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 device's 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.

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.

Blood pressure (BP) measurement is crucial for medical care, yet existing BP methods are either invasive, tethered, or suffer from low temporal resolution. Non-invasive continuous BP estimation thus remains a significant challenge. To address these challenges, this work presents a novel, non-invasive, multi-modal sensor designed for continuous blood pressure estimation using multiple biosignal modalities as feature inputs. From these input data, we extract cardiovascular timing intervals (e.g., pulse arrival time), which serve as key features for BP regression models, enabling continuous, non-invasive BP monitoring. We validate our algorithm with 16 healthy subjects using standard blood pressure cuff readings as ground truth. Our wearable, non-invasive multimodal and multinodal sensor array for integrated computation (MOSAIC) demonstrated promising performance and was able to predict systolic and diastolic BP across all study subjects with a MAE of 5.31 {+/-} 7.32 mmHg and 4.27 {+/-} 2.35 mmHg, respectively.

The electrocardiogram (ECG) is a critical tool in the diagnosis and monitoring of cardiovascular disease. Although traditional 12-lead ECGs offer comprehensive in-sights into the electrical activity of the heart, they typically require clinical settings and expert interpretation, which limits their accessibility. In contrast, smartwatch 1-lead ECGs can be recorded at home, allowing more frequent and rapid monitoring. This opens opportunities not only for early detection but also for enhancing patient autonomy. This study investigates whether 1-lead ECGs can provide information beyond heart rhythm, specifically whether they can be used to assess left ventricular function (LVF) using explainable deep learning models. Our findings show that LVF can be accurately predicted from 1-lead ECGs (AUC = 0.883), nearly matching the performance of 12-lead ECGs (AUC = 0.897). These results suggest that 1-lead ECGs, when combined with interpretable AI, could support broader clinical applications and empower patients, particularly in resource-limited or remote settings.

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.

Passive acoustic monitoring is a valuable tool for studying elusive marine mammals, but analyzing large datasets is typically labor-intensive and costly. In this study, we piloted an automatic approach for sound analysis on extensive datasets of acoustic underwater recordings from freshwater Lake Saimaa over a total of 12 months. Our focus was on "knocking" vocalizations, the most commonly found call type of the endangered Saimaa ringed seal (Pusa saimensis). The annotated datasets of knock sounds (n = 13,179) were used to train and test binary classification systems to detect this sound type. In addition, the fundamental frequencies of the vocalizations were automatically estimated by an ensemble of methods and corroborated by recent literature. The best classifier was a spectrogram-based convolutional neural network that achieved a minimum F1-score of 97.76% on unseen samples from each dataset, demonstrating its ability to detect knockings amongst noise and other events. Moreover, the estimated fundamental frequencies are comparable to the ones manually computed for the same datasets. These automated approaches can significantly reduce labor and costs associated with manual analysis, making long-term species monitoring more feasible and efficient.

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.

Anxiety symptoms among adults in the U.S. have increased significantly in recent years, with higher prevalence among younger adults in rural areas. Using an experimental design, this industry-academia collaborative study evaluates the impact of a telehealth-enabled multi-sensory environment on anxiety levels among young adults. A sample of 30 participants aged 18-25 was recruited from a university population in the U.S. Anxiety levels were assessed during three five-minute episodes: baseline, exposure to Trier Social Stress Test (TSST), and physical sensory environment with telehealth (pre-recorded guided meditation). Physiological data-- electrodermal activity (EDA), number and duration of eye fixations and saccades-- were collected continuously using sensors (EmbracePlus) and eye-tracking (Tobii Pro Glasses). Subjective data were measured using the State-Trait Anxiety Inventory (STAI) and semi-structured exit interviews. Pairwise comparisons based on complete datasets from 25 participants revealed a significant decrease in EDA (P < .001), mean frequency of saccades (P = .011) and eye fixations (P < .001), and mean state anxiety scores (P < .001) among participants following TSST, indicating the effectiveness of the telehealth-enabled multi-sensory environment in anxiety reduction. Semi-structured interviews also highlighted participants preferences regarding key sensory environment features, including tactile, form, lighting, furniture types, and configurations. Findings from this study will inform the design and implementation of telehealth-enabled multi-sensory environments in the future educational settings to improve anxiety symptoms among young adults.

Minimizing handling of threatened and endangered fish has become increasingly important as populations have dwindled. To minimize handling in morphometric measurements, the HandsFreeFishing program has been developed for juvenile Chinook Salmon (Oncorhynchus tshawytscha). By segmenting a 2D image many morphometric measurements are able to be estimated; from these measurements a weight prediction model is built based on fish whose ground truthed weights were measured using a digital scale. While many segmentation methods may be used, here Metas Segment Anything model (SAM) is employed to produce segmentation masks of raw images. This model is open-source and easily used on any image (of any size) with good performance. In the proposed framework, the user supplies a bounding box around a target fish along with minimal orientation data (left or right facing, upside down or right-side up); the rest of the segmentation, feature extraction, and final weight prediction is completely automated. A main goal of the segmentation is to estimate the surface area of the side profile of the fish. Then, assuming an ellipsoidal shape, this surface area can be related to the volume of the fish, which is directly proportional to the weight. Even on a relatively small dataset of 149 images (fork length 27-90mm) our results confirm the predictive qualities of the morphometric features measured. The model achieved weight prediction with a mean absolute error of 0.16 g with a mean absolute percentage error of 12%, and an r-squared value of 0.99, on fish ranging from 0.31g - 7.74g. The raw images come from a variety of fish viewers, the design of which is relatively inexpensive and reproducible, and, in conjunction with the HandsFreeFishing program, allows for minimal handling compared to traditional length and weight measurement methods.

Background: For early detection of Alzheimer's disease, it is essential to identify individuals showing cognitive performance consistent with the mild cognitive impairment (MCI) range during preliminary screening, ideally using methods that extend beyond conventional cognitive assessments. Non-invasive, easily accessible screening tools applicable in daily life are increasingly needed. Facial expressions, particularly during rest, may offer promising biomarkers for MCI level detection. This study aimed to identify specific facial features associated with MCI level during rest to inform development of facial expression-based screening tools. Methods: Participants were classified into an MCI level group and a healthy control (HC) group based on the Montreal Cognitive Assessment (MoCA) scores. Facial Action Units (AUs) were extracted from video recordings of resting-state facial expressions in 31 individuals with MCI level and 14 HC. Two statistical models were employed: a multilevel zero-inflated beta regression model for intensity of 17 AUs and a multilevel logistic regression model for presence or absence of 18 AUs. Results: In the zero-inflated beta regression, the AU relates to upper lip raiser showed a significant group effect (MCI level vs. HC; p <0.001), remaining significant after multiple comparison correction. The logistic regression revealed significant group differences for the AUs related to lip tightener (p <0.001) and lip suck (p <0.001), both remained significant after multiple comparison correction. Conclusions: Distinctive facial action patterns during rest were observed in individuals with MCI level. These findings highlight the potential of resting-state facial expressions as a basis for novel, unobtrusive screening tools for early MCI level detection.

Sustained attention is an important requirement for high performance in all cognitive processes. Quantifying the level of sustained attention to prevent attention lapses is therefore necessary for effective human-machine interfacing. Furthermore, sustained attention evaluation can help diagnose and treat attention deficit hyperactivity disorders. Attention level can be assessed by brain and heart signals. This study employed functional near infrared spectroscopy (fNIRS) and the heart rate variability (HRV) information extracted from the fNIRS signals to differentiate the rest and three levels of sustained attention states. Sustained attention states are induced by three modified versions of continuous performance tests (CPT). Eight subjects engaged in three sessions of attention tests. fNIRS brain signals were recorded from the right prefrontal and dorsolateral prefrontal cortex. HRV information was then extracted by processing the fNIRS signals. For attention classification, support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF) algorithms with mutual information based feature selection were applied on the fNIRS and HRV data both separately and together. In the classification of the three levels of attention using fNIRS and HRV data, the LDA classifier showed the best performance accuracy of (80.9 {+/-} 1.5%) and (56.2 {+/-} 1.0%), respectively. For two-class classification between the rest and the attention states (all together), the accuracies of (98.9 {+/-} 0.3%), (95.6 {+/-} 1.2%), and (99.5 {+/-} 0.2%) were obtained using the RF classifier on the fNIRS, HRV, and combined data, respectively. These results demonstrate the effectiveness of the HRV data for classifying sustained attention states. Moreover, using the combined fNIRS and HRV data provides better classification accuracy.

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.

We report a 0.18 {micro}m CMOS lab-on-a-chip system that monolithically integrates a passive radio frequency identification (RFID) interface and an 8 x 8 array of capacitance sensors configured for measuring the capacitance change resulting from an overlying biological specimen. This lab-on-CMOS platform is designed to operate wirelessly, first in a harvesting mode in which on-chip power is generated via the inductive coupling of an on-chip antenna to an external antenna, and second, in a sense-and-transmit mode where the capacitance sensor array is scanned and the measured data are transmitted to the external antenna using the same on-chip antenna. This paper presents characterization results of the passive RFID interface and of the sensor core, the latter utilizing several test analytes. The proposed system will facilitate the integration and packaging of a large number of chips in wet environments, paving the way for the inclusion of lab-on-CMOS technology in standard bio-analytical lab practice.

Continuous, non-invasive cardiovascular monitoring is limited by the superficial sensing depth of Photoplethysmography (PPG), which is susceptible to peripheral artifacts. This study evaluates a wearable dual-modality prototype integrating dryelectrode Impedance Plethysmography (IPG) and PPG within a smartwatch form factor. Results from a pilot study (N=2) demonstrate that IPG signals exhibit a temporal lead over PPG across ventral and dorsal sites, supporting its greater penetration depth. During brachial artery modulation, IPG showed superior sensitivity to arterial recovery on the ventral forearm. Furthermore, 60-minute napping sessions revealed that while PPG remained morphologically stable, IPG signals underwent significant evolution, capturing distinct pulsewave archetypes. These findings suggest that wearable IPG provides a high-fidelity window into deep systemic hemodynamics typically reserved for clinical instrumentation.

There are many alternative methods to joystick control for control of Electric Powered Wheelchairs for users with neuromuscular disabilities, such as muscular dystrophy, and spinal cord injuries, such as tetraplegia. However, these methods- which include the sip-and-puff method, head and neck movement, blinking, or tongue movement- hinder social interaction, and are therefore detrimental to user independence. In recent years, research has explored the use of Electromyography (EMG) signals from alternative muscles to control a powered wheelchair, consequently increasing the quality of life of these users. The Auricular Muscles (AM) may be suitable, as they are controlled separately from the facial nerve and are vestigial in humans, making them advantageous for powered wheelchair control for users with tetraplegia. Additionally, they are located around the ear, adding a level of cosmesis when designing wearable sensors and prosthesis. This paper extracts and implements two control strategies from current literature and, for the first time, compares them directly, demonstrating viable implementation approaches for an online EMG-based powered-wheelchair control system. A Support Vector Machine (SVM) was developed and various window lengths were compared, with the most accuracy and real-time effectiveness found at 300ms. A study with three participants demonstrates the feasibility of these methods of control as well as experimental results to guide the potential AM use.

ObjectiveAccurate stride length measurement is essential for assessing functional mobility, yet gold-standard methods remain confined to laboratory settings. This study aimed to develop and validate a computationally efficient, interpretable linear model for predicting stride length using thigh- and shank-mounted inertial measurement units integrated into a wearable neuromodulation sleeve. MethodsData from the sleeve were collected from 29 healthy adults performing walking bouts at four self-selected speeds. Participants traversed a pressure-sensitive gait mat, providing gold standard labels. A linear regression model was developed from engineered features from the kinematics data streams and validated against a held-out test set (n = 6) using leaveone-participant-out cross-validation. ResultsThe final linear model utilized five predictors: participant height, shank range of motion (ROM), thigh ROM, and thigh swing duration metrics. It achieved high predictive accuracy with a mean absolute error (MAE) of 5.98 cm, a mean absolute percentage error (MAPE) of 4.53%, and an R2 of 0.89. The model significantly outperformed naive baseline models (p < 0.05) and performed similarly to more complex non-linear architectures, such as neural networks and random forests. Notably, 88.4% of strides were predicted within 10% of the ground truth. ConclusionA parsimonious linear model leveraging proximal limb kinematics provides accurate and biomechanically interpretable stride length estimation. Low computational demand makes it suitable for real-time, ondevice gait monitoring in wearable assistive technologies, facilitating clinical assessments in real-world environments.