Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems.

The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.

Stress is a complex factor that simultaneously triggers both psychological and physiological changes in humans. However, research on the relationship between the psychological and physiological aspects of stress has been limited. This study assessed psychological factors related to stress using stress questionnaires and evaluated the physiological aspects of stress reactivity and recovery based on the examined characteristics. We administered questionnaires including Perceived Stress Scale (PSS) and the Connor-Davidson Resilience Scale (CD-RISC) to a group of 56 police officers. We also performed stress-inducing experiments, which consisted of the Trier Social Stress Task (TSST) and watching a horror movie. During the stress-inducing experiments, the police officers used wearable sensors to extract heart rate variability (HRV) and we recorded physiological changes through an electrocardiogram (ECG) and electrodermal activity (EDA) signals. We grouped the participants into three groups based on the PSS and CD-RISC scores and analyzed the differences in stress reactivity during stress situations and stress recovery following stress situations among the groups. The results revealed that in the PSS groups, higher perceived stress levels were associated with a decrease in stress reactivity, as indicated by reductions in EDA parameters (SCR std and SCR amplitude) during acute stress situations. In contrast, in the CD-RISC groups, higher resilience levels were associated with increased stress recovery, as indicated by improved recovery abilities in HRV parameters (HR, pNN30, and pNN50) following acute stress situations. The findings of this study provide insights into the physiological changes of the autonomic nervous system based on the psychological factors related to stress in healthy individuals. This research may contribute to future personalized psychological stress assessments.

In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability. This study aims to evaluate the possibility of a stable, flexible, and convenient-to-use bio-key using ECGs. We propose an approach that minimizes biosignal variability using normalization, clustering-based binarization, and the fuzzy extractor, enabling the generation of personalized seeds and offering ease of use. The proposed method achieved a maximum entropy of 0.99 and an authentication accuracy of 95%. This study evaluated various parameter combinations for generating effective bio-keys for personal authentication and proposed the optimal combination. Our research holds potential for security technologies applicable to wearable devices and healthcare systems.

Heart rate variability (HRV) is a non-invasive physiological indicator of autonomic nervous system activity and provides an objective means to assess psychological states such as perceived stress and resilience. This study investigated whether HRV can reflect individual tendencies in terms of perceived stress and resilience, as assessed by the Perceived Stress Scale (PSS), the Connor–Davidson Resilience Scale (CD-RISC), and their combined groups according to the Perceived Stress Scale (PSS), the Connor–Davidson Resilience Scale (CD-RISC), and their combined groups, and examined the autonomic mechanisms underlying these constructs. Ninety-two adults completed both questionnaires and participated in a stress-induction protocol based on the Trier Social Stress Test, consisting of pre-resting, stress session, and post-resting sessions. HRV features across time, frequency, and nonlinear domains were extracted from the resting, reactivity, and recovery phases, analyzed with linear mixed models, and applied in classification models using sequential forward selection and leave-one-subject-out cross-validation. The results showed that high PSS participants exhibited reduced parasympathetic activity primarily at rest, whereas high CD-RISC individuals demonstrated higher parasympathetic activity and autonomic balance across all phases, with greater stability under acute stress and faster recovery. The combined PSS–CD-RISC grouping revealed compounded autonomic dysfunction in the high-stress–low-resilience quadrant. Classification accuracies reached approximately 82% for PSS and CD-RISC separately and 87% when combined, with resilience-related models benefiting particularly from reactivity and recovery features in addition to resting indices. These findings demonstrate that HRV captures distinct autonomic processes underlying stress and resilience, indicating that the two constructions are not redundant but complementary. HRV thus holds promise as a supportive indicator in wearable systems for continuous monitoring and personalized intervention.

Noninvasive blood glucose monitoring is crucial for detecting early dysglycemia, yet continuous glucose monitors remain invasive and costly. Electrocardiogram (ECG) and its derived heart rate variability (HRV) measure may offer a noninvasive indicator of autonomic and cardiac responses associated with acute changes in glucose. In this study, 30 adults underwent a 75 g oral glucose tolerance test with concurrent ECG Holter and interstitial glucose monitoring. From these recordings, HRV and ECG features were extracted. A deep learning classifier with HRV and ECG was then trained to detect hyperglycemia (glucose ≥ 180 mg/dL). Cross-correlation analysis confirmed a significant association between HRV and glucose (Pearson r ~0.65, p < 0.05) when aligning each participant’s data according to individual response delays. The model achieved high classification performance under rigorous temporal validation (accuracy ~89%, area under the receiver operating characteristic curve ~0.89). Saliency analyses revealed that the classifier’s decisions focus on distinct ECG waveform transitions and key HRV features linked to glucose-induced autonomic changes. Overall, acute hyperglycemia elicited discernible changes in HRV and cardiac conduction, supporting the feasibility of this physiologically grounded approach for detecting the acute hyperglycemic phase under controlled conditions. This method holds promise for real-time implementation in wearable devices, enabling early diabetes risk screening.