Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects.
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http://dx.doi.org/10.3390/s22155805 | DOI Listing |
Sleep Med
January 2025
Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University Nanchang, 330006, China; Intelligent Medical Imaging of Jiangxi Key Laboratory, 330006, Nanchang, China; School of Biomedical Engineering, National Graduate College for Engineers, Tsinghua University, 100084, Beijing, China. Electronic address:
Background: Sleep is associated with glymphatic circulation activity; however, there is no direct imaging modality to validate glymphatic circulation disorders in patients with insomnia. Therefore, this study aimed to explore the relationship between insomnia disorder (ID) and the glymphatic system. Dynamic synthetic magnetic resonance imaging (syMRI) was performed.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Engineering Sciences, Lappeenranta-Lahti University of Technology LUT, Lahti, 15110, Finland; Atmospheric Modelling Centre Lahti, Lahti University Campus, Lahti, 15140, Finland; Institute for Atmospheric and Earth System Research (INAR), The University of Helsinki, Helsinki, 00014, Finland.
Modelling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modelling. We consider atmospheric chemistry as a time-dependent Ordinary Differential Equation.
View Article and Find Full Text PDFSci Rep
January 2025
Westchase Software, Houston, TX, 77063, USA.
It is well known that the sedimentary rock record is both incomplete and biased by spatially highly variable rates of sedimentation. Without absolute age constraints of sufficient resolution, the temporal correlation of spatially disjunct records is therefore problematic and uncertain, but these effects have rarely been analysed quantitatively using signal processing methods. Here we use a computational process model to illustrate and analyse how spatial and temporal geochemical records can be biased by the inherent, heterogenous processes of marine sedimentation and preservation.
View Article and Find Full Text PDFTalanta
December 2024
The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou 510317, China. Electronic address:
Tuberculosis (TB) is the second deadliest infectious disease worldwide. Current TB diagnostics utilize sputum samples, which are difficult to obtain, and sample processing is time-consuming and difficult. This study developed an integrated diagnostic platform for the rapid visual detection of Mycobacterium tuberculosis (Mtb) in breath samples at the point-of-care (POC), especially in resource-limited settings.
View Article and Find Full Text PDFGait Posture
December 2024
Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China. Electronic address:
Background: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals.
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