Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition.

At the population level, astrocytes dynamically interact with neurons.^{1 – 3} Astrocytes sense activity within the neuronal network and respond to neural signals by generating ${\mathrm{Ca}}^{2+}$ activities in vivo.^4,5 Simultaneously, astrocyte activation or manipulation modulates neuronal network activity.^2,3 This bidirectional, interconnected communication between astrocytes and neurons is collectively termed the “astrocyte-neuronal network.”^6,7 Accumulating experimental evidence suggests that various behaviors result from coordinated activity between neurons and astrocytes within these networks.^2,8,9 Therefore, deciphering the interaction between astrocytes and neurons within astrocyte-neuronal networks is essential for comprehensively understanding the genesis and dysregulation of behavior.^2,3,10

Objective: The objective of this study was to investigate the role of the high-frequency cochlear dysfunction in the cognitive-ear link.Methods: Seventy-four presbycusis patients (PC group) and seventy-one age-, sex-, and education-level matched normal hearing controls (NH group) were recruited in this study. Participants underwent a battery of cognitive tests estimated by Montreal Cognitive Assessment (MoCA), Stroop Color-Word Interference Test (Stroop), Symbol Digit Modalities Test (SDMT), Auditory Verbal Learning Test (AVLT), and Trail-Making Test (TMT-A and B), as well as auditory tests including distortion product otoacoustic emission (DPOAE), pure tone (PT) thresholds, and speech reception thresholds (SRT). Data were analyzed using the factor analysis, partial correlation analysis, multiple linear regression models, and mediation models.Results: Distortion product otoacoustic emission detection amplitudes and PT thresholds performed worse gradually from low to high frequencies in both the NH and PC groups. High-frequency DPOAE (H-DPOAE) was significantly correlated with cognitive domains in the PC group (AVLT: r = 0.30, p = 0.04; SDMT: r = 0.36, p = 0.01; Stroop: r = –0.32, p = 0.03; TMT-A: r = –0.40, p = 0.005; TMT-B: r = –0.34, p = 0.02). Multiple linear regression models showed that H-DPOAE predicted cognitive impairment effectively for aspects of memory (R² = 0.27, 95% CI, 0.03 to 1.55), attention (R² = 0.32, 95% CI, –6.18 to –0.40), processing speed (R² = 0.37, 95% CI, 0.20 to 1.64), and executive function (TMT-A: R² = 0.34, 95% CI, –5.52 to 1.03; TMT-B: R² = 0.29, 95% CI, –11.30 to –1.12). H-DPOAE directly affected cognition and fully mediated the relationship between pure tone average (PTA)/SRT and cognitive test scores, excluding MoCA.Conclusion: This study has demonstrated that the high-frequency cochlear amplifier dysfunction has a direct predictive effect on the cognitive decline and makes a large contribution to the cognitive-ear link.

Cold stress can affect ventricular repolarization (VR), which may serve as a potential trigger for malignant arrhythmias. Compared to conventional electrocardiogram (ECG) devices, wearable technology is more suitable for out-of-hospital monitoring due to their portability. However, outdoor cold environments can limit their application. We aimed to develop a wearable ECG capable of functioning in a cold environment. Moreover, to explore the impact of cold stress on VR parameters in a real-world cold environment and identify associated cardiovascular risks. A self-developed handheld 12-lead wearable ECG was utilized, featuring a fabric electrode and a novel “sandwich” design to enhance its applicability in cold environments. A total of 152 healthy adults were enrolled and both conventional and wearable ECG were performed on them. The results from testing under an outdoor cold environment indicated that the parameters of the ECG measured by the wearable device were in good agreement with those measured by the conventional machine. Those with higher baseline heart rate (HR) were at increased risk of prolonged QT_c, TpTe_c, and TpTe/QT when compared to those with lower baseline HR after exposure to a cold environment, potentially enabling risk stratification for arrhythmia-prone populations, thereby reducing the incidence of sudden cardiac death.