Accurate environmental monitoring in outdoor and indoor settings is critical for exposure assessment in environmental and public health research. Conventional methods, predominantly relying on high-end instruments or laboratory analyses, face limitations in real-world applications due to their high cost and inflexibility. Recent advances in low-cost sensor technologies have enabled more adaptable monitoring. This study systematically reviews research utilizing low-cost sensors for environmental monitoring in real-world settings across China. A literature search was performed using the Web of Science database, resulting in the inclusion of 43 eligible studies out of 31,003 initially identified records. These studies primarily investigated air pollution (17 studies), noise (14), light (7), and water pollution (5). Results reveal that air and noise pollution were the most extensively examined factors. Nevertheless, the reviewed studies exhibited notable shortcomings, including limited geographical/thematic coverage, inadequate reliability validation, small sample sizes (typically under 100 participants), and short durations (often under one month). This review discusses these challenges and suggests future research directions. By synthesizing current practices and identifying gaps, this work offers valuable insights to guide the design of future sensor-based environmental monitoring projects and inform the selection of suitable sensors.

Resting-state electroencephalography (EEG) offers a scalable approach to quantify affective neurophysiology. Using data from eighty-one young adults aged 18–25 years, this study proposes a systematic framework to address limitations in the integration of multidimensional EEG features and the insufficient characterization of their spatial distribution. We first extracted a comprehensive set of time-, frequency-, time–frequency, nonlinear, and connectivity features to capture the multiscale neural patterns associated with depression symptom. A statistical feature-selection procedure based on the two-sided Mann–Whitney U test was applied to identify robust discriminative EEG markers. Using the full 64-channel feature set, the constructed model achieved strong overall performance (AUC = 0.958), providing a reliable baseline for subsequent spatial analyses. The selected features were then mapped onto electrode locations to characterize their spatial distribution, and a progressive channel-reduction analysis was performed to determine the minimal electrode set needed to preserve predictive performance. The channel-reduction analysis indicates that using only 5, 8, or 12 electrodes still maintains high performance, with AUC values of 0.879, 0.918, and 0.948, respectively. This framework provides a reproducible foundation for developing low-density, wearable EEG systems for mental-health monitoring.

Cognitive Frailty (CF), characterized by the co-occurrence of Physical Frailty (PF) and Mild Cognitive Impairment (MCI), is increasingly recognized as a critical predictor of adverse health outcomes in aging populations. Despite its clinical significance, early identification of CF remains challenging due to heterogeneous diagnostic criteria and reliance on resource-intensive assessments. This systematic review synthesizes current evidence on sensor-based digital biomarkers for CF detection across eight databases following PRISMA 2020 guidelines. A combined qualitative and quantitative synthesis is conducted based on 20 eligible studies. Key findings reveal that Inertial Measurement Units (IMUs) are the most frequently utilized sensors (n = 6 studies), primarily capturing gait and motor function parameters under various functional tasks. Among digital biomarkers, gait features, particularly Stride Length under single-task conditions and Velocity under dual-task or single-task conditions, show significant associations with CF. Body composition metrics, particularly Appendicular Skeletal Muscle Mass Index (ASM), and physical activity parameters, such as Moderate-to-Vigorous-intensity Physical Activity (MVPA), show consistent negative associations with CF, while cardiovascular indices like Cardio-Ankle Vascular Index (CAVI) reveal positive correlations. Eighteen studies develop a total of 23 CF-related models, of which only four focus on predictive classification. These four studies report ten distinct models, with AUCs ranging from 0.696 to 0.911 and accuracy from 0.480 to 0.863. A quantitative synthesis of six models from three studies yields pooled estimates of sensitivity at 0.81 (95% CI: 0.55-0.94) and specificity at 0.80 (95% CI: 0.49-0.95), suggesting moderate-to-good discriminative performance despite notable heterogeneity in diagnostic definitions, sample sizes, sensor types, and modeling approaches. These results systematically map the evolving landscape of digital biomarkers for CF, emphasizing the need for standardized sensor protocols and rigorous validation to advance clinical translation.

Mild Cognitive Impairment (MCI) involves cognitive decline beyond age-expected norms without impairing daily function. Crucially, it often precedes dementia, making early identification vital for timely intervention. MCI identification remains challenging with conventional clinical methods. Existing studies have shown a significant association between motion behavior and cognitive performance, highlighting the promise of Inertial Measurement Units (IMUs) for motion-based portable MCI screening. Although some studies in the field of MCI identification have explored feature extraction based on IMU data, these studies mostly focus on time-domain and frequency-domain features and have not systematically analyzed features from multiple domains, which may limit a comprehensive understanding of MCI-related movement patterns for screening. Additionally, the reliance on feature importance rankings in these studies often lacks the interpretability needed for direct application in clinical decision-making, thus limiting their clinical applicability. This study proposes an integrated framework combining multi-domain gait analysis with explainable machine learning.Waist-mounted IMU was utilized for data collection during standardized Short Physical Performance Battery (SPPB) assessments in older adults. Comprehensive features were extracted across four domains, including time, frequency, nonlinear and time-frequency. A hybrid feature selection strategy was then employed for reducing dimensionality while preserving both linear and nonlinear indicators relevant to MCI. Additionally, association rule mining further identified key diagnostic feature combinations strongly linked to MCI. We conducted a field study and collected real-world data for performance evaluation. Our proposed approach achieved a mean test area under the ROC curve (AUC) of 0.897 and a mean test accuracy of 0.828 with the SVM classifier. The approach demonstrates significant potential for cost-effective and generalizable IMU-based MCI screening tools.