Sensors

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.

While the need for continuous blood pressure (BP) monitoring in Japan is high, there are no commercially available cuffless devices for personal daily monitoring use. Fingertip-based sensors are a promising alternative as they eliminate the discomfort of repeated cuff inflation. However, their reliability during winter has been a major technical limitation due to cold-induced peripheral vasoconstriction. This study aimed to address this issue by validating a novel fingertip-based continuous BP monitor used by exercising adults during summer and winter. Eleven community-dwelling older adults (mean age, 73.1 {+/-} 8.8 years) were included in this seasonal comparative study. During exercise, we compared a personal fingertip-based continuous monitor (ArteVu) with a standard oscillometric cuff device (Omron) in summer (mean, 26.5{degrees}C) and winter (mean, 7.4{degrees}C). The study also evaluated the devices accuracy during exercise-induced BP fluctuations and seasonal environmental changes. Awareness of the participants regarding BP management was also assessed using questionnaires. There were strong correlations for systolic BP (SBP) between summer and winter (r = 0.93 in summer; r = 0.88 in winter). Although the mean difference for the SBP was higher in winter than in summer (3.1 {+/-} 11.2 mmHg vs. 0.2 {+/-} 9.4 mmHg), the values remained within a clinically acceptable range for personal monitoring. Notably, 72.7% of participants reported that the ease of using the fingertip-based device significantly increased their awareness and motivation for daily BP management. This study confirms the feasibility of cuffless fingertip-based continuous BP monitoring across different seasons, including in winter. By overcoming the seasonal limitations, this device fills a critical gap in the Japanese health-monitoring market. Our findings support the development of smaller and more portable models, representing a shift from traditional "snapshot" cuff measurements to continuous and integrated lifestyle monitoring for older adults.

This analysis evaluates the feasibility and psychometric properties of daily digital cognitive assessments (DCAs) delivered on smartphones using data from the large, international Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities of daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases (IDEA-FAST) study. The data we analyse were collected from patients with neurodegenerative diseases (NDDs) and immune-mediated inflammatory diseases (IMIDs), and healthy controls (a subset who participated in all phases of the study, total N=977) in their own homes. These data were obtained alongside data from other devices that monitored physiology, kinematics, and sleep quality. Following a baseline visit, participants were remotely monitored via three scheduled daily sessions for 6-7 days in each of 4 active assessment phases (APs). APs were separated by 6-week intervals. Daily schedules comprised a morning psychomotor vigilance task (PVT) with eDiary, afternoon session (eDiary only), and an evening digit symbol substitution task (DSST) with eDiary. We evaluated session coverage using logistic mixed effects, test-retest reliability using ICCs, disease impacts on performance using linear mixed effect ANCOVA, and familiarisation using linear mixed effects. Overall coverage was 67.5% for the PVT and 77.0% for the DSST, with no significant differences between the healthy volunteers and disease cohorts. Coverage varied significantly by time-of-day (Evening > Morning > Afternoon), and improved with age, with an interaction revealing session time-of-day affected older participants less, all p < .001. Coverage was highest in AP 1 and reduced in subsequent APs. AP-day effects on coverage interacted significantly with AP, with a modest decline over AP 1, and the pattern reversed in APs 2-4. Baseline reliability was good (> .70) for both PVT mean reaction time and DSST total correct across all cohorts, and the movement-based measure from the DSST ranged [.55, .75], with lower values in the Parkinsons Disease and Primary Sjogrens Syndrome cohorts. Both tasks showed significant cohort effects, with performance in IMID cohorts intermediate between healthy controls and NDD. Longitudinal analysis revealed significant familiarisation effects in DSST. This was greatest in healthy controls, with significant attenuation of these effects in disease cohorts. No effect of familiarisation was seen in the PVT. Collectively, these results support the usefulness of at-home cognitive assessment on smartphones. Brief measures of cognition can be captured remotely in disease as well as controls with good adherence and sensitivity to distinguish known patient groups from healthy controls.

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

With the emergence of electroencephalography (EEG) as a tool in the cognitive domain, new demands are being placed on the technology to keep up with functional applications, especially in the context of at-home neural monitoring. New use cases have fostered development of wearable EEG (wEEG) devices: portable, low-cost headsets used for EEG monitoring. This evolution of technology and application has not been accompanied by development in technology evaluation, often relying on function-agnostic markers to assess devices for efficacy in this new space. With current methods limited in scope, this study designed, tested and evaluated a novel functionally-focused comparative protocol for wEEG devices. Eight participants undertook a protocol for the evaluation of four established wEEG devices, assessing cognitive resolution and general usability. Compared to a well-established traditional analysis method (eyes open/eyes closed protocol), the novel design proposed here enabled the same analysis of headset resolution, while also providing additional context into user preferences and opening downstream possibilities for specific cognitive insights. Future research could enable the development of this protocol into a standardised method to ensure the performance of wEEG technology can satisfy emerging clinical needs.

Recent advancements in wearable sensors and machine learning show promise for estimating lower-body joint torques outside of laboratory settings. Inertial Measurement Units combined with Convolutional Neural Networks have proven effective for this task. However, the impact of different input data types and formats remains underexplored. This study investigates how variations in input data influence the prediction of lower-body joint torques during walking. Results indicate that while dataset choice causes only minor differences in prediction performance, the overall quality of the dataset plays a more critical role than the specific input variables in achieving accurate torque predictions using wearable sensors.

Reliable interpretation of electrocardiograms (ECGs) requires precise identification of P, QRS, and T (PQRST) wave boundaries. However, it remains challenging due to noise, signal quality variability, and inherent morphological diversity particularly in recordings from children. This study systematically compares the performance of leading deep neural networks (DNN) and heuristic-based delineation algorithms on ambulatory single-lead ECG signals focusing on temporal accuracy. Experiments were conducted using the publicly available LUDB dataset and a private validation dataset comprising 21,759 annotated single-lead wave segments from 611 children recorded using KardiaMobile ECG sensor. DNN were first trained on the LUDB dataset and subsequently tested on the validation dataset. The delineation performance was assessed using Sensitivity (Se) and positive-predictive-value (P+) metrics. The best-performing heuristic based and DNN models reached Se and P+ of (98.9% vs 97.9%) for P, (99.8% vs 99.2%) for QRS, and (98.7% vs 95.9%) for T wave fiducials, respectively. The lowest standard-deviation (in ms) of wave onset/offset delineation was achieved by attention based 1DU-Net model; {+/-}16.6/{+/-}16.3 for P-wave, {+/-}14.0/{+/-}16.3 for QRS, and {+/-}26.3/{+/-}18.8 for T-wave, respectively. The findings indicate that optimized heuristic models can perform comparably to complex DNN, highlighting their efficiency and suitability for real-time ECG delineation in digital health monitoring applications.

Carsickness impairs comfort and affects a large proportion of the population. However, interventions that provide a therapeutic solution to carsickness have yet to be established. Here we introduce a wearable mindfulness meditation brain-computer interface (MM-BCI) system as a closed-loop training therapy for carsickness. The system records electroencephalographic activity, decodes meditative state in real time and delivers audiovisual neurofeedback to scaffold meditation practice. In a 10-week randomized controlled trial, 60 individuals susceptible to carsickness were assigned to practice mindfulness meditation with either real-time MM-BCI neurofeedback or sham feedback, both during real-world car riding and at home. Critically, pre-intervention, post-intervention, and one-month follow-up assessments of carsickness severity were conducted during regular car riding without any task or feedback system. Relative to the sham group, the MM-BCI group showed significantly reduced carsickness severity at post-intervention and follow-up. At baseline, carsickness-susceptible participants exhibited a reduced aperiodic exponent in occipito-parietal cortex relative to non-susceptible controls, identifying a candidate neural signature of carsickness susceptibility. MM-BCI training increased this exponent toward non-susceptible levels, and the magnitude of this neural normalization was associated with the degree of symptom improvement. This study provides the first demonstration that BCI-enhanced mindfulness meditation can induce promising treatment effect on carsickness, offering a transformative non-pharmacological approach to enhance passenger well-being in everyday transit.

The 1.8 million residents of Region Hovedstaden (Denmarks Capital Region) currently lack a secure, standardized pathway for integrating continuous wearable health data into Sundhed.dk, the national electronic health record. Consumer wearables such as Apple Watch, Oura Ring, and Garmin generate longitudinal physiological data relevant to chronic disease management, yet existing workflows rely on manual, non-standardized exports incompatible with FHIR DK v6.0.2 profiles and GDPR Article 25 privacy-by-design requirements. This paper presents a conceptual five-layer microservice architecture for secure wearable data sharing, employing MitID national authentication, National Service Infrastructure (NSI) integration, and Zero Trust security controls. Requirements were derived from a mixed-methods study including surveys of 47 Danish stakeholders and systematic benchmarking of existing platforms. Results show 51.1% conditional willingness to share wearable data under secure conditions, with audit transparency and non-medical misuse identified as central trust factors. Fourteen MoSCoW-prioritized requirements (F1-F7, NF1-NF7) are mapped to architecture components, providing a traceable blueprint for closing the interoperability gap in Danish public healthcare.

A Wearable Monitoring System (WMS), comprising a chest patch, wrist-worn pulse oximeter, and arm-worn blood pressure device, was developed in preparation for a pilot Randomised Controlled Trial (RCT) on a UK surgical ward. The system was designed to support continuous physiological monitoring and early detection of deterioration. An initial prototype user interface was developed by the research team based on prior clinical experience and engineering knowledge. To ensure suitability for clinical practice, iterative user-centred refinement was undertaken through a series of clinician focus groups and wearability assessments. Six focus groups were conducted between November 2019 and May 2021 involving multidisciplinary healthcare professionals. Feedback from these sessions informed successive interface and system modifications. System development spanned the COVID-19 pandemic, during which the WMS was rapidly adapted and deployed to support clinical care on isolation wards. Feedback obtained during this period was incorporated into later versions of the system and provided a unique opportunity to examine changes in clinician priorities under pandemic conditions. Clinicians consistently prioritised alert visibility, alarm fatigue mitigation, parameter flexibility, and centralised monitoring. Notably, preferences regarding alert modality and access mechanisms evolved over time: early enthusiasm for mobile or smartphone-type devices shifted towards a preference for fixed, ward-based displays and audible alerts at the nurses station following pandemic deployment. Building on previous wearability testing in healthy volunteers, wearability testing using a validated questionnaire was completed by 169 patient participants during the RCT. The chest patch and pulse oximeter demonstrated high tolerability, whereas the blood pressure cuff showed poor wearability and was removed from the final system. These findings demonstrate the importance of iterative, clinician-led design for wearable WMS and highlight how extreme clinical contexts such as the COVID-19 pandemic can significantly reshape perceived requirements for safety-critical monitoring technologies.

This paper focuses on forecasting minute-by-minute stress, anxiety, and affective states using wearable sensor data. It addresses mental health as a growing concern and the limitations of traditional assessment methods. A time-series machine learning framework was developed using electrodermal activity (EDA) and heart rate variability (HRV) features from the WESAD dataset. Models were trained and evaluated for minute-by-minute prediction of self-reported psychological states. Both classification (stress, anxiety) and regression models (affect) were explored comparing time-series and static approaches. Findings support the feasibility of real-time, personalized mental health monitoring using wearable devices and their potential for timely interventions in clinical or remote settings. The paper demonstrates how temporal modeling can enhance emotional state prediction and inform future research and system development.

Background and Objectives Patients with peripheral neuropathies (PN) commonly exhibit balance impairment. In clinical practice, balance is typically assessed using the Rombergs test and ataxia scales, which rely on examiner interpretation, while objective biomarkers for quantifying balance remain lacking. Wearable sensors are valuable tools for objectively quantifying gait abnormalities in PN patients and may capture clinically meaningful changes over time. By integrating these parameters, artificial intelligence (AI) can assist in generating a digital score that enables easy, objective, and reproducible monitoring of patients postural balance. This study aims to generate and assess an AI-generated digital Rombergs test to quantify balance impairments in a cohort of PN patients. Methods PN patients were assessed in a longitudinal study using a wearable system composed of inertial sensors placed on the trunk and plantar pressure sensors integrated in insoles. Patients performed the Rombergs test under both eyes-open and eyes-closed conditions and were classified according to ataxia severity (mild, moderate, or severe) following the score obtained in item 1 of MICARS and SARA scales. Results We included 97 patients with PN (including autoimmune and hereditary polyneuropathies), and 117 healthy controls (HC). Significant differences in trunk sway and center of pressure (COP) were observed between groups, particularly with eyes closed. Using wearable sensor parameters, we developed an AI digital Rombergs test, which correlated with clinician-rated Rombergs test performance and distinguished patients with and without ataxia (AUC=0.632) and across different PN pathologies. Longitudinally, digital Rombergs test and iRODS showed concordant trajectories. Also, changes [&ge;]25% in the score were associated with clinical changes in ataxia severity measured by an increase in MICARS-SARA score (+1.42 points), whereas improvement was associated with a decrease (-0.20 points) in the scale. Discussion This study demonstrates that wearable sensors are useful to detect and quantify balance impairment. The AI-generated Rombergs test is an objective and reproducible tool for postural balance assessment, with robust discriminatory performance across clinical ataxia severity in PN. Scores longitudinal changes aligned with clinical severity, supporting its potential for monitoring disease progression and treatment response. Its strong association with balance measures reinforces its role as a quantitative biomarker of postural control in ataxia patients.

Early-stage Parkinsons disease (PD) presents motor impairments that are difficult to detect clinically. Surface EMG (sEMG) offers an objective alternative, yet many studies rely on non-standardized tasks and provide limited task- and symptom-specific interpretability. This study analyzes sEMG recorded during standardized MDS-UPDRS-III upper-limb tasks--pronation-supination and postural tremor--performed by individuals with early-stage PD (Hoehn and Yahr 1-3, n=31) and healthy controls (n=30). Time-, frequency-, and nonlinear features were extracted and evaluated using a two-stage framework combining filter-based ranking and wrapper-based methods to support feature selection across multiple classifiers and interpretability. Pronation-supination showed the strongest single-task discrimination (balanced accuracy 0.79{+/-}0.181), driven by rhythm and nonlinear features reflecting impaired rhythmicity, reduced neuromuscular complexity, and unstable muscle deactivation, consistent with bradykinesia and rigidity. The postural tremor task highlighted tremor-specific spectral changes and reduced signal complexity during sustained posture (balanced accuracy 0.75{+/-}0.18), capturing low-frequency oscillations typical of PD tremor. Combining both tasks further improved classification without increasing feature dimensionality (balanced accuracy 0.83{+/-}0.186), indicating complementary diagnostic information. Filter-guided selection enhanced robustness and consistency across models. Beyond classification, this study highlights the value of interpretable, task-aligned motor quantification, showing that standardized clinical movements combined with targeted sEMG analysis can support explainable assessment of early-stage PD motor symptoms.

Wearable devices for gravity balancing have high potential for impact across domains, including neuromotor rehabilitation and occupational systems. Devices made from compliant mechanisms, optimized to achieve specific compensation moments at target joints, have proven effective, but thus far have solely been optimized towards gravity compensation and not other wearability criteria. In this work, we propose a multi-objective optimization framework, based on particle swarm optimization, to design a soft, gravity balancing shoulder orthosis, while taking into account wearability constraints such as undesired loading directions and device size. Using this custom framework, we pursued multiple stages of orthosis design and optimization, selecting multiple solutions to be translated to real-world prototypes. These solutions were realized via 3D printing with thermoplastic polyurethane and evaluated for mechanical performance on benchtop and in-vivo. In-vivo testing on 6 healthy individuals demonstrated relative reductions in muscle activity for the anterior deltoid and upper trapezius, by 53 % and 71 % respectively when operating the orthosis for static tasks within functional shoulder ranges of motion. Changes in muscle activation were also were observed across other muscles, including the posterior deltoid, as well as in dynamic tasks at different speeds.

Non-invasive wrist pulse monitoring has been integrated into various medical systems for cardiovascular assessment. However, different definitions of pulse transit time are used in the literature, and their statistical behavior when measured locally at the wrist using pressure sensors has not been systematically examined. Wearable wristbands designed to measure pulse transit time (PTT) have emerged as valuable tools for evaluating cardiac activity. While several algorithms have been developed to predict blood pressure using PTT, it is well recognized that PTT and its inverse parameter, pulse wave velocity (PWV), exhibit temporal variability. In this study, PTT was explicitly measured at the wrist's radial artery to investigate its statistical variation and relationship with different arterial pressures. The experiment exhibits two distinct methodologies for PTT computation using onset-based and peak based measurements. Data were recorded across five cuff pressure levels at 20, 40, 60, 80, and 100 mmHg using the pulse pressure sensor (PPS). PTTonset time shows lower coefficient of variation as compared to PTTpeak time within the 100 mmHg pressure range. The weak correlation coefficient is recorded between PTT values. However, dynamic time warping (DTW) analysis revealed a notable similarity in the time series of PTTonset and PTTpeak, regardless of the applied pressure level. For the multi participant dataset, the mean DTW distances ranged from 0.029 to 0.046 across the tested cuff pressures, illustrating consistent similarity between PTTonset and PTTpeak over time. The objective of this study is to examine the statistical behavior, stability, and temporal similarity of the two commonly used PTT definitions when measured at the radial artery using pressure sensors. Statistical analysis shows consistent differences between the two PTT definitions across participants. PTTonset shows lower variation than PTTpeak. However, PTTpeak requires simpler computation and produces fewer detection errors, while PTTonset provides lower statistical variation.

This study aims to predict human intentions during intense sports activities, specifically in table tennis. Using a publicly available Real World Table Tennis dataset containing simultaneous EEG and video recordings, we developed a series of participant-specific classifiers for nine players (7 males and 2 females; age range 18-30), based on pose features and EEG signals. The pose-based classifier used a stochastic gradient descent model with logistic loss, whereas the EEG-based classifier employed a modified convolutional neural network architecture (EEGNet). Both classifiers successfully predicted left-right attack intentions from the time windows preceding racket-ball impact, with optimal decoding occurring at -100 ms for pose features and -500 ms for EEG signals. EEG-based decoding achieved higher performance than pose-based decoding, and a multi-modal ensemble further improved prediction, reaching a mean macro F1 score of 0.563 (bootstrapped 95% CI: 0.523-0.603), corresponding to gains of +0.03 over pose-only and +0.02 over EEG-only classifiers. Because each classifier is trained independently, the ensemble can be feasibly extended to incorporate additional modalities in the future. These results suggest potential applications in neural prosthetic systems and neurofeedback tools for sports training.

BACKGROUND: Aging and Parkinsons disease (PD) reduce gait automaticity and increase cognitive demand during walking. Although dual task (DT) paradigms investigate cognitive motor interference, evidence remains limited by heterogeneous tasks, predominant focus on prefrontal cortex (PFC) activity, and variability in functional near infrared spectroscopy (fNIRS) methods. This study investigates whether longitudinal changes in cortical activation during DT walking differ among young adults, older adults, and individuals with PD, and how these changes relate to DT costs over 5 years. METHODS: This longitudinal observational study follows STROBE and fNIRS guidelines and will be conducted in a controlled laboratory (Rede Amparo, CEPID NeuroMat, University of Sao Paulo). Participants will be evaluated annually under three randomized conditions: motor single-task walking, cognitive single task phonemic verbal fluency and DT walking with phonemic verbal fluency, each repeated 10 times. The primary outcome measure will be longitudinal changes in cortical activation during DT walking, quantified by oxygenated hemoglobin (HbO) signals measured with fNIRS in prefrontal and premotor cortical regions. The main predictors of interest will be motor and cognitive DT costs. Covariates will include age, sex, education, cognition, balance, mood, and disease severity in the PD group. Spatiotemporal gait parameters, including gait speed, step length, stride length, step time, base of support, double support, stance phase, and variability, will be recorded using the GAITRite system, and DT costs will be calculated for selected parameters. Cortical activation will be assessed using a 66 channel wearable fNIRS system with short separation channels. DISCUSSION: By combining randomized task blocks, separate motor and cognitive conditions, broader cortical coverage, and concurrent neural and gait assessment across three groups annually, this protocol is expected to provide a comprehensive characterization of cognitive motor interference during walking and its evolution, supporting interpretation of cortical and behavioral responses. The study may help distinguish age related adaptations from PD specific alterations and clarify whether increased cortical recruitment during DT gait reflects compensation, reduced neural efficiency, or ceiling effects, refining understanding of gait automaticity decline and informing rehabilitation and non invasive brain stimulation approaches.

PurposeWe present a large-scale (N=120) comparative study of gel-based and dry electroencephalography systems for cognitive load analysis in tasks involving information visualization stimuli. Although dry systems are increasingly adopted owing to their portability and fast setup, their sensitivity to cognitive-related measurements (as compared to gel-based systems) remains debated. This limits the understanding of whether dry systems provide sufficient sensitivity for cognitive load assessment under controlled task conditions. MethodsWe analyzed a diverse set of signal quality metrics, such as signal-to-noise ratio and channel retention, combined with spectral features across frequency bands to evaluate the ability for each device to capture workload-related neural markers during information visualization tasks. ResultsAlthough the gel-based device showed consistently better quality results than the dry one, the effect sizes suggest a small practical significance of the differences between systems. These results demonstrate that dry systems can provide adequate physiological sensitivity for cognitive load assessments. ConclusionOur findings highlight the trade-off between usability (setup, calibration, etc.) and data fidelity, providing practical guidance for choosing electroencephalography systems for cognitive workload monitoring and applied neuroengineering research. Overall, the results suggest that dry systems can support coarse-grained cognitive load assessment, while gel-based systems remain advantageous when greater sensitivity is required.

Introduction: Physical activity during pregnancy can be tracked directly by accelerometer measurements and indirectly by validated questionnaires. Considering the advancement of the Internet of Things (IOT), managing and/or monitoring physical activities can be better explored to analyze individuals, as well as indirectly compare the intensity and domains of physical activities carried out by pregnant women. The project, called 'EVA'(Expert Virtual Assistant), suggests combining several fields of knowledge to obtain better information about physical activity during pregnancy, surpassing the claim made in previous research that studying and measuring the duration of daily physical activities in pregnant women is a challenge. Objective: In the present study, we present the results of the first stage of the EVA project, which aims to develop a Virtual Assistant (VA) in Portuguese, providing examples of health management features for monitoring Physical Activity measurements for pregnant women assisted in the Unified Health System (SUS) and the adaptation of the Pregnancy Physical Activity Questionnaire (PPAQ). Methods and Analysis: The methods used were developed in two stages: adapting the physical activity questionnaire and building the Virtual Assistent to monitor physical activities. Thirty pregnant women who used the Unified Health System (SUS) in the city of Ribeir&atildeo Preto, Brazil participated in the study. The pregnant women wore sensor wristbands (accelerometers) and answered the sociodemographic, lifestyle and physical activity questionnaires via an application developed for this study. Results: The questionnaire used was the PPAQ adapted for Brazilian pregnant women. The most important changes were in the occupational domain for the house cleaning and in sedentary behavior activities. In the pilot study, it was observed that pregnant women spend more energy at home and in light and moderate intensity activities. textbfConclusion:This study made important contributions to evaluating PA in pregnant women. The proposal and studies for the construction of the AV-EVA, the inclusion of a specific occupational domain for pregnant women with domestic occupations and the new cutoff points for PA intensity measurements obtained via accelerometers.

With metabolic disease on the rise across the globe, the devices that can provide precise and reliable estimates of energy expenditure and macronutrient oxidation can play a critical role in the development and evaluation of therapeutic regimes and wearable technologies that can be used outside of the laboratory. Whereas, metabolic carts can provide short-term (minutes to hours) metabolic measurements, whole-room calorimeters enable long-duration (hours to days) metabolic assessment, providing insights into how metabolism changes in response to meals, activity, sleep, etc. Obtaining accurate metabolic measurement via whole-room calorimetry, however, requires rigorous methods for calibration and quality assurance. To date most room calorimeters have been tuned to assess energy expenditure over long periods of time, i.e. 24-hours. Here we present novel calibration and signal processing techniques and recommendations that aim to improve the utility of metabolic chambers for use over different measurement epochs. This work serves as both a transparent description of our hardware, validation procedures, and data processing approaches.