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.

With the rapid development of networking and computing technology, users can easily store and interact with sensitive information on smart devices. Since smart devices are vulnerable to unauthorized access or theft, the security of personal information is becoming more important. Gait authentication is attracting attention as a continuous or unconscious biometrics method for smart devices. However, various factors, such as gait variability and sensor state by day, can degrade authentication performance. This study proposed a sensor compensation algorithm that overcomes various factors that may occur in the real world and new 2D cyclogram features to improve user authentication performance. The dataset consists of gait data from 20 people wearing wearable sensors on the wrist and thigh over 3 days. A support vector machine (SVM) model was used for the classification of gait authentication. The results showed that the proposed sensor compensation algorithm could obtain a consistent gait signal by transforming the unstable sensor coordinate system into a stable anatomical coordinate system. Also, 2D cyclogram feature sets could be used to effectively discriminate individual gait patterns. The proposed gait authentication has an accuracy of 99.63%, 94.16%, and 94.2% and an equal error rate (EER) of 0.3%, 5.84%, and 5.8% for the same session (day 1), cross session1 (day 2), and cross session2 (day 3), respectively.

Recently, a smart-device-based chest compression depth (CCD) feedback system that helps ensure that chest compressions have adequate depth during cardiopulmonary resuscitation (CPR) was developed. However, no CCD feedback device has been developed for infants, and many feedback systems are inconvenient to use. In this paper, we report the development of a smart-ring-based CCD feedback device for CPR based on an inertial measurement unit, and propose a high-quality chest compression depth estimation algorithm that considers the orientation of the device. The performance of the proposed feedback system was evaluated by comparing it with a linear variable differential transformer in three CPR situations. The experimental results showed compression depth errors of 2.0 ± 1.1, 2.2 ± 0.9, and 1.4 ± 1.1 mm in the three situations. In addition, we conducted a pilot test with an adult/infant mannequin. The results of the experiments show that the proposed smart-ring-based CCD feedback system is applicable to various chest compression methods based on real CPR situations.

Hand functions affect the instrumental activities of daily living. While functional outcome measures, such as a targeted box and block test, have been widely used in clinical settings and provide a useful measure of overall performance, the advent of a wearable Inertial Measurement Unit(IMU)-based system enables the examination of the specific performance and kinematic parameters of hand movements. This study proposed a novel clip-on IMU system to facilitate the clinically fitted measurements of fine-motor finger and wrist joint movements. Clinical validation was conducted with the aim of characterising age-related changes in hand functions, namely grasping, transporting, and releasing blocks. Eighteen young (age 20–31) and sixteen healthy older adults (age 75–89) were evaluated during the box and block test. The results demonstrated that an older age was characterized by slower movements and higher variations and kinematic alterations in the hand functions, such as a larger range of motions at the fingers as well as kinematic trajectories. The proposed IMU system and subsequent validations highlight the value of the performance and kinematics parameters for a more comprehensive understanding of fine-motor finger and wrist movements that could shed light on further implementations in clinical and practical settings.

This study aimed to assess the effectiveness of a novel chest compression (CC) smart-ring-based feedback system in a manikin simulation. In this randomized, crossover, controlled study, we evaluated the effect of smart-ring CC feedback on cardiopulmonary resuscitation (CPR). The learnability and usability of the tool were evaluated with the System Usability Scale (SUS). Participants were divided into two groups and each performed CCs with and without feedback 2 weeks apart, using different orders. The primary outcome was compression depth; the proportion of accurate-depth (5–6 cm) CCs, CC rate, and the proportion of complete CCs (≤1 cm of residual leaning) were assessed additionally. The feedback group and the non-feedback group showed significant differences in compression depth (52.1 (46.3–54.8) vs. 47.1 (40.5–49.9) mm, p = 0.021). The proportion of accurate-depth CCs was significantly higher in the interventional than in the control condition (88.7 (30.0–99.1) vs. 22.6 (0.0–58.5%), p = 0.033). The mean SUS score was 83.9 ± 8.7 points. The acceptability ranges were ‘acceptable’, and the adjective rating was ‘excellent’. CCs with smart-ring feedback could help achieve the ideal range of depth during CPR. The smart-ring may be a valuable source of CPR feedback.