Frequency analysis of gait signals for detection of neurodegenerative diseases

Das, Kuntalkanti; Saji, A. J.; Kumar, C. Sathish

doi:10.1109/iccpct.2017.8074273

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…Frequency spectra (obtained by fast Fourier transform) and power spectral density of left stride, right stride, and left swing signals, which were derived from the vGRF signal were effective to differentiate patients with ALS, HD, or PD from HC subjects. 124 Features of power spectral density such as peak amplitude, peak delay, and area under the curve were extracted. 117 Higher-order spectra have the ability to preserve useful information due to deviations from Gaussianity and nonlinearities in the time series such as EEG and EMG.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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“…Frequency analysis of gait signals has also been explored for the detection of neurodegenerative diseases. One study [6] applied artificial neural networks (ANN), support vector machines (SVM), and Naïve Bayes classifiers on gait data from patients with amyotrophic lateral sclerosis, Huntington's disease, PD, and healthy control subjects. The ANN classifier achieved the highest accuracy of 90.6%, followed by SVM (64.00%) and Naïve Bayes (44.44%).…”

Section: A Conventional Machine Learning Methodsmentioning

confidence: 99%

Vision-Based Gait Analysis for Neurodegenerative Disorders Detection

Tan,

Ooi,

Chan

et al. 2024

JIWE

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Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder that affects a significant portion of aging population. Early detection of PD symptoms is crucial to prevent the progression of the disease. Research has revealed that gait attributes can provide valuable insights into PD symptoms. The gait acquisition techniques used in current research can be broadly divided into two categories: vision-based and sensor-based. The markerless vision-based classification model has become a prominent research trend due to its simplicity, low cost and patient comfort. In this study, we propose a novel markerless vision-based approach to obtain gait features from participants' gait videos. A dataset containing gait videos from normal subjects and PD patients were collected, along with a control group of 25 healthy adults. The participants were requested to perform a Timed Up and Go (TUG) test, during which their walking sequences were recorded using two smartphones positioned at different angles, namely side and front. A multi-person pose estimator is used to estimate human skeletal joint points from the collected gait videos. Different gait features associated with PD including stride length, number of steps taken during turn, turning duration, speed and cadence are derived from these key point information to perform PD detection. Experimental results show that the proposed solution achieves an accuracy of 89.39%. The study's findings demonstrate the potential of markerless vision-based gait acquisition techniques for early detection of PD symptoms.

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