Signal Artifacts and Techniques for Artifacts and Noise Removal

Today’s world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a “job” or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.

Section: Challenges and Future Limitationsmentioning

confidence: 99%

“…Several implementations have already been made for this purpose, such as those mentioned in Refs. [ 136 , 138 , 139 , 140 ]. Therefore, automation of noise reduction is an area that should be investigated to clean and preprocess the data to improve the accuracy of physical fatigue detection in the workplace.…”

Section: Challenges and Future Limitationsmentioning

confidence: 99%

Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Moshawrab

Adda

Bouzouane

et al. 2022

“…Therefore, to interpret the measured signal properly and obtain diagnostic information, eliminating these interferences is necessary. The main sources of interference with the bioelectric signals (see Table 2 ) are as follows [ 41 , 42 , 43 ]: Power line interference (PLI) is caused by the electromagnetic field distributed through the power supply. It is the most common noise in bioelectrical signals, superposing sinusoidal harmonic components with a frequency of 50 Hz in European countries and 60 Hz in the USA and Japan.…”

Section: Introductionmentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Martínek

Ládrová

Šidiková

et al. 2021

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

“…Although CSP is the most widely employed algorithm, several factors may hinder the extraction of highly separable features in terms of spatial patterns. Namely, while spatial filters are efficient on clean datasets that were obtained in constrained environments, they need a convenient artifact removing from EEG signals because of distorting artifacts and outliers of real-world acquisition contexts [17], the interval of neural responses is generally chosen heuristically. Additionally, features that are extracted by CSP are dense with patterns repeatedly selected [18], small datasets [19], and unsuitability to analyze task-free (unlabeled) EEG data (like resting-state), due to the sources being identified by CSP representing the summed activity from multiple distinct neural electrodes that, together, allow for differentiation by contrasting two or more labeled MI conditions [20].…”

Section: Introductionmentioning

confidence: 99%

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

García-Murillo

Álvarez-Meza

Castellanos-Domínguez

2021

Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.