Introduction Physiological monitoring of soldiers can indicate combat readiness and performance. Despite demonstrated use of wearable devices for HR monitoring, commercial options lack desired military features. A newly developed OMNI monitor includes desired features such as long-range secure data transmission. This study investigated the accuracy of the OMNI to measure HR via accuracy of R-R interval duration relative to research-grade ECG and commercial products. Methods 54 healthy individuals (male/female=37/17, age=22.2±3.6 years, height=173.0±9.1 cm, weight=70.1±11.2 kg) completed a submaximal exercise test while wearing a reference ECG (Biopac) and a randomly assigned chest-based monitor (OMNI, Polar H10, Equivital EQ-02, Zephyr Bioharness 3). All participants also wore two wrist-based photoplethysmography (PPG) devices, Garmin fēnix 6 and Empatica E4. Bland-Altman analyses of agreement, concordance correlation coefficient (CCC) and root-mean-squared error (RMSE) were used to determine accuracy of the OMNI and commercial devices relative to Biopac. Additionally, a linear mixed-effects model evaluated the effects of device and exercise intensity on agreement. Results Chest-based devices showed superior agreement with Biopac for measuring R-R interval compared with wrist-based ones in terms of mean bias, CCC and RMSE, with OMNI demonstrating the best scores on all metrics. Linear mixed-effects model showed no significant main or interaction effects for the chest-based devices. However, significant effects were found for Garmin and Empatica devices (p<0.001) as well as the interaction effects between both Garmin and Empatica and exercise intensity (p<0.001). Conclusions Chest-based ECG devices are preferred to wrist-based PPG devices due to superior HR accuracy over a range of exercise intensities, with the OMNI device demonstrating equal, if not superior, performance to other commercial ECG monitors. Additionally, wrist-based PPG devices are significantly affected by exercise intensity as they underestimate HR at low intensities and overestimate HR at high intensities.

Electrocardiogram (ECG) devices are commonly used to monitor heart rate (HR) and heart rate variability (HRV), but their signal quality under non-upright or torso-dominant activities may suffer due to motion artifact and interference from surrounding musculature. We compared ECG signal quality during treadmill walking, circuit training, and an obstacle course using three chest-worn commercial devices (Polar H10, Equivital EQ-02, and Zephyr BioHarness 3.0) and a multi-lead ECG monitor (BIOPAC). Signal quality was quantified using the beat signal quality index (SQI), and HR data were rejected if SQI fell below 0.7 or if values were physiologically implausible. Signal rejection rate was calculated as the proportion of low-quality observations across device and activity type. Significant effects of both device (p < 0.001) and activity (p < 0.001) were observed, with greater signal rejection during the obstacle course and circuit training compared to treadmill walking (p < 0.01). The Zephyr exhibited significantly higher rejection rates than the Polar (p = 0.018) and BIOPAC (p = 0.017), while the Polar showed lower average rejection rates across all activities. These findings underscore the importance of including dynamic, non-upright tasks in ECG validation protocols and suggest that certain commercial devices may be more robust under realistic conditions.

Introduction: Maximal heart rate (MHR) is a key measure for cardiorespiratory exercise prescription yet is often estimated using age-based prediction equations. The accuracy of these equations may vary by individual characteristics, including cardiorespiratory fitness (CRF), but limited research has examined predictive accuracy across CRF levels. Therefore, we evaluated the accuracy of seven commonly used MHR prediction equations in adults with varying CRF to assess whether prediction error differs by fitness level. Materials and methods: Data from 230 healthy adults (76% male, mean age 38.5 ± 12.3 years) who completed maximal graded exercise tests between 2019 and 2024 were analyzed retrospectively. Predicted MHR values were calculated using the Fox, Tanaka, Gellish, Arena, Åstrand, Nes, and Fairbairn equations. Linear mixed-effects models (LMM) tested the influence of VO₂max and its interaction with prediction equation on error, with sex included as a covariate. Estimated marginal means and slopes were extracted, with pairwise contrasts adjusted by the Tukey method. Prediction equation accuracy was evaluated by comparing predicted and measured MHR using Bland-Altman analyses, and metrics including mean absolute error (MAE), root mean square error (RMSE), and intraclass correlation coefficients (ICC). Results: LMM indicated a significant main effect of prediction equation on error ( p < 0.001) and a significant equation × VO₂max interaction ( p = 0.015), though neither sex ( p = 0.49) nor VO₂max ( p = 0.18) alone influenced error. The conditional R ² for the LME model was 0.70, with a marginal R ² of 0.02. Post-hoc linear regressions showed higher VO₂max was associated with greater prediction error for several equations in males, but not females, with a small amount of variance explained ( R ² ≤ 0.06). Agreement analyses indicated small mean biases across equations (–3 to +6 bpm) but wide limits of agreement (~±18–24 bpm). Arena, Tanaka and Gellish equations showed the lowest MAE and RMSE. Among the equations, Fox showed the most stable performance across MHR ranges, being the only formula without proportional bias across the sample. Discussion: The findings indicate that CRF had only a limited influence on MHR prediction error, with small associations observed in males but not females, reinforcing age as the primary determinant of MHR. Although some equations (e.g., Tanaka, Gellish, Arena, Fox) performed better than others across agreement metrics, none demonstrated high individual level accuracy, which highlights a lack of precision when estimating MHR for exercise prescription and monitoring purposes. Future work should explore more individualized modeling approaches, though adjusting for CRF alone may not substantially improve prediction accuracy in healthy adults.

Introduction Physical fatigue is a key determinant of operational readiness in the physically demanding occupations of tactical athletes. Specific hormonal, immune, and enzymatic biomarkers have been proposed for fatigue assessment, but their reliability can be affected by external factors. This study aimed to explore the predictive accuracy of targeted stress-related small molecules to proteins identified via untargeted salivary proteomics in classifying physical fatigue. Methods Ten recreationally active adults (6M, 4F) completed a fatiguing protocol designed to simulate common operational movements and intensities. Saliva samples were collected pre- and post-protocol and analyzed for targeted biomarkers using commercial immunoassays and for untargeted proteins via liquid chromatography-mass spectrometry. Machine learning models were trained to classify pre- vs post-exercise state using these biomarkers, with performance assessed through sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results Targeted small molecules achieved an overall model accuracy of 86%, with immunoglobulin A and uric acid demonstrating the highest predictive power. However, a proteomic panel of four proteins (ATP1B1, STOML2, PGLYRP2, FH) exhibited superior performance, with 95% classification accuracy and improved sensitivity. Pathway analysis revealed that these proteins were involved in mitochondrial function, immune regulation, and metabolic adaptation, suggesting their role in fatigue-associated physiological changes. Conclusions Salivary proteomics identified biomarkers with greater sensitivity and specificity for detecting physical fatigue than targeted stress-related molecules within this sample. These findings support the potential for non-invasive proteomic monitoring of fatigue in operational and athletic settings. Future studies should validate these findings in larger, more diverse populations and assess their applicability to chronic fatigue monitoring.

Purpose: Physical fatigue alters neuromuscular function and movement mechanics and is associated with reduced performance and increased injury risk. Wearable sensing technologies offer a potential means of discriminating fatigue outside the laboratory; however, most prior approaches rely on a single sensor modality, which may limit robustness across individuals and tasks. The purpose of this study was to evaluate the feasibility of classifying physical fatigue state using a fusion of physiological and biomechanical measurements during treadmill running.,Methods: Ten healthy, physically active adults completed baseline and fatigued running trials while joint kinematics, lower-extremity electromyography (EMG), and ground reaction forces were collected. Fatigue was induced using a standardized multi-stage protocol and defined using a combination of subjective exertion and performance decline. Features extracted from gait, kinematic, EMG, and force signals were down-selected using statistical filtering, correlation analysis, and recursive feature elimination. Classification models were evaluated using leave-one-participant-out cross-validation to assess subject-independent performance., Results: Feature selection reduced an initial set of 912 features to 15 features, primarily derived from EMG signals. Linear discriminant analysis and support vector machine classifiers achieved average cross-validation accuracies exceeding 80%. Across models, EMG-derived features consistently contributed most strongly to fatigue discrimination.,Conclusion: These findings demonstrate the feasibility of classifying physical fatigue state using multimodal wearable physiological and biomechanical data in a controlled laboratory setting. The results highlight the potential value of incorporating muscle activity measurements into future wearable fatigue-monitoring systems, while underscoring the need for larger-scale studies to evaluate generalizability across populations and movement tasks.