Background and Objectives: Maternal perception of fetal movement is widely used to monitor fetal activity; however, maternally perceived movements may not correspond directly to movements observed by ultrasonography. This study aimed to quantify same-window maternal recognition of ultrasound-observed fetal movements, characterize temporal concordance and directional discrepancies, and examine associated maternal and pregnancy-related factors. Materials and Methods: Pregnant women at ≥28 weeks of gestation underwent simultaneous 20 min fetal-movement assessment using real-time ultrasonography and maternal button-press reporting. The ultrasound observer pressed a designated button whenever fetal movement was visually observed, while participants independently pressed a separate button whenever they perceived fetal movement. Both responses were electronically timestamped using the same system clock and segmented into consecutive 5 s windows. Windows containing an ultrasound-observer button press were classified as ultrasound-positive, and those accompanied by a maternal response within the corresponding window were classified as temporally matched. Same-window maternal recognition was defined as the proportion of ultrasound-positive windows accompanied by a maternal response within the same 5 s analytical window. Associated factors were examined using fractional logistic regression with robust standard errors. Results: A total of 112 valid assessment sessions were analyzed. The median number of ultrasound-positive windows was 26.5 (IQR, 15.5–41.5), compared with 10.5 (IQR, 6.0–18.5) maternal-response windows. Median same-window maternal recognition was 20.3% (IQR, 8.3–38.5%; bootstrap percentile 95% CI, 15.7–23.1%), and median overall temporal concordance was 16.1% (IQR, 7.4–25.3%). Maternal-response window counts were lower than ultrasound-positive window counts in 101 of 112 assessment sessions (90.2%). After adjustment for maternal age, BMI, and fetal presentation, later gestational age was associated with lower same-window maternal recognition (β = −0.063, 95% CI −0.122 to −0.004; p = 0.037), corresponding to an average marginal effect of −1.12 percentage points per week (95% CI −2.15 to −0.09; p = 0.033). Conclusions: Maternal and ultrasound-observed fetal-movement counts showed limited same-window temporal correspondence during the 20 min observation period. A modest inverse association with gestational age was observed in the primary model, although the association was less precise in the denominator-aware and vertex-only sensitivity analyses. These findings reflect temporal matching under the predefined 5 s analytical framework and should not be interpreted as indicating that mothers perceive only a small proportion of fetal movements or that maternal perception becomes less reliable as pregnancy advances.

Wearable vibrotactile devices are increasingly used in virtual reality, teleoperation and neurorehabilitation, but objective EEG evaluation of glove-mediated touch remains limited. We compared EEG recorded during natural object interaction with EEG recorded when tactile feedback was reproduced through a vibrotactile smart glove. Data were collected with an eight-channel wireless headset while participants interacted with three object types (bottle, cube, and sphere) in natural-touch and glove-mediated conditions. An exploratory model trained on natural-touch data and tested on glove-mediated trials yielded rounded cross-condition accuracies of 83%, 78%, and 68% for bottle vs. rest, cube vs. rest, and sphere vs. rest, respectively. These findings suggest that some object-related EEG patterns may carry across conditions, but they should not be interpreted as evidence of physiological equivalence. Supplementary analyses using repeated-run evaluation, band-power and ERD/ERS summaries, temporal-window inspection, and channel ablation were included as cautious interpretability checks. The results underscore the need for larger subject-independent studies, stronger artifact-control pipelines, formal statistical testing, and richer haptic conditions before asserting equivalence to natural touch.

Congenital heart disease (CHD) is a major cause of infant morbidity and mortality, and timely diagnosis remains difficult where advanced imaging is not consistently available. Automated phonocardiogram (PCG) screening can support early triage, but pediatric multiclass classification is limited by subtle acoustic differences and class imbalance. This study proposes a five-class heart-sound framework (Normal, ASD, PDA, PFO, and VSD) using multi-domain feature fusion and a convolutional recurrent neural network (CRNN). A four-channel representation combining Log-Mel, PCEN-Mel, Gramian Angular Summation Field (GASF), and Markov Transition Field (MTF) is modeled with a CNN encoder, bidirectional GRU, and dual temporal pooling. Recordings are segmented with uniform 50% overlap and evaluated under a strict subject-wise protocol with macro-F1-guided model selection. Across five runs on the pediatric ZCHSound dataset, the five-class model achieves a mean macro F1-score of 78.61 ± 1.00% and a mean per-subject accuracy of 87.94 ± 0.87%. A dedicated Normal-versus-Abnormal model reaches 95.74% accuracy. These results suggest that multi-domain fusion with sequence-aware modeling is a promising approach for pediatric CHD auscultation support.