Review of Stress Detection Methods Using Wearable Sensors

From lab to real-life: A three-stage validation of wearable technology for stress monitoring

Stress negatively impacts health, contributing to hypertension, cardiovascular diseases, and immune dysfunction. While conventional diagnostic methods, such as self-reported questionnaires and basic physiological measurements, often lack the objectivity and precision needed for effective stress management, wearable devices present a promising avenue for early stress detection and management. This study conducts a three-stage validation of wearable technology for stress monitoring, transitioning from controlled experimental data to real-life scenarios. Using the controlled WESAD dataset, binary and five-class classification models were developed, achieving maximum accuracies of 99.78 %±0.15 % and 99.61 %±0.32 %, respectively. Electrocardiogram (ECG), Electrodermal Activity (EDA), and Respiration (RESP) were identified as reliable stress biomarkers. Validation was extended to the SWEET dataset, representing real-life data, to confirm generalizability and practical applicability. Furthermore, commercially available wearables supporting these modalities were reviewed, providing recommendations for optimal configurations in dynamic, real-world conditions. These findings demonstrate the potential of multimodal wearable devices to bridge the gap between controlled studies and practical applications, advancing early stress detection systems and personalized stress management strategies.•Stress detection methods were validated using multimodal wearable data in controlled (WESAD) and real-life (SWEET) datasets.•Commercial wearable technologies were reviewed, offering insights into their applicability for practical stress monitoring.

Transdisciplinary Innovations in Athlete Health: 3D-Printable Wearable Sensors for Health Monitoring and Sports Psychology

The integration of 3D printing technology into wearable sensor systems has catalyzed a paradigm shift in sports psychology and athlete health monitoring by enabling real-time, personalized data collection on physiological and psychological states. In this study, not only is the technical potential of these advancements examined but their real-world applications in sports psychology are also critically assessed. While the existing research primarily focuses on sensor fabrication and data acquisition, a significant gap remains in the evaluation of their direct impact on decision-making processes in coaching, mental resilience, and long-term psychological adaptation in athletes. A critical analysis of the current state of 3D-printed wearable sensors is conducted, highlighting both their advantages and limitations. By combining theoretical insights with practical considerations, a comprehensive framework is established for understanding how sensor-based interventions can be effectively incorporated into sports training and psychological evaluation. Future research should prioritize longitudinal studies, athlete-centered validation, and interdisciplinary collaborations to bridge the gap between technological developments and real-world applications. Additionally, the integration of artificial intelligence and advanced biomaterials has significant potential to enhance the reliability and interpretability of sensor-driven interventions. However, without rigorous scientific validation, their effectiveness remains uncertain. This study highlights the importance of a systematic approach in implementing and evaluating 3D-printed wearable sensors in sports psychology.

The brain under pressure: Exploring neurophysiological responses to cognitive stress

Stress is an increasingly dominating part of our daily lives and higher performance requirements at work or to ourselves influence the physiological reaction of our body. Elevated stress levels can be reliably identified through electroencephalogram (EEG) and heart rate (HR) measurements. In this study, we examined how an arithmetic stress-inducing task impacted EEG and HR, establishing meaningful correlations between behavioral data and physiological recordings. Thirty-one healthy participants (15 females, 16 males, aged 20 to 37) willingly participated. Under time pressure, participants completed arithmetic calculations and filled out stress questionnaires before and after the task. Linear mixed effects (LME) allowed us to generate topographical association maps showing significant relations between EEG features (delta, theta, alpha, beta, and gamma power) and factors such as task difficulty, error rate, response time, stress scores, and HR. With task difficulty, we observed left centroparietal and parieto-occipital theta power decreases, and alpha power increases. Furthermore, frontal alpha, delta and theta activity increased with error rate and relative response time, while parieto-temporo-occipital alpha power decreased. Practice effects on EEG power included increases in temporal, parietal, and parieto-occipital theta and alpha activity. HR was positively associated with frontal delta, theta and alpha power whereas frontal gamma power decreases. Significant alpha laterality scores were observed for all factors except task difficulty and relative response time, showing overall increases in left parietal regions. Significant frontal alpha asymmetries emerged with increases in error rate, sex, run number, and HR and occipital alpha asymmetries were also found with run number and HR. Additionally we explored practice effects and noted sex-related differences in EEG features, HR, and questionnaire scores. Overall, our study enhances the understanding of EEG/ECG-based mental stress detection, crucial for early interventions, personalized treatment and objective stress assessment towards the development of a neuroadaptive system.

The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness and challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, and mental health. A thorough literature search identified studies focusing on wearable devices' impact on patient outcomes. In cardiology, wearables have proven effective for monitoring hypertension, detecting arrhythmias, and aiding cardiac rehabilitation. In respiratory health, these devices enhance asthma management and continuous monitoring of critical parameters. Neurological applications include seizure detection and Parkinson's disease management, with wearables showing promising results in improving patient outcomes. In endocrinology, wearable technology advances thyroid dysfunction monitoring, fertility tracking, and diabetes management. Orthopedic applications include improved postsurgical recovery and rehabilitation, while wearables help in early complication detection in oncology. Mental health benefits include anxiety detection, post-traumatic stress disorder management, and stress reduction through wearable biofeedback. In conclusion, wearable health devices offer transformative potential for managing chronic illnesses by enhancing real-time monitoring and patient engagement. Despite significant improvements in adherence and outcomes, challenges with data accuracy and privacy persist. However, with ongoing innovation and collaboration, we can all be part of the solution to maximize the benefits of wearable technologies in healthcare.