Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Abstract

Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

Promoting child and adolescent health through wearable technology: A systematic review

“… 73 Additionally, reviews exploring the use of wearables in diagnosing cardiac arrhythmias and neurological disorders have been documented. 76 , 77 Although this review did not include studies focused on children and adolescents in these areas, it presents a potential avenue for future research. For children and adolescents with chronic diseases who can move freely, there is a need for more prolonged and continuous monitoring.…”

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness

“…Skewness Skewness (S k ) is a higher-order moment, which measures the asymmetry of a probability distribution, indicating whether the distribution is skewed to the left or right. In the context of EEG signals, skewness reflects the underlying asymmetry or imbalance in the neural activity within different frequency components (Zhao et al, 2023). If the input EEG data is distributed more towards the left of the mean point, it has a positive skewness value.…”

“…Kurtosis ( K ) measures the “peakedness” or “tailedness” of a probability distribution, providing insights into the shape and distribution of the underlying neural activity within different frequency components (Zhao et al, 2023 ). In the context of EEG signals, kurtosis reflects the deviation of the distribution from a Gaussian distribution.…”

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery—A Narrative Review

“…The diagnosis of NDDs is mainly based on clinical examination by physicians. Wearable sensors have been investigated for the diagnosis and measurement of disease progression in NDDs [ 58 , 59 , 60 ]. Moreover, with increasing incidence and a shortage of physician specialists, the treatment of patients with NDDs is becoming increasingly difficult, so the use of telemedicine and wearable devices may provide an alternative perspective to appropriate care [ 61 ].…”