Using Cognitive-Motor Dual-Tasks and Functional Near-Infrared Spectroscopy to Characterize Older Adults with and without Subjective Cognitive Decline.

Background: Subjective cognitive decline (SCD) refers to individuals who report persistent cognitive deficits but perform normally on neuropsychological tests. Performance may be facilitated by increased prefrontal cortex activation, known as neural compensation, and could be used to differentiate between older adults with and without SCD.

Objective: This cross-sectional pilot study measured changes in the hemodynamic response (ΔHbO2) using functional near-infrared spectroscopy (fNIRS) as well as cognitive and motor performance during fine and gross motor dual-tasks in older adults with and without SCD.

Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note.

Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance.

Deep learning in multimedia healthcare applications: a review.

The increase in chronic diseases has affected the countries' health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens' needs.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement.

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement.

Adaptive Spectro-Temporal Filtering for Electrocardiogram Signal Enhancement.

Advances in low-cost portable electrocardiogram (ECG) devices have opened doors for numerous new applications, including fitness tracking, remote health, and peak athletic performance monitoring, to name a few. Many such devices, however, have been shown to be highly contaminated by movement and/or muscle contraction artifacts, which, in turn, can lead to erroneous heart rate and heart rate variability (HRV) analyses. Here, we propose a new denoising method based on adaptive spectro-temporal filtering for ECG enhancement.