Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

Matthews, Jared; Kim, Jihoon; Yeo, Woon‐Hong

doi:10.1002/anse.202200062

Cited by 11 publications

(7 citation statements)

References 144 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, our study involved a smaller participant pool [ 5 , 11 , 12 ]. However, the key distinction of our approach is dry electrodes, which are more common in wearable devices, for ECG measurements at varying times [ 34 , 35 ]. This aspect of our research is particularly relevant for practical applications, as it reflects the varying electrode positions over time.…”

Section: Discussionmentioning

confidence: 99%

Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features

Hwang,

Lee,

Kwon

et al. 2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features

Hwang,

Lee,

Kwon

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Wearable sensors have undergone significant technological advancements, resulting in reduced size and power requirements, improved wearability, and enhanced data quality and variety [43]. These advancements have facilitated the application of wearable sensors to address critical clinical challenges affecting human health.…”

Section: Discussionmentioning

confidence: 99%

AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals

Cossich,

Katz,

Laett

2024

Applied Sciences

View full text Add to dashboard Cite

This study explores the use of accelerometer signals as the predictors of Rate of Torque Development (RTD) using an artificial neural network (ANN) prediction model. Sixteen physically active men participated (29 ± 5 years), performing explosive isometric contractions while acceleration (ACC) signals were measured. The dataset, comprising ACC signals and corresponding RTD values, was split into training and testing (70–30%) sets for ANN training. The trained model predicted the peak RTD values from the ACC signal inputs. The measured and predicted peak RTD values were compared, with no significant differences observed (p = 0.852). A strong linear fit (R² = 0.81), ICC = 0.94 (p < 0.001), and a mean bias of 30.8 Nm/s demonstrated almost perfect agreement between measures. The study demonstrates the feasibility of using accelerometer data to predict peak RTD, offering a portable and cost-effective method compared to traditional equipment. The ANN prediction model provides a reliable means of estimating RTD from ACC signals, potentially enhancing accessibility to RTD assessment in sports and rehabilitation settings. The findings support the use of ANN models for predicting RTD, highlighting the potential of AI in developing performance analysis tools.

show abstract

“…In the field of health sciences, the following geophysical analysis procedures are now in use: Signal Processing: ECG, EEGs, EEGs, and even functional MRI data are treated as medical signals using signal processing techniques utilized in geophysics, including filtering, noise reduction, and Fourier analysis. These methods aid in pattern recognition, abnormality quantification, and information extraction from noisy data [ 115 , 116 ]. Image Reconstruction: Medical imaging modalities like CT and MRI are developed from geophysical data inversion methods, which are used to rebuild subsurface structures.…”

Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

“…Signal Processing: ECG, EEGs, EEGs, and even functional MRI data are treated as medical signals using signal processing techniques utilized in geophysics, including filtering, noise reduction, and Fourier analysis. These methods aid in pattern recognition, abnormality quantification, and information extraction from noisy data [ 115 , 116 ].…”

Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Singh,

Nayak,

Behl

et al. 2024

Diagnostics

View full text Add to dashboard Cite

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth’s subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

show abstract

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

Cited by 11 publications

References 144 publications

Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features

Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features

AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Contact Info

Product

Resources

About