Search Results (31)

Tracking highly maneuverable UAVs in complex maritime environments faces multiple challenges: dynamic sea surface interference and low-altitude occlusion make UAV motion trajectories difficult to predict; the strong maneuvering behavior of UAVs imposes high demands on tracking real-time performance and accuracy; and marine environmental noise and unstable shipborne sensor data lead to measurement incompleteness. These factors collectively limit the adaptability and robustness of existing maneuvering UAV tracking methods in complex maritime scenarios. In this context, accurate model recognition for UAVs becomes a key factor in improving tracking performance. Traditional interactive multiple model (IMM) methods rely on probabilistic weighting for model selection, suffering from response delays during UAV maneuvers, and fixed model sets cannot adapt to diverse maneuvering scenarios, resulting in degraded UAV velocity estimation accuracy. To address the above issues, this study proposes a dual long short-term memory (LSTM) cooperative network architecture, targeting the two key problems of incomplete measurements in shipborne radar measurements and inaccurate model probability estimation, and presents corresponding solutions. First, an online-trained LSTM-based incomplete-measurement compensation method is proposed, which achieves real-time fitting and restoration of historical measurement data, providing continuous and stable measurement inputs for shipborne platform UAV tracking in maritime environments. Second, building on this, an LSTM-based UAV model recognition method is developed to directly identify the UAV’s current motion model from multi-frame historical measurement information, effectively reducing maneuvering delays. Furthermore, GPS data is used to generate optimal model probabilities as training labels, thereby improving model reliability. Simulation results show that, under incomplete-measurement conditions, the proposed method can effectively reconstruct missing measurements and ensure measurement continuity. Under complete-measurement conditions, the proposed LSTM-based model recognition method significantly improves UAV model recognition accuracy and three-dimensional velocity estimation performance, demonstrating the effectiveness of deep learning for maneuvering UAV tracking from shipborne platforms in maritime environments. Full article

Background/Objectives: Prognostication in ICU patients with documented coma or unresponsiveness is a high-stakes task that informs escalation of care, goals-of-care discussions, and family counselling. Conventional scores are often based on static snapshots and may not reflect early physiological evolution in heterogeneous real-world ICU populations. Routine arterial blood gases (ABG) and SpO₂ are repeatedly measured during early ICU care and may capture clinically meaningful trajectories that can be leveraged by explainable machine learning. To develop and internally validate exploratory, time-updated explainable machine-learning models for ICU outcome in ICU patients with clinically documented coma or unresponsiveness using routine ABG/SpO₂ measurements and physiological trajectories available at admission, 24 h, and 72 h, and to evaluate whether trajectory information adds prognostic information within a staged internal-validation framework. Methods: We conducted a retrospective single-centre study of 108 adult ICU patients with clinically documented coma or unresponsiveness. Predictors included demographics, comorbidity burden, COVID-19 status, baseline ABG/SpO₂ at ICU admission, inflammatory and coagulation biomarkers, and derived ABG/SpO₂ trajectory variables at 24 h and 72 h. Trajectory variables were defined as changes from admission to 24 h and to 72 h and were retained as missing when follow-up measurements were unavailable. The primary ICU-course outcome was ICU death versus transfer to ward. Three staged models were evaluated: Model A using baseline variables, Model B adding 24 h trajectory features, and Model C adding 72 h trajectory features. For each stage, models were analyzed with and without the derived respiratory_support index; models excluding respiratory_support were treated as the main interpretive analyses. Logistic regression, random forest, and gradient boosting (XGBoost) classifiers were assessed using repeated stratified 5-fold cross-validation with 20 repeats and aligned out-of-fold predictions. Performance was reported using AUC-ROC, precision–recall AUC, Brier score, and operating-point metrics; clinical utility was examined with decision-curve analysis. Model interpretation used SHAP and partial dependence plots. Robustness analyses included feature-exclusion sensitivity analysis for respiratory_support and a label-permutation sanity check. Results: ICU mortality was 65.7% (71/108). Follow-up ABG completeness was 75.9% at 24 h and 61.1% at 72 h. Because respiratory_support summarized the highest support level during the first 72 h and strongly separated outcome groups, models excluding respiratory_support were treated as the primary interpretive analyses. In the primary NoRS logistic-regression models, discrimination was moderate-to-strong, with AUC-ROC 0.822 for Model A_noRS, 0.848 for Model B_noRS, and 0.895 for Model C_noRS; bootstrap 95% confidence intervals were 0.739–0.897, 0.766–0.919, and 0.830–0.951, respectively. Measurement-availability sensitivity analyses and simple benchmark models were added to contextualize trajectory-related performance. Respiratory_support-enriched models were retained only as secondary severity-aware analyses, not as admission-only prediction models. Label permutation reduced discrimination toward chance (AUC ≈ 0.55). SHAP and partial-dependence analyses identified oxygenation variables, inflammatory burden, acid–base status, and ΔPaO₂ at 72 h as clinically coherent contributors to predicted risk; when included, respiratory_support dominated feature attribution, consistent with its role as an organ-support intensity marker. Conclusions: In ICU patients with clinically documented coma or unresponsiveness, explainable machine-learning models using routine ABG/SpO₂ trajectories within the first 72 h are feasible and may provide time-updated prognostic information, but the incremental value of trajectory-enriched models over simpler admission-only benchmarks remains unproven. Trajectory-enriched NoRS models retained meaningful discrimination after removing organ-support severity, suggesting a possible physiologically meaningful signal beyond support intensity alone, although definitive incremental value over parsimonious admission-only benchmarks was not established. These findings should be interpreted as exploratory and internally validated only; they do not establish a deployable ICU mortality score, do not demonstrate superiority over established ICU severity scores, and require external validation in larger multicentre cohorts before clinical deployment. Full article

Multi-day travel diaries are often associated with high rates of partial completion, limiting their value for activity-based demand modeling. This paper develops a probabilistic framework that encodes daily activity sequences, clusters them with a Gaussian Mixture Model (GMM) to obtain soft (probabilistic) memberships, and predicts missing days through a constrained Lagrangian regression that guarantees valid probability distributions. Applied to the New York City Citywide Mobility Survey for 2019 and 2022, the soft-clustering approach achieves an RMSE as low as 0.17—substantially outperforming hard-clustering baselines (16–36% accuracy)—and reconstructs population-level time-use profiles with approximately 5–6% mean absolute error. Results show that post-pandemic activity patterns are more home-anchored and less varied, with pronounced socioeconomic divergence in recovery trajectories. Full article

Background/Objectives: Chronic low back pain (CLBP) affects approximately 20% of the global population and is a leading cause of years lived with disability. Long-term, real-world evidence for inhaled cannabis in patients refractory to conventional multimodal therapy remains scarce. We assessed the five-year efficacy and safety of inhaled cannabis in CLBP patients who had documented failure of ≥1 year of opioid analgesics, anticonvulsants, antidepressants, NSAIDs, and physiotherapy, with each patient serving as their own historical control. Methods: We analyzed prospectively collected clinical data from 241 consecutive adults with treatment-refractory CLBP (mean age 49.3 ± 14.9 years; 37.8% female; mean pain duration 15.1 years) initiated on inhaled medical cannabis (predominantly smoking, THC 4–22%, CBD 2–22%) in a single-center tertiary orthopedic clinic between 2020 and 2025 (Hasharon Hospital, Rabin Medical Center, Israel; IRB protocols 0807-21-RMC and 0634-25-RMC). Year-0 outcomes during conventional therapy were compared with outcomes at Years 1–5 on cannabis. Primary outcomes were the Numeric Rating Scale (NRS), Oswestry Disability Index (ODI), and Brief Pain Inventory severity/interference (BPI-S/BPI-I). Concomitant-medication trajectories were a secondary outcome. The primary analysis was a mixed model for repeated measures (MMRM) with random intercept and slope, REML estimation, and time as a categorical fixed effect. Multiple imputation (MAR, m = 20, Rubin’s rules) was the primary missing-data approach; complete-case and tipping-point pattern-mixture sensitivity analyses were used. A multivariate Hotelling T² provided a joint test across the four correlated PROMs. Concomitant-medication discontinuation was modeled with GEE logistic regression and exact McNemar tests. Time to discontinuation was estimated by Kaplan–Meier and Cox regression. The Bonferroni-adjusted significance threshold for the four primary outcomes was α = 0.0125. BioWell gas-discharge-visualization (GDV) parameters were exploratory only. Results: Of 241 patients, 238 (98.8%) provided Year-5 data and 224 (92.9%) remained on cannabis at Year 5; only five patients (2.1%) discontinued for adverse events or inefficacy. All four primary PROMs improved markedly and durably. MMRM-estimated Year-5 minus Year-0 changes were: NRS −5.36 (95% CI −5.65, −5.07), ODI −17.68 (95% CI −19.73, −15.63), BPI-S −6.73 (95% CI −6.99, −6.47), and BPI-I −3.41 (95% CI −3.65, −3.16); all four contrasts had |z| ≥ 16.9 and p < 10⁻²⁰. MI-pooled estimates were within 0.05 of MMRM (FMI < 0.03 for all outcomes). Hotelling T² was F(4, 232) = 872.8, p < 10⁻²⁰. At Year 5, 89.2% achieved ≥30% NRS reduction, 77.2% ≥ 50%, and 93.4% met the NRS minimum clinically important difference (MCID); ODI MCID 65.6%, BPI-S MCID (≥1 pt) 98.3%, BPI-I MCID (≥1 pt) 91.3%. Concomitant opioid use fell from 100% at baseline to 4.6% at Year 5 (within-patient absolute risk reduction 95.4%, McNemar exact p = 1.16 × 10⁻⁶⁹), NSAID from 100% to 7.1%, SSRI/SNRI from 80.5% to 5.4%, and gabapentinoid from 38.6% to 2.5%. The ARR-derived NNT for opioid discontinuation was 1.05; this NNT is referenced to each patient’s own documented maximal-conventional-therapy state and is not equivalent to a between-arm randomized-trial NNT. Cannabis dose × time interaction was consistent with no pharmacological tolerance (β = −0.0044 per gram-month per year, p = 0.074). Across 1205 patient-years of cannabis exposure (calculated as 241 patients × 5 follow-up years from Year 1 through Year 5; baseline Year 0 represents pre-cannabis state and is not included in person-time on cannabis), 1338 organ-system AE events were recorded at 1.110/patient-year (Poisson 95% CI 1.05–1.17); 99.8% of graded events were mild (grade 1), with ocular (476 events, 0.40/PY), cognitive (460, 0.38/PY), and gastrointestinal (368, 0.31/PY) reactions predominating. The Year-3 retention dip reflected a documented telemedicine-clinic phenomenon during 2022–2024, with patients returning to in-person follow-up by Year 4–5. BioWell GDV discriminated NRS ≥ 4 only at chance level (BWS AUC 0.574, 95% CI 0.54–0.60; BWV AUC 0.51). Conclusions: In a treatment-refractory CLBP cohort with five-year longitudinal follow-up, inhaled cannabis was associated with large, sustained, and statistically robust improvements in pain, disability, and pain interference, accompanied by near-total displacement of opioids, NSAIDs, antidepressants, and gabapentinoids. These observational associations, although mechanically less susceptible to bias for the binary medication-discontinuation outcomes than for self-reported PROMs, cannot be interpreted causally in the absence of a concurrent randomized control arm and may reflect a combination of pharmacological effect, regression to the mean from a high pre-treatment baseline, expectancy and self-selection effects intrinsic to an actively chosen open-label therapy, and secular trends in pain reporting. The within-patient benefit-risk profile—ARR-derived NNT ≈ 1 for opioid sparing against a predominantly mild adverse-event burden—supports consideration of cannabis as a potentially clinically meaningful, opioid-sparing option in patients who have failed multimodal conventional therapy, pending confirmation in randomized comparative trials. Full article

Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiological data. Such interruptions fragment the signals, resulting in discontinuities that compromise their overall integrity. Therefore, reconstructing missing values and preserving signal continuity are essential for ensuring the stable computation of signal trajectories and the accuracy of derived cerebrovascular indices. Methods: To address this issue, this systematic scoping review aimed to identify and synthesize existing interpolation and imputation strategies for handling missing segments in continuous pressure-flow cerebral bio-signals. Following the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search of five electronic databases was conducted from their inception to 24 September 2024, and updated on 16 June 2025, using a detailed search string. Results: The initial searches yielded 19,403 results, and 8 studies were filtered and included in the review. All included studies employed interpolation techniques, such as linear and spline interpolation algorithms, to correct distorted signal segments. However, none of the included studies directly utilized interpolation or imputation strategies to reconstruct or completely fill missing data segments. Conclusions: This reveals a critical knowledge gap, as no study has systematically addressed the utilization of interpolation or imputation strategies for missing segments in pressure-flow cerebral bio-signals. Therefore, this systematic review emphasizes the need for specialized methodologies and standardized frameworks to enable reliable recovery of missing data segments in pressure-flow cerebral bio-signals, which is critical for advancing real-time neurocritical care monitoring and experimental neuroscience/psychological research. Significance: This systematic review lays the groundwork for future research into physiologically informed interpolation and imputation strategies for pressure-flow cerebral bio-signals in clinical and research applications. Full article

Continuous maize cropping is often associated with yield decline and soil degradation, yet the temporal responses of rhizosphere bacterial communities to prolonged monocropping remain incompletely understood. Here, we used a continuous maize cropping chronosequence representing 0, 1, 2, 3, 6, 7, and 8 years of cropping to evaluate soil physicochemical properties, maize yield, rhizosphere bacterial community composition, and BugBase-predicted phenotypes using 16S rRNA gene amplicon sequencing. Available potassium declined progressively with cropping duration, whereas alkali-hydrolyzable nitrogen (AN) increased and available phosphorus (AP) changed nonlinearly. Soil pH declined in the later stages of the chronosequence. Maize yield declined progressively with prolonged cropping, with reduction of 46–55% in the 6–8 years treatments relative to earlier within-plot peaks. Bacterial alpha diversity changed nonlinearly, with Shannon diversity peaking at C3, declining at C6, and partially recovering at C7–C8. Because years 4 and 5 were not sampled, the exact shape of the transition between C3 and C6 remains unknown. Community composition also shifted with cropping duration, including a relative decline in Proteobacteria and enrichment of Actinobacteria in the longer-duration treatments. At the genus level, Arthrobacter increased in the later stages of the chronosequence. Redundancy analysis indicated broad associations between community composition and soil variables, although the phylum-level model was only marginally significant. BugBase-predicted phenotypes also varied across treatments, but these functional inferences should be interpreted cautiously because they were derived from 16S-based predictions. Overall, our findings support nonlinear changes in rhizosphere bacterial communities along the continuous maize cropping chronosequence and suggest an unresolved transition between C3 and C6, followed by partial stabilization at later stages. However, due to the missing data for years 4–5 and the inherent limitations of the chronosequence design, the existence and timing of a proposed mid-term transition remain tentative. These findings highlight the need for complete annual sampling to resolve successional trajectories. Full article

Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation–maximization (EM) framework is proposed in which data association variables and motion model variables are jointly modeled as latent variables. These variables are estimated through iterative updates based on the loopy belief propagation (LBP) algorithm and the interacting multiple model (IMM) filtering and smoothing algorithms to generate high-confidence tracklets. Then, a delayed decision-making strategy based on the multi-hypothesis approach is employed to associate these tracklets into complete target trajectories. The resulting algorithm is named IMM-TrackletMHT. The performance of the IMM-TrackletMHT algorithm is evaluated and compared with several baseline algorithms in simulated scenarios under different clutter rates and detection probabilities. The simulation results demonstrate that the proposed algorithm consistently outperforms the baseline methods in terms of tracking accuracy, exhibits strong robustness to variations in the operating environment, and achieves higher computational efficiency in multi-scan measurement processing, thereby demonstrating the effectiveness and superiority of the proposed tracklet generation approach for maneuvering MTT. Full article

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications. Full article

Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (CNN–BiLSTM–Attention) model. By integrating multi-source data, including meteorological parameters (e.g., temperature, humidity, wind speed) and aircraft trajectory data (e.g., altitude, longitude, latitude), the framework achieves high-precision prediction of aircraft noise. The Haversine formula and inverse distance weighting (IDW) interpolation are employed to effectively supplement missing data, while spatiotemporal alignment techniques ensure data consistency. The CNN–BiLSTM–Attention model leverages the spatial feature extraction capabilities of CNNs, the bidirectional temporal sequence processing capabilities of BiLSTMs, and the context-enhancing properties of the attention mechanism to capture the spatiotemporal characteristics of noise. The experimental results indicate that the model’s predicted mean value of 68.66 closely approximates the actual value of 68.16, with a minimal difference of 0.5 and a mean absolute error of 0.89%. Notably, the error remained below 2% in 91.4% of the prediction rounds. Furthermore, ablation studies revealed that the complete CNN–BiLSTM–AM model significantly outperformed single-structure models. The incorporation of the attention mechanism was found to markedly enhance both the accuracy and generalization capability of the model. These findings highlight the model’s robust performance and reliability in predicting aviation noise. This study provides a scientific basis for effective aviation noise management and offers an innovative solution for addressing noise prediction problems under data-scarce conditions. Full article

High-completeness ship trajectory data are critical for analyzing navigation behavior characteristics and enhancing effective maritime management. To address the common issues of prolonged AIS data loss for small coastal vessels in nearshore waters, an intelligent trajectory repairing model based on the artificial potential field-enhanced A* algorithm (APF-A*) has been proposed. Kernel density estimation was utilized to quantify the distribution characteristics of vessels, thereby constructing an attractive potential field based on historical trajectories and a repulsive potential field based on coastal terrain. Speed distribution characteristics were extracted from historical trajectory points in different regions; on the basis of this, the A* algorithm, integrated with attractive and repulsive fields, was proposed to repair missing trajectory segments. Based on the speed distribution characteristics, time intervals, and distance information, the temporal information of the vessel trajectories was effectively reconstructed. The present study fills the research gap in AIS data reconstruction for small coastal vessels in complex coastal waters. A case study has been conducted in Luoyuan Bay, Fujian Province, China, to further validate the proposed model. The results demonstrate that the trajectory repairing model based on the artificial potential field-enhanced A* algorithm outperformed other models. More specifically, the Hausdorff Distance and Dynamic Time Warping (DTW) metrics decreased by 81.67% and 91.56%, respectively. The present study shares useful insights into intelligent maritime management and further supports accident prevention in coastal waters. Full article

Multi-object tracking (MOT) in satellite videos presents significant challenges, including small target sizes, dense distributions, and complex motion patterns. To address these issues, we propose OITrack, an improved tracking framework that integrates a Trajectory Completion Module (TCM), an Adaptive Kalman Filter (AKF), and an Iterative Expansion Intersection over Union Strategy (I-EIoU) strategy. Specifically, TCM enhances temporal continuity by compensating for missing trajectories, AKF improves tracking robustness by dynamically adjusting observation noise, and I-EIoU optimizes target association, leading to more accurate small-object matching. Experimental evaluations on the VIdeo Satellite Objects (VISO) dataset demonstrated that OITrack outperforms existing MOT methods across multiple key metrics, achieving a Multiple Object Tracking Accuracy (MOTA) of 57.0%, an Identity F1 Score (IDF1) of 67.5%, a reduction in False Negatives (FN) to 29,170, and a decrease in Identity Switches (ID switches) to 889. These results indicate that our method effectively improves tracking accuracy while minimizing identity mismatches, enhancing overall robustness. Full article

Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks detail and a triangulated model can hide gaps. The challenge is to generate a dense cloud with field-deployable tools. Traditional dense reconstruction methods can take several dozen hours on low-capacity systems like laptops or embedded units. To speed up this process, we propose building the dense cloud incrementally within an SfM framework while incorporating data redundancy management to eliminate recalculations and filtering already-processed data. The method evaluates overlap area limits and computes depths by propagating the matching around SeaPoints—the keypoints we design for identifying reliable areas regardless of the quality of the processed underwater images. This produces local partial dense clouds, which are aggregated into a common frame via the SfM pipeline to produce the global dense cloud. Compared to the production of complete dense local clouds, this approach reduces the computation time by about 70% while maintaining a comparable final density. The underlying prospect of this work is to enable real-time completeness estimation directly on board, allowing for the dynamic re-planning of the acquisition trajectory. Full article

The traditional maneuver decision-making approaches are highly dependent on accurate and complete situation information, and their decision-making quality becomes poor when opponent information is occasionally missing in complex electromagnetic environments. In order to solve this problem, an autonomous maneuver decision-making approach is developed based on deep reinforcement learning (DRL) architecture. Meanwhile, a Transformer network is integrated into the actor and critic networks, which can find the potential dependency relationships among the time series trajectory data. By using these relationships, the information loss is partially compensated, which leads to maneuvering decisions being more accurate. The issues of limited experience samples, low sampling efficiency, and poor stability in the agent training state appear when the Transformer network is introduced into DRL. To address these issues, the measures of designing an effective decision-making reward, a prioritized sampling method, and a dynamic learning rate adjustment mechanism are proposed. Numerous simulation results show that the proposed approach outperforms the traditional DRL algorithms, with a higher win rate in the case of opponent information loss. Full article

Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM’s superior accuracy, showcasing lower errors in multiple metrics. TRFM’s alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method’s precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency. Full article

Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory and future prognosis, there is a critical need to quantify the potential variability that may be present in the underlying sensor data due to variations in the smartphone hardware and software used by large population. Using sensor data collected in real-world settings from 3000 participants’ smartphones for up to 84 days, we compared differences in the completeness, correctness, and consistency of the three most common smartphone sensors—the accelerometer, gyroscope, and GPS— within and across Android and iOS devices. Our findings show considerable variation in sensor data quality within and across Android and iOS devices. Sensor data from iOS devices showed significantly lower levels of anomalous point density (APD) compared to Android across all sensors (p  <  1 × 10⁻⁴). iOS devices showed a considerably lower missing data ratio (MDR) for the accelerometer compared to the GPS data (p  <  1 × 10⁻⁴). Notably, the quality features derived from raw sensor data across devices alone could predict the device type (Android vs. iOS) with an up to 0.98 accuracy 95% CI [0.977, 0.982]. Such significant differences in sensor data quantity and quality gathered from iOS and Android platforms could lead to considerable variation in health-related inference derived from heterogenous consumer-owned smartphones. Our research highlights the importance of assessing, measuring, and adjusting for such critical differences in smartphone sensor-based assessments. Understanding the factors contributing to the variation in sensor data based on daily device usage will help develop reliable, standardized, inclusive, and practically applicable digital behavioral patterns that may be linked to health outcomes in real-world settings. Full article