaluated this model in conditions intended to simulate real-world use, including variations in expression intensity, head movement and glasses position relative to the face. The model demonstrated an overall accuracy of 93% (0.90 f1-score) - evaluated using a leave-one-subject-out cross-validation technique.

Stress management is a pervasive issue in the modern high schoolers life. Despite many efforts to support adolescents mental well-being, teenagers often fail to recognize signs of high stress and anxiety until their emotions have escalated. Being able to identify early signs of these intense emotional states and predict their onset using physiological signals collected passively in real-time could help teenagers improve their awareness of their emotional well-being and take a more proactive approach to managing their emotions. To evaluate the potential of this approach, we collected data from high schoolers with Empatica E4 wearable health monitors (wristband) while they were living their daily lives. The data consisted of stressful event reports and physiological markers over the course of 4 weeks. We developed a random forest model and a support vector machine model and systematically assessed their performance in terms of predicting the onset of stress events and identifying physiological signals of stress. The models showed strong performance in terms of these measures and provided insights on physiological indicators of adolescent stress.